From b8f12ab58160a9de1cbd6c4a332f5182b7bcf361 Mon Sep 17 00:00:00 2001
From: Andrew Huang <ahuang@anaconda.com>
Date: Mon, 29 Apr 2024 10:46:47 -0700
Subject: [PATCH 1/4] Add contents

---
 06-panel-intro.ipynb | 937 +++++++++++++++++++++++++++++++++++++++++++
 images/recap.png     | Bin 0 -> 194983 bytes
 2 files changed, 937 insertions(+)
 create mode 100644 06-panel-intro.ipynb
 create mode 100644 images/recap.png

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Intro to HoloViz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "HoloViz is a suite of high-level Python tools that are designed to work together to make visualizing data a breeze, from conducting exploratory data analysis to deploying complex dashboards.\n",
+    "\n",
+    "The core HoloViz projects are as follow,\n",
+    "\n",
+    "- [Panel](https://panel.holoviz.org): Create interactive dashboards in Jupyter notebooks or standalone apps\n",
+    "- [hvPlot](https://hvplot.holoviz.org): Quickly and interactively explore data with a familiar API\n",
+    "- [HoloViews](https://holoviews.org): Interactive plotting experience\n",
+    "- [GeoViews](http://geoviews.org): Geographic extension of HoloViews\n",
+    "- [Datashader](https://datashader.org): Render big data images in a browser\n",
+    "- [Lumen](https://lumen.holoviz.org/): Construct no-code dashboards from simple YAML specifications\n",
+    "- [Colorcet](https://colorcet.holoviz.org/): Plot with perceptually based colormaps\n",
+    "- [Param](https://param.holoviz.org): Declaratively code in Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What is Panel\n",
+    "\n",
+    "Today, the focus is on Panel.\n",
+    "\n",
+    "Panel packs many pre-built frontend components that are **usable with Python**.\n",
+    "\n",
+    "That means you can convert your static Python scripts into interactive ones--**no Javascript necessary**!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import panel as pn\n",
+    "pn.extension()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic Panel Tutorial\n",
+    "\n",
+    "Let's start out building an interactive app that allows the user to print a custom message.\n",
+    "\n",
+    "Currently it's hard coded to `\"Hello World\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Hello World!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can give the user more control by introducing a `TextInput` widget."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "message_input = pn.widgets.TextInput(value=\"Hello World!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, we can `pn.bind` the widget's `param.value` to the callback, `echo_message`, which simply echos the input value on change.\n",
+    "\n",
+    "Note: it's important to prefix `value` with `param`--without it, there will be no updates!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def echo_message(message):\n",
+    "    return f\"<i>{message}</i>\"\n",
+    "\n",
+    "message_ref = pn.bind(echo_message, message=message_input.param.value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, create a simple layout to see the results.\n",
+    "\n",
+    "Try typing unique in the widget to see the message update!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pn.Column(message_input, message_ref)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To recap, we:\n",
+    "\n",
+    "1. instantiated a widget (`TextInput`).\n",
+    "2. defined a function `echo_message`\n",
+    "3. bounded the function to the widget's *param* value\n",
+    "4. laid out the the widget and the bound reference\n",
+    "\n",
+    "![recap](images/.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's all the code cells collected into one!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import panel as pn\n",
+    "pn.extension()\n",
+    "\n",
+    "message_input = pn.widgets.TextInput(value=\"Hello World!\")\n",
+    "\n",
+    "def echo_message(message):\n",
+    "    return f\"<i>{message}</i>\"\n",
+    "\n",
+    "message_ref = pn.bind(echo_message, message=message_input.param.value)\n",
+    "\n",
+    "pn.Column(message_input, message_ref)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Doing this repeatedly is key to creating more complex apps with Panel, so let's do a quick exercise.\n",
+    "\n",
+    "Your goal is to create a widget that will toggle the message to upper case if activated by filling out the ellipses (`...`)!\n",
+    "\n",
+    "Hint: check out the [Component gallery](https://panel.holoviz.org/reference/index.html) to see what widgets are available to accomplish this goal (one of them starts with a T, but there are multiple solutions!)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import panel as pn\n",
+    "pn.extension()\n",
+    "\n",
+    "message_input = pn.widgets.TextInput(value=\"Hello World!\")\n",
+    "toggle_upper = ...\n",
+    "\n",
+    "def echo_message(message, toggle_upper):\n",
+    "    ...\n",
+    "    return f\"<i>{message}</i>\"\n",
+    "\n",
+    "message_ref = pn.bind(echo_message, message=message_input.param.value, toggle_upper=...)\n",
+    "\n",
+    "pn.Column(message_input, message_ref)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Congrats on building an interactive Panel app! 🎉"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic Panel ChatInterface"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, introducing `pn.chat.ChatInterface`, which is a component that packages all the steps you just learned to provide convenient features for developing a Chat UI with LLMs!\n",
+    "\n",
+    "Try typing a message and pressing enter to send!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e1b47eef7ef45388b045f69d4d507b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "BokehModel(combine_events=True, render_bundle={'docs_json': {'8a999aa4-7efd-4bfb-925e-69148192146e': {'version…"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat = pn.chat.ChatInterface()\n",
+    "chat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You might have noticed that it echoes the message you entered, but it doesn't reply... not fun (yet).\n",
+    "\n",
+    "To make it reply, all we have to do is set a `callback`, like `pn.bind`, but with a caveat: it needs these three arguments: `contents`, `user`, and `instance`.\n",
+    "\n",
+    "Now when you try sending a message in the chat interface, it will be echoed back in italics!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def echo_message(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+    "    return f\"<i>{contents}</i>\"\n",
+    "\n",
+    "chat.callback = echo_message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You might have seen services, like OpenAI and Mistral, stream tokens as they arrive.\n",
+    "\n",
+    "We can simulate streaming tokens by looping through the contents of the user's input, concatenating the characters to the final message, and `yield`ing it.\n",
+    "\n",
+    "Since there's no serious computation, it'll run too fast for us to perceive streaming--thus `time.sleep`.\n",
+    "\n",
+    "Here's the latest code collected into one (and also `callback` within instantation)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } 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EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n",
+      "application/vnd.holoviews_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "  ),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.holoviews_exec.v0+json": "",
+      "text/html": [
+       "<div id='52657123-eb28-4912-a84b-6adbb91b24d2'>\n",
+       "  <div id=\"feb8fd1d-6507-4b1b-bc6e-0fe3eb8f4485\" data-root-id=\"52657123-eb28-4912-a84b-6adbb91b24d2\" style=\"display: contents;\"></div>\n",
+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"6313024c-ef2a-4cb6-9a35-2f8ef704a866\":{\"version\":\"3.4.0\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"52657123-eb28-4912-a84b-6adbb91b24d2\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"fe0e2aa4-46fa-445f-9e6a-7ba9b2287552\",\"attributes\":{\"plot_id\":\"52657123-eb28-4912-a84b-6adbb91b24d2\",\"comm_id\":\"eafb2eb6a17b4599ae4016016b77298c\",\"client_comm_id\":\"c12adc6e0e4849f28d4c2a3f0b0f859d\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
+       "  var render_items = [{\"docid\":\"6313024c-ef2a-4cb6-9a35-2f8ef704a866\",\"roots\":{\"52657123-eb28-4912-a84b-6adbb91b24d2\":\"feb8fd1d-6507-4b1b-bc6e-0fe3eb8f4485\"},\"root_ids\":[\"52657123-eb28-4912-a84b-6adbb91b24d2\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ]
+     },
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "52657123-eb28-4912-a84b-6adbb91b24d2"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd1ebc0fa8ff41789afc3fbdedeaa1a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "BokehModel(combine_events=True, render_bundle={'docs_json': {'c6988535-b97b-4698-9edd-81a411a34f2e': {'version…"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import time\n",
+    "import panel as pn\n",
+    "pn.extension()\n",
+    "\n",
+    "def stream_echo_message(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+    "    message = \"\"\n",
+    "    for char in contents:\n",
+    "        time.sleep(0.1)  # to simulate a serious computation\n",
+    "        message += char\n",
+    "        yield f\"<i>{message}</i>\"\n",
+    "\n",
+    "chat = pn.chat.ChatInterface(callback=stream_echo_message)\n",
+    "chat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Awesome! Now let's make it much more interesting by connecting an LLM, like the quantized Mistral Instruct 7B model through llama-cpp-python (so no API key necessary)!\n",
+    "\n",
+    "Here, we:\n",
+    "1. download the quantized model (if it doesn't exist already)\n",
+    "2. instantiate the model with `Llama`\n",
+    "3. serialize all messages into `transformers` format\n",
+    "4. calls the chat completion Openai-like API on the messages\n",
+    "5. stream the chunks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.4.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n      root._bokeh_is_loading = css_urls.length + 0;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; 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+      "application/vnd.holoviews_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "  ),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.holoviews_exec.v0+json": "",
+      "text/html": [
+       "<div id='1dbadddd-9c72-4048-9b25-f0243041326f'>\n",
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+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"364c1505-5202-485f-9333-cc60ee81f81f\":{\"version\":\"3.4.0\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"1dbadddd-9c72-4048-9b25-f0243041326f\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"ff293dab-9d60-4ab9-8733-74b3acb89df5\",\"attributes\":{\"plot_id\":\"1dbadddd-9c72-4048-9b25-f0243041326f\",\"comm_id\":\"02b1985c9aa64affa81c38b327d95ee9\",\"client_comm_id\":\"d57d40d822814b9ea036080fdb81cb9c\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
+       "  var render_items = [{\"docid\":\"364c1505-5202-485f-9333-cc60ee81f81f\",\"roots\":{\"1dbadddd-9c72-4048-9b25-f0243041326f\":\"c2b527e4-00a9-41b5-82cd-72a4fd9ae438\"},\"root_ids\":[\"1dbadddd-9c72-4048-9b25-f0243041326f\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ]
+     },
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "1dbadddd-9c72-4048-9b25-f0243041326f"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1a5aa7dfd4f4b1aa8b363a40bbc0f6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "BokehModel(combine_events=True, render_bundle={'docs_json': {'5c3303d1-50ef-4d08-80ca-f5f3daab1588': {'version…"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import llama_cpp\n",
+    "import panel as pn\n",
+    "from pydantic import BaseModel\n",
+    "from huggingface_hub import hf_hub_download\n",
+    "pn.extension()\n",
+    "\n",
+    "model_path = hf_hub_download(\n",
+    "    \"TheBloke/Mistral-7B-Instruct-v0.2-GGUF\",\n",
+    "    \"mistral-7b-instruct-v0.2.Q5_K_M.gguf\",\n",
+    ")  # 1.\n",
+    "\n",
+    "llama = llama_cpp.Llama(\n",
+    "    model_path=model_path,\n",
+    "    n_gpu_layers=-1,\n",
+    "    chat_format=\"mistral-instruct\",\n",
+    "    n_ctx=2048,\n",
+    "    logits_all=True,\n",
+    "    verbose=False,\n",
+    ") # 2.\n",
+    "\n",
+    "def stream_response(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+    "    messages = instance.serialize()  # 3.\n",
+    "    response = llama.create_chat_completion_openai_v1(messages=messages, stream=True)  # 4.\n",
+    "\n",
+    "    message = \"\"\n",
+    "    for chunk in response:\n",
+    "        part = chunk.choices[0].delta.content or \"\"\n",
+    "        message += part\n",
+    "        yield message  # 5.\n",
+    "\n",
+    "chat = pn.chat.ChatInterface(callback=stream_response)\n",
+    "chat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can even give the model a personality by setting a system message!\n",
+    "\n",
+    "Note, Mistral Instruct does NOT support the `system` role."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "application/vnd.holoviews_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        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+      "application/vnd.holoviews_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "  ),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
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+      "text/html": [
+       "<div id='a3664db1-6e55-4f4a-b2c1-5ff889950e27'>\n",
+       "  <div id=\"a6d914e6-6aa1-4db5-8ae4-2c7e14d4f802\" data-root-id=\"a3664db1-6e55-4f4a-b2c1-5ff889950e27\" style=\"display: contents;\"></div>\n",
+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"f641276f-867b-4163-b48e-14a786481ef7\":{\"version\":\"3.4.0\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"a3664db1-6e55-4f4a-b2c1-5ff889950e27\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"398c253b-4ab3-4cc7-bb9e-c26a1355f4ab\",\"attributes\":{\"plot_id\":\"a3664db1-6e55-4f4a-b2c1-5ff889950e27\",\"comm_id\":\"91f5cd425c1b42a0bc4cdb935f18e673\",\"client_comm_id\":\"a32cd21fcf3146d3b62ec95572fd135c\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
+       "  var render_items = [{\"docid\":\"f641276f-867b-4163-b48e-14a786481ef7\",\"roots\":{\"a3664db1-6e55-4f4a-b2c1-5ff889950e27\":\"a6d914e6-6aa1-4db5-8ae4-2c7e14d4f802\"},\"root_ids\":[\"a3664db1-6e55-4f4a-b2c1-5ff889950e27\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ]
+     },
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "a3664db1-6e55-4f4a-b2c1-5ff889950e27"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4e2c5ccdc0cb410e88f0f1e8f9e8180d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "BokehModel(combine_events=True, render_bundle={'docs_json': {'f2849008-b11d-47e6-a0b5-f45b0673da05': {'version…"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import llama_cpp\n",
+    "import panel as pn\n",
+    "from pydantic import BaseModel\n",
+    "from huggingface_hub import hf_hub_download\n",
+    "\n",
+    "pn.extension()\n",
+    "\n",
+    "system_message = \"You are an excessively passionate Pythonista.\"\n",
+    "\n",
+    "model_path = hf_hub_download(\n",
+    "    \"TheBloke/Mistral-7B-Instruct-v0.2-GGUF\",\n",
+    "    \"mistral-7b-instruct-v0.2.Q5_K_M.gguf\",\n",
+    ")  # 1.\n",
+    "\n",
+    "llama = llama_cpp.Llama(\n",
+    "    model_path=model_path,\n",
+    "    n_gpu_layers=-1,\n",
+    "    chat_format=\"mistral-instruct\",\n",
+    "    n_ctx=2048,\n",
+    "    logits_all=True,\n",
+    "    verbose=False,\n",
+    ")  # 2.\n",
+    "\n",
+    "\n",
+    "def stream_response(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+    "    messages = [\n",
+    "        {\"role\": \"user\", \"content\": system_message}\n",
+    "    ] + instance.serialize()  # 3.\n",
+    "    response = llama.create_chat_completion_openai_v1(\n",
+    "        messages=messages, stream=True\n",
+    "    )  # 4.\n",
+    "\n",
+    "    message = \"\"\n",
+    "    for chunk in response:\n",
+    "        part = chunk.choices[0].delta.content or \"\"\n",
+    "        message += part\n",
+    "        yield message  # 5.\n",
+    "\n",
+    "\n",
+    "chat = pn.chat.ChatInterface(callback=stream_response)\n",
+    "chat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can make this Chat UI improved by using templates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Launching server at http://localhost:57205\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<panel.io.server.Server at 0x2abd5c210>"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "template = pn.template.FastListTemplate(main=[chat], title=\"Chatbot\", accent=\"#A01346\")\n",
+    "template.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Your turn! Try aggregating all you've learned to customize the personality of the chatbot on the go!\n",
+    "\n",
+    "Again, replace the ellipses with the appropriate code snippets!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import llama_cpp\n",
+    "import panel as pn\n",
+    "from pydantic import BaseModel\n",
+    "from huggingface_hub import hf_hub_download\n",
+    "\n",
+    "pn.extension()\n",
+    "\n",
+    "system_message = \"You are an excessively passionate Pythonista.\"\n",
+    "\n",
+    "model_path = hf_hub_download(\n",
+    "    \"TheBloke/Mistral-7B-Instruct-v0.2-GGUF\",\n",
+    "    \"mistral-7b-instruct-v0.2.Q5_K_M.gguf\",\n",
+    ")  # 1.\n",
+    "\n",
+    "llama = llama_cpp.Llama(\n",
+    "    model_path=model_path,\n",
+    "    n_gpu_layers=-1,\n",
+    "    chat_format=\"mistral-instruct\",\n",
+    "    n_ctx=2048,\n",
+    "    logits_all=True,\n",
+    "    verbose=False,\n",
+    ")  # 2.\n",
+    "\n",
+    "\n",
+    "def stream_response(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+    "    messages = [\n",
+    "        {\"role\": \"user\", \"content\": ...}\n",
+    "    ] + instance.serialize()  # 3.\n",
+    "    response = llama.create_chat_completion_openai_v1(\n",
+    "        messages=messages, stream=True\n",
+    "    )  # 4.\n",
+    "\n",
+    "    message = \"\"\n",
+    "    for chunk in response:\n",
+    "        part = chunk.choices[0].delta.content or \"\"\n",
+    "        message += part\n",
+    "        yield message  # 5.\n",
+    "\n",
+    "\n",
+    "system_input = ...\n",
+    "chat = pn.chat.ChatInterface(callback=stream_response)\n",
+    "template = pn.template.FastListTemplate(\n",
+    "    main=[chat], sidebar=[...], title=\"Chatbot\", accent=\"#A01346\"\n",
+    ")\n",
+    "template.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "holonote",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/images/recap.png b/images/recap.png
new file mode 100644
index 0000000000000000000000000000000000000000..01eb77ae3afaccfb2d9826428ec711e07540b0cc
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literal 0
HcmV?d00001


From 3f0739cdc4510a70742d5e877ac46404d13bb8db Mon Sep 17 00:00:00 2001
From: Andrew Huang <ahuang@anaconda.com>
Date: Mon, 29 Apr 2024 10:47:09 -0700
Subject: [PATCH 2/4] strip

---
 06-panel-intro.ipynb | 468 ++-----------------------------------------
 1 file changed, 16 insertions(+), 452 deletions(-)

diff --git a/06-panel-intro.ipynb b/06-panel-intro.ipynb
index 7498efe..130f919 100644
--- a/06-panel-intro.ipynb
+++ b/06-panel-intro.ipynb
@@ -220,25 +220,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6e1b47eef7ef45388b045f69d4d507b0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "BokehModel(combine_events=True, render_bundle={'docs_json': {'8a999aa4-7efd-4bfb-925e-69148192146e': {'version…"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "chat = pn.chat.ChatInterface()\n",
     "chat"
@@ -257,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -282,143 +266,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.4.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n      root._bokeh_is_loading = css_urls.length + 0;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.0.min.js\", \"https://cdn.holoviz.org/panel/1.4.0/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; 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-       "<style>*[data-root-id],\n",
-       "*[data-root-id] > * {\n",
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-       "}\n",
-       "\n",
-       "/* Override VSCode background color */\n",
-       ".cell-output-ipywidget-background:has(\n",
-       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
-       "  ),\n",
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-       "  background-color: transparent !important;\n",
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-       "<div id='52657123-eb28-4912-a84b-6adbb91b24d2'>\n",
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-       "</div>\n",
-       "<script type=\"application/javascript\">(function(root) {\n",
-       "  var docs_json = {\"6313024c-ef2a-4cb6-9a35-2f8ef704a866\":{\"version\":\"3.4.0\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"52657123-eb28-4912-a84b-6adbb91b24d2\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"fe0e2aa4-46fa-445f-9e6a-7ba9b2287552\",\"attributes\":{\"plot_id\":\"52657123-eb28-4912-a84b-6adbb91b24d2\",\"comm_id\":\"eafb2eb6a17b4599ae4016016b77298c\",\"client_comm_id\":\"c12adc6e0e4849f28d4c2a3f0b0f859d\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
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-       "\tconst id_el = document.getElementById(root_id)\n",
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-       "\t  const root_el = id_el.children[0]\n",
-       "\t  root_el.id = root_el.id + '-rendered'\n",
-       "\t}\n",
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-       "  function get_bokeh(root) {\n",
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-       "\treturn null\n",
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-       "      return root.Bokeh\n",
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-       "    var attempts = 0;\n",
-       "    var timer = setInterval(function(root) {\n",
-       "      if (is_loaded(root)) {\n",
-       "        clearInterval(timer);\n",
-       "        embed_document(root);\n",
-       "      } else if (document.readyState == \"complete\") {\n",
-       "        attempts++;\n",
-       "        if (attempts > 200) {\n",
-       "          clearInterval(timer);\n",
-       "\t  var Bokeh = get_bokeh(root)\n",
-       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
-       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
-       "\t  } else {\n",
-       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
-       "\t    embed_document(root)\n",
-       "\t  }\n",
-       "        }\n",
-       "      }\n",
-       "    }, 25, root)\n",
-       "  }\n",
-       "})(window);</script>"
-      ]
-     },
-     "metadata": {
-      "application/vnd.holoviews_exec.v0+json": {
-       "id": "52657123-eb28-4912-a84b-6adbb91b24d2"
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fd1ebc0fa8ff41789afc3fbdedeaa1a3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "BokehModel(combine_events=True, render_bundle={'docs_json': {'c6988535-b97b-4698-9edd-81a411a34f2e': {'version…"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import time\n",
     "import panel as pn\n",
@@ -451,143 +301,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.4.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n      root._bokeh_is_loading = css_urls.length + 0;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.0.min.js\", \"https://cdn.holoviz.org/panel/1.4.0/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n          inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t  if (!reloading) {\n\t    throw e;\n\t  }\n\t}\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
-      "application/vnd.holoviews_load.v0+json": ""
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        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((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = 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the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n",
-      "application/vnd.holoviews_load.v0+json": ""
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-       "<style>*[data-root-id],\n",
-       "*[data-root-id] > * {\n",
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-       "}\n",
-       "\n",
-       "/* Override VSCode background color */\n",
-       ".cell-output-ipywidget-background:has(\n",
-       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
-       "  ),\n",
-       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
-       "  background-color: transparent !important;\n",
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-      "text/html": [
-       "<div id='1dbadddd-9c72-4048-9b25-f0243041326f'>\n",
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-       "</div>\n",
-       "<script type=\"application/javascript\">(function(root) {\n",
-       "  var docs_json = {\"364c1505-5202-485f-9333-cc60ee81f81f\":{\"version\":\"3.4.0\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"1dbadddd-9c72-4048-9b25-f0243041326f\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"ff293dab-9d60-4ab9-8733-74b3acb89df5\",\"attributes\":{\"plot_id\":\"1dbadddd-9c72-4048-9b25-f0243041326f\",\"comm_id\":\"02b1985c9aa64affa81c38b327d95ee9\",\"client_comm_id\":\"d57d40d822814b9ea036080fdb81cb9c\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
-       "  var render_items = [{\"docid\":\"364c1505-5202-485f-9333-cc60ee81f81f\",\"roots\":{\"1dbadddd-9c72-4048-9b25-f0243041326f\":\"c2b527e4-00a9-41b5-82cd-72a4fd9ae438\"},\"root_ids\":[\"1dbadddd-9c72-4048-9b25-f0243041326f\"]}];\n",
-       "  var docs = Object.values(docs_json)\n",
-       "  if (!docs) {\n",
-       "    return\n",
-       "  }\n",
-       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
-       "  function embed_document(root) {\n",
-       "    var Bokeh = get_bokeh(root)\n",
-       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
-       "    for (const render_item of render_items) {\n",
-       "      for (const root_id of render_item.root_ids) {\n",
-       "\tconst id_el = document.getElementById(root_id)\n",
-       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
-       "\t  const root_el = id_el.children[0]\n",
-       "\t  root_el.id = root_el.id + '-rendered'\n",
-       "\t}\n",
-       "      }\n",
-       "    }\n",
-       "  }\n",
-       "  function get_bokeh(root) {\n",
-       "    if (root.Bokeh === undefined) {\n",
-       "      return null\n",
-       "    } else if (root.Bokeh.version !== py_version) {\n",
-       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
-       "\treturn null\n",
-       "      }\n",
-       "      return root.Bokeh.versions.get(py_version);\n",
-       "    } else if (root.Bokeh.version === py_version) {\n",
-       "      return root.Bokeh\n",
-       "    }\n",
-       "    return null\n",
-       "  }\n",
-       "  function is_loaded(root) {\n",
-       "    var Bokeh = get_bokeh(root)\n",
-       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
-       "  }\n",
-       "  if (is_loaded(root)) {\n",
-       "    embed_document(root);\n",
-       "  } else {\n",
-       "    var attempts = 0;\n",
-       "    var timer = setInterval(function(root) {\n",
-       "      if (is_loaded(root)) {\n",
-       "        clearInterval(timer);\n",
-       "        embed_document(root);\n",
-       "      } else if (document.readyState == \"complete\") {\n",
-       "        attempts++;\n",
-       "        if (attempts > 200) {\n",
-       "          clearInterval(timer);\n",
-       "\t  var Bokeh = get_bokeh(root)\n",
-       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
-       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
-       "\t  } else {\n",
-       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
-       "\t    embed_document(root)\n",
-       "\t  }\n",
-       "        }\n",
-       "      }\n",
-       "    }, 25, root)\n",
-       "  }\n",
-       "})(window);</script>"
-      ]
-     },
-     "metadata": {
-      "application/vnd.holoviews_exec.v0+json": {
-       "id": "1dbadddd-9c72-4048-9b25-f0243041326f"
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b1a5aa7dfd4f4b1aa8b363a40bbc0f6f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "BokehModel(combine_events=True, render_bundle={'docs_json': {'5c3303d1-50ef-4d08-80ca-f5f3daab1588': {'version…"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import llama_cpp\n",
     "import panel as pn\n",
@@ -634,143 +350,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.0.min.js\", \"https://cdn.holoviz.org/panel/1.4.0/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n          inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t  if (!reloading) {\n\t    throw e;\n\t  }\n\t}\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
-      "application/vnd.holoviews_load.v0+json": ""
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || 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the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n",
-      "application/vnd.holoviews_load.v0+json": ""
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>*[data-root-id],\n",
-       "*[data-root-id] > * {\n",
-       "  box-sizing: border-box;\n",
-       "  font-family: var(--jp-ui-font-family);\n",
-       "  font-size: var(--jp-ui-font-size1);\n",
-       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
-       "}\n",
-       "\n",
-       "/* Override VSCode background color */\n",
-       ".cell-output-ipywidget-background:has(\n",
-       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
-       "  ),\n",
-       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "</style>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.holoviews_exec.v0+json": "",
-      "text/html": [
-       "<div id='a3664db1-6e55-4f4a-b2c1-5ff889950e27'>\n",
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-       "</div>\n",
-       "<script type=\"application/javascript\">(function(root) {\n",
-       "  var docs_json = {\"f641276f-867b-4163-b48e-14a786481ef7\":{\"version\":\"3.4.0\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"a3664db1-6e55-4f4a-b2c1-5ff889950e27\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"398c253b-4ab3-4cc7-bb9e-c26a1355f4ab\",\"attributes\":{\"plot_id\":\"a3664db1-6e55-4f4a-b2c1-5ff889950e27\",\"comm_id\":\"91f5cd425c1b42a0bc4cdb935f18e673\",\"client_comm_id\":\"a32cd21fcf3146d3b62ec95572fd135c\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
-       "  var render_items = [{\"docid\":\"f641276f-867b-4163-b48e-14a786481ef7\",\"roots\":{\"a3664db1-6e55-4f4a-b2c1-5ff889950e27\":\"a6d914e6-6aa1-4db5-8ae4-2c7e14d4f802\"},\"root_ids\":[\"a3664db1-6e55-4f4a-b2c1-5ff889950e27\"]}];\n",
-       "  var docs = Object.values(docs_json)\n",
-       "  if (!docs) {\n",
-       "    return\n",
-       "  }\n",
-       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
-       "  function embed_document(root) {\n",
-       "    var Bokeh = get_bokeh(root)\n",
-       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
-       "    for (const render_item of render_items) {\n",
-       "      for (const root_id of render_item.root_ids) {\n",
-       "\tconst id_el = document.getElementById(root_id)\n",
-       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
-       "\t  const root_el = id_el.children[0]\n",
-       "\t  root_el.id = root_el.id + '-rendered'\n",
-       "\t}\n",
-       "      }\n",
-       "    }\n",
-       "  }\n",
-       "  function get_bokeh(root) {\n",
-       "    if (root.Bokeh === undefined) {\n",
-       "      return null\n",
-       "    } else if (root.Bokeh.version !== py_version) {\n",
-       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
-       "\treturn null\n",
-       "      }\n",
-       "      return root.Bokeh.versions.get(py_version);\n",
-       "    } else if (root.Bokeh.version === py_version) {\n",
-       "      return root.Bokeh\n",
-       "    }\n",
-       "    return null\n",
-       "  }\n",
-       "  function is_loaded(root) {\n",
-       "    var Bokeh = get_bokeh(root)\n",
-       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
-       "  }\n",
-       "  if (is_loaded(root)) {\n",
-       "    embed_document(root);\n",
-       "  } else {\n",
-       "    var attempts = 0;\n",
-       "    var timer = setInterval(function(root) {\n",
-       "      if (is_loaded(root)) {\n",
-       "        clearInterval(timer);\n",
-       "        embed_document(root);\n",
-       "      } else if (document.readyState == \"complete\") {\n",
-       "        attempts++;\n",
-       "        if (attempts > 200) {\n",
-       "          clearInterval(timer);\n",
-       "\t  var Bokeh = get_bokeh(root)\n",
-       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
-       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
-       "\t  } else {\n",
-       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
-       "\t    embed_document(root)\n",
-       "\t  }\n",
-       "        }\n",
-       "      }\n",
-       "    }, 25, root)\n",
-       "  }\n",
-       "})(window);</script>"
-      ]
-     },
-     "metadata": {
-      "application/vnd.holoviews_exec.v0+json": {
-       "id": "a3664db1-6e55-4f4a-b2c1-5ff889950e27"
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4e2c5ccdc0cb410e88f0f1e8f9e8180d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "BokehModel(combine_events=True, render_bundle={'docs_json': {'f2849008-b11d-47e6-a0b5-f45b0673da05': {'version…"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import llama_cpp\n",
     "import panel as pn\n",
@@ -824,27 +406,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Launching server at http://localhost:57205\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<panel.io.server.Server at 0x2abd5c210>"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "template = pn.template.FastListTemplate(main=[chat], title=\"Chatbot\", accent=\"#A01346\")\n",
     "template.show()"

From 49439c4e44713a57851b32eff6aeb3c09b378f76 Mon Sep 17 00:00:00 2001
From: Andrew Huang <ahuang@anaconda.com>
Date: Fri, 10 May 2024 00:25:10 -0700
Subject: [PATCH 3/4] add env and exllama

---
 06-panel-intro.ipynb | 374 ++++++++++++++++++++++++++++++++++++++-----
 environment.yml      |   7 +
 2 files changed, 342 insertions(+), 39 deletions(-)

diff --git a/06-panel-intro.ipynb b/06-panel-intro.ipynb
index 130f919..b1261b8 100644
--- a/06-panel-intro.ipynb
+++ b/06-panel-intro.ipynb
@@ -13,7 +13,7 @@
    "source": [
     "HoloViz is a suite of high-level Python tools that are designed to work together to make visualizing data a breeze, from conducting exploratory data analysis to deploying complex dashboards.\n",
     "\n",
-    "The core HoloViz projects are as follow,\n",
+    "The core HoloViz projects are as follows:\n",
     "\n",
     "- [Panel](https://panel.holoviz.org): Create interactive dashboards in Jupyter notebooks or standalone apps\n",
     "- [hvPlot](https://hvplot.holoviz.org): Quickly and interactively explore data with a familiar API\n",
@@ -289,14 +289,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Awesome! Now let's make it much more interesting by connecting an LLM, like the quantized Mistral Instruct 7B model through llama-cpp-python (so no API key necessary)!\n",
+    "Awesome! Now let's make it much more interesting by connecting an LLM, like the quantized Mistral Instruct 7B model through ExLlama (so no API key necessary)!\n",
     "\n",
     "Here, we:\n",
-    "1. download the quantized model (if it doesn't exist already)\n",
-    "2. instantiate the model with `Llama`\n",
-    "3. serialize all messages into `transformers` format\n",
-    "4. calls the chat completion Openai-like API on the messages\n",
-    "5. stream the chunks"
+    "1. download the quantized model (if it doesn't exist already) in exl2 format\n",
+    "2. instantiate the model; first checking the cache\n",
+    "3. calls the chat completion through the streaming generator\n",
+    "4. stream the chunks"
    ]
   },
   {
@@ -304,10 +303,295 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "import panel as pn\n",
+    "from huggingface_hub import snapshot_download\n",
+    "from exllamav2 import(\n",
+    "    ExLlamaV2,\n",
+    "    ExLlamaV2Config,\n",
+    "    ExLlamaV2Cache,\n",
+    "    ExLlamaV2Tokenizer,\n",
+    ")\n",
+    "\n",
+    "from exllamav2.generator import (\n",
+    "    ExLlamaV2BaseGenerator,\n",
+    "    ExLlamaV2Sampler\n",
+    ")\n",
+    "from exllamav2.generator import ExLlamaV2Sampler, ExLlamaV2StreamingGenerator\n",
+    "\n",
+    "model_directory = snapshot_download(\n",
+    "    repo_id=\"turboderp/Mistral-7B-v0.2-exl2\", revision=\"2.5bpw\"\n",
+    ")  # 1.\n",
+    "\n",
+    "# 2.\n",
+    "if model_directory in pn.state.cache:\n",
+    "    generator = pn.state.cache[model_directory]\n",
+    "else:\n",
+    "    config = ExLlamaV2Config()\n",
+    "    config.model_dir = model_directory\n",
+    "    config.prepare()\n",
+    "    model = ExLlamaV2(config)\n",
+    "    cache = ExLlamaV2Cache(model, lazy=True)\n",
+    "    model.load_autosplit(cache)\n",
+    "    tokenizer = ExLlamaV2Tokenizer(config)\n",
+    "    generator = ExLlamaV2BaseGenerator(model, cache, tokenizer)\n",
+    "    settings = ExLlamaV2Sampler.Settings()\n",
+    "    settings.temperature = 0.85\n",
+    "    settings.top_k = 50\n",
+    "    settings.top_p = 0.8\n",
+    "    settings.token_repetition_penalty = 1.01\n",
+    "    settings.disallow_tokens(tokenizer, [tokenizer.eos_token_id])\n",
+    "    generator = ExLlamaV2StreamingGenerator(model, cache, tokenizer)\n",
+    "    pn.state.cache[model_directory] = generator\n",
+    "\n",
+    "\n",
+    "def stream_response(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+    "    input_ids = tokenizer.encode(contents, add_bos=False)\n",
+    "    generator.begin_stream_ex(input_ids, settings)\n",
+    "\n",
+    "    message = \"\"\n",
+    "    for _ in range(256):\n",
+    "        result = generator.stream_ex()\n",
+    "        if result[\"eos\"]:\n",
+    "            break\n",
+    "        message += result[\"chunk\"]  # 4.\n",
+    "        yield message\n",
+    "\n",
+    "\n",
+    "chat = pn.chat.ChatInterface(callback=stream_response)\n",
+    "chat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For posterity, we can use `llama-cpp-python` for quantized models too!\n",
+    "\n",
+    "`llama-cpp` can run on both CPU and GPU, and has an API that mimics OpenAI's API. Personally, I use it because I don't have any spare GPUs lying around and it runs extremely well on my local Mac M2 Pro! It also handles chat template formats internally so it's just a matter of specifying a the proper `chat_format` key.\n",
+    "\n",
+    "Here, we:\n",
+    "1. download the quantized model (if it doesn't exist already) in GGUF format\n",
+    "2. instantiate the model; first checking the cache\n",
+    "3. serialize all messages into `transformers` format (new)\n",
+    "4. calls the chat completion Openai-like API on the messages\n",
+    "5. stream the chunks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "application/vnd.holoviews_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>*[data-root-id],\n",
+       "*[data-root-id] > * {\n",
+       "  box-sizing: border-box;\n",
+       "  font-family: var(--jp-ui-font-family);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+       "}\n",
+       "\n",
+       "/* Override VSCode background color */\n",
+       ".cell-output-ipywidget-background:has(\n",
+       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+       "  ),\n",
+       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "</style>"
+      ]
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+      "text/html": [
+       "<div id='148e04ca-3d33-4860-bac7-1cd1af00a92d'>\n",
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+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
+       "  var docs_json = {\"e4303f0e-ca6f-4797-a913-958e76720ae2\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"148e04ca-3d33-4860-bac7-1cd1af00a92d\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"6ef2dcbf-0b94-443d-a979-438cb8f32ddc\",\"attributes\":{\"plot_id\":\"148e04ca-3d33-4860-bac7-1cd1af00a92d\",\"comm_id\":\"d0a4b34abe074be39f6ecff1ab685681\",\"client_comm_id\":\"a29100c594434e738eec0bb649adf130\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
+       "  var render_items = [{\"docid\":\"e4303f0e-ca6f-4797-a913-958e76720ae2\",\"roots\":{\"148e04ca-3d33-4860-bac7-1cd1af00a92d\":\"d948f30a-8b95-4953-bf2a-21801204e881\"},\"root_ids\":[\"148e04ca-3d33-4860-bac7-1cd1af00a92d\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ]
+     },
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "148e04ca-3d33-4860-bac7-1cd1af00a92d"
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {},
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.holoviews_exec.v0+json": "",
+      "text/html": [
+       "<div id='3f218a26-eb24-472c-b81d-c30c0cd11b12'>\n",
+       "  <div id=\"c7f979d8-95b0-42a4-92dc-2345e6812842\" data-root-id=\"3f218a26-eb24-472c-b81d-c30c0cd11b12\" style=\"display: contents;\"></div>\n",
+       "</div>\n",
+       "<script type=\"application/javascript\">(function(root) {\n",
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400px));\\n}\",{\"id\":\"3632fbba-1d7a-4a7f-8ac3-0e47e0ec988d\"},{\"id\":\"963c55f7-a461-433d-92da-d20277df4454\"},{\"id\":\"461946be-60c5-4f98-bb18-4c592b1ec64f\"},{\"id\":\"f577ac28-0fcf-46b9-a115-d74a4d4b4b03\"}],\"max_width\":90,\"max_height\":50,\"margin\":[0,5,0,0],\"sizing_mode\":\"stretch_width\",\"align\":\"center\",\"label\":\"Clear\",\"icon\":{\"type\":\"object\",\"name\":\"TablerIcon\",\"id\":\"1f7ae95a-3f9e-4e65-b81f-5b843c6f436a\",\"attributes\":{\"icon_name\":\"trash\"}}}}]}}]}}],\"active_header_background\":\"\",\"button_css_classes\":[\"card-button\"],\"collapsed\":false,\"collapsible\":false,\"header_background\":\"\",\"header_color\":\"\",\"header_css_classes\":[\"chat-feed-header\"],\"hide_header\":true}},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"b34ae742-450d-4164-bb09-c734eba27d28\",\"attributes\":{\"plot_id\":\"3f218a26-eb24-472c-b81d-c30c0cd11b12\",\"comm_id\":\"57ea2cf7e77c4605af83de15f01ce1ba\",\"client_comm_id\":\"c50a9be500e942ba94816819698c7a6f\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
+       "  var render_items = [{\"docid\":\"0e4775a0-c3ac-4fa6-8d04-71f9126436be\",\"roots\":{\"3f218a26-eb24-472c-b81d-c30c0cd11b12\":\"c7f979d8-95b0-42a4-92dc-2345e6812842\"},\"root_ids\":[\"3f218a26-eb24-472c-b81d-c30c0cd11b12\"]}];\n",
+       "  var docs = Object.values(docs_json)\n",
+       "  if (!docs) {\n",
+       "    return\n",
+       "  }\n",
+       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+       "  function embed_document(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+       "    for (const render_item of render_items) {\n",
+       "      for (const root_id of render_item.root_ids) {\n",
+       "\tconst id_el = document.getElementById(root_id)\n",
+       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+       "\t  const root_el = id_el.children[0]\n",
+       "\t  root_el.id = root_el.id + '-rendered'\n",
+       "\t}\n",
+       "      }\n",
+       "    }\n",
+       "  }\n",
+       "  function get_bokeh(root) {\n",
+       "    if (root.Bokeh === undefined) {\n",
+       "      return null\n",
+       "    } else if (root.Bokeh.version !== py_version) {\n",
+       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+       "\treturn null\n",
+       "      }\n",
+       "      return root.Bokeh.versions.get(py_version);\n",
+       "    } else if (root.Bokeh.version === py_version) {\n",
+       "      return root.Bokeh\n",
+       "    }\n",
+       "    return null\n",
+       "  }\n",
+       "  function is_loaded(root) {\n",
+       "    var Bokeh = get_bokeh(root)\n",
+       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+       "  }\n",
+       "  if (is_loaded(root)) {\n",
+       "    embed_document(root);\n",
+       "  } else {\n",
+       "    var attempts = 0;\n",
+       "    var timer = setInterval(function(root) {\n",
+       "      if (is_loaded(root)) {\n",
+       "        clearInterval(timer);\n",
+       "        embed_document(root);\n",
+       "      } else if (document.readyState == \"complete\") {\n",
+       "        attempts++;\n",
+       "        if (attempts > 200) {\n",
+       "          clearInterval(timer);\n",
+       "\t  var Bokeh = get_bokeh(root)\n",
+       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
+       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+       "\t  } else {\n",
+       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+       "\t    embed_document(root)\n",
+       "\t  }\n",
+       "        }\n",
+       "      }\n",
+       "    }, 25, root)\n",
+       "  }\n",
+       "})(window);</script>"
+      ],
+      "text/plain": [
+       "ChatInterface(_button_data={'send': _ChatButtonData(i...}, _buttons={'send': Button(align='cen...}, _input_container=Row, _input_layout=Row, _placeholder=ChatMessage, _widgets={'TextInput': TextInput(cs...}, callback=<function stream_response ..., show_button_name=True, sizing_mode='stretch_width', widgets=[TextInput(css_classes=['c...])"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {
+      "application/vnd.holoviews_exec.v0+json": {
+       "id": "3f218a26-eb24-472c-b81d-c30c0cd11b12"
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import llama_cpp\n",
     "import panel as pn\n",
-    "from pydantic import BaseModel\n",
     "from huggingface_hub import hf_hub_download\n",
     "pn.extension()\n",
     "\n",
@@ -316,14 +600,19 @@
     "    \"mistral-7b-instruct-v0.2.Q5_K_M.gguf\",\n",
     ")  # 1.\n",
     "\n",
-    "llama = llama_cpp.Llama(\n",
-    "    model_path=model_path,\n",
-    "    n_gpu_layers=-1,\n",
-    "    chat_format=\"mistral-instruct\",\n",
-    "    n_ctx=2048,\n",
-    "    logits_all=True,\n",
-    "    verbose=False,\n",
-    ") # 2.\n",
+    "# 2.\n",
+    "if model_path in pn.state.cache:\n",
+    "    llama = pn.state.cache[model_path]\n",
+    "else:\n",
+    "    llama = llama_cpp.Llama(\n",
+    "        model_path=model_path,\n",
+    "        n_gpu_layers=-1,\n",
+    "        chat_format=\"mistral-instruct\",\n",
+    "        n_ctx=2048,\n",
+    "        logits_all=True,\n",
+    "        verbose=False,\n",
+    "    )\n",
+    "    pn.state.cache[model_path] = llama\n",
     "\n",
     "def stream_response(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
     "    messages = instance.serialize()  # 3.\n",
@@ -356,7 +645,6 @@
    "source": [
     "import llama_cpp\n",
     "import panel as pn\n",
-    "from pydantic import BaseModel\n",
     "from huggingface_hub import hf_hub_download\n",
     "\n",
     "pn.extension()\n",
@@ -368,15 +656,19 @@
     "    \"mistral-7b-instruct-v0.2.Q5_K_M.gguf\",\n",
     ")  # 1.\n",
     "\n",
-    "llama = llama_cpp.Llama(\n",
-    "    model_path=model_path,\n",
-    "    n_gpu_layers=-1,\n",
-    "    chat_format=\"mistral-instruct\",\n",
-    "    n_ctx=2048,\n",
-    "    logits_all=True,\n",
-    "    verbose=False,\n",
-    ")  # 2.\n",
-    "\n",
+    "# 2.\n",
+    "if model_path in pn.state.cache:\n",
+    "    llama = pn.state.cache[model_path]\n",
+    "else:\n",
+    "    llama = llama_cpp.Llama(\n",
+    "        model_path=model_path,\n",
+    "        n_gpu_layers=-1,\n",
+    "        chat_format=\"mistral-instruct\",\n",
+    "        n_ctx=2048,\n",
+    "        logits_all=True,\n",
+    "        verbose=False,\n",
+    "    )\n",
+    "    pn.state.cache[model_path] = llama\n",
     "\n",
     "def stream_response(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
     "    messages = [\n",
@@ -443,15 +735,19 @@
     "    \"mistral-7b-instruct-v0.2.Q5_K_M.gguf\",\n",
     ")  # 1.\n",
     "\n",
-    "llama = llama_cpp.Llama(\n",
-    "    model_path=model_path,\n",
-    "    n_gpu_layers=-1,\n",
-    "    chat_format=\"mistral-instruct\",\n",
-    "    n_ctx=2048,\n",
-    "    logits_all=True,\n",
-    "    verbose=False,\n",
-    ")  # 2.\n",
-    "\n",
+    "# 2.\n",
+    "if model_path in pn.state.cache:\n",
+    "    llama = pn.state.cache[model_path]\n",
+    "else:\n",
+    "    llama = llama_cpp.Llama(\n",
+    "        model_path=model_path,\n",
+    "        n_gpu_layers=-1,\n",
+    "        chat_format=\"mistral-instruct\",\n",
+    "        n_ctx=2048,\n",
+    "        logits_all=True,\n",
+    "        verbose=False,\n",
+    "    )\n",
+    "    pn.state.cache[model_path] = llama\n",
     "\n",
     "def stream_response(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
     "    messages = [\n",
@@ -479,9 +775,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "holonote",
+   "display_name": "ahuang@anaconda.com-ahuang@anaconda.com-ragna-panel-presentations",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-ahuang_anaconda.com-ahuang_anaconda.com-ragna-panel-presentations-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -493,9 +789,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/environment.yml b/environment.yml
index a92d01a..cb6c716 100644
--- a/environment.yml
+++ b/environment.yml
@@ -1,12 +1,15 @@
 name: ragna-presentations
 channels:
+  - nvidia
   - conda-forge
 dependencies:
   - python=3.11
+  - libcublas
   - gxx
   - pip
   - pip:
       - python-dotenv
+      - panel
       - ragna @ git+https://github.com/Quansight/ragna@pycon
       - chromadb>=0.4.13
       - httpx_sse
@@ -18,9 +21,13 @@ dependencies:
       - python-pptx
       - tiktoken
       - torch ==2.2.*
+      - openai
+      - llama-cpp-python @
+        https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.72-cu121/llama_cpp_python-0.2.72-cp311-cp311-linux_x86_64.whl
       - exllamav2 @
         https://github.com/turboderp/exllamav2/releases/download/v0.0.18/exllamav2-0.0.18+cu121-cp311-cp311-linux_x86_64.whl
       - jupyterlab_nvdashboard
   - ipykernel
   - iprogress
   - ipywidgets
+variables: {}

From aff2030cb5f74edc1fab3d969e9ca787f181362d Mon Sep 17 00:00:00 2001
From: Andrew Huang <ahuang@anaconda.com>
Date: Fri, 10 May 2024 11:43:43 -0700
Subject: [PATCH 4/4] split notebooks

---
 04-panel-intro.ipynb     | 397 +++++++++++++++++++
 05-panel-local-llm.ipynb | 315 ++++++++++++++++
 06-panel-intro.ipynb     | 797 ---------------------------------------
 3 files changed, 712 insertions(+), 797 deletions(-)
 create mode 100644 04-panel-intro.ipynb
 create mode 100644 05-panel-local-llm.ipynb
 delete mode 100644 06-panel-intro.ipynb

diff --git a/04-panel-intro.ipynb b/04-panel-intro.ipynb
new file mode 100644
index 0000000..8c2545c
--- /dev/null
+++ b/04-panel-intro.ipynb
@@ -0,0 +1,397 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Intro to HoloViz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "HoloViz is a suite of high-level Python tools that are designed to work together to make visualizing data a breeze, from conducting exploratory data analysis to deploying complex dashboards.\n",
+    "\n",
+    "The core HoloViz projects are as follows:\n",
+    "\n",
+    "- [Panel](https://panel.holoviz.org): Create interactive dashboards in Jupyter notebooks or standalone apps\n",
+    "- [hvPlot](https://hvplot.holoviz.org): Quickly and interactively explore data with a familiar API\n",
+    "- [HoloViews](https://holoviews.org): Interactive plotting experience\n",
+    "- [GeoViews](http://geoviews.org): Geographic extension of HoloViews\n",
+    "- [Datashader](https://datashader.org): Render big data images in a browser\n",
+    "- [Lumen](https://lumen.holoviz.org/): Construct no-code dashboards from simple YAML specifications\n",
+    "- [Colorcet](https://colorcet.holoviz.org/): Plot with perceptually based colormaps\n",
+    "- [Param](https://param.holoviz.org): Declaratively code in Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What is Panel\n",
+    "\n",
+    "Today, the focus is on Panel.\n",
+    "\n",
+    "Panel packs many pre-built frontend components that are **usable with Python**.\n",
+    "\n",
+    "That means you can convert your static Python scripts into interactive ones--**no Javascript necessary**!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import panel as pn\n",
+    "pn.extension()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic Panel Tutorial\n",
+    "\n",
+    "Let's start out building an interactive app that allows the user to print a custom message.\n",
+    "\n",
+    "Currently it's hard coded to `\"Hello World\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Hello World!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Widget\n",
+    "\n",
+    "We can give the user more control by introducing a `TextInput` widget."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "message_input = pn.widgets.TextInput(value=\"Hello World!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interactivity\n",
+    "\n",
+    "Then, we can `pn.bind` the widget's `param.value` to the callback, `echo_message`, which simply echos the input value on change.\n",
+    "\n",
+    "Note: it's important to prefix `value` with `param`--without it, there will be no updates!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def echo_message(message):\n",
+    "    return f\"<i>{message}</i>\"\n",
+    "\n",
+    "message_ref = pn.bind(echo_message, message=message_input.param.value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Layout\n",
+    "\n",
+    "Next, create a simple layout to see the results.\n",
+    "\n",
+    "Try typing unique in the widget to see the message update!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pn.Column(message_input, message_ref)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Recap\n",
+    "\n",
+    "To recap, we:\n",
+    "\n",
+    "1. instantiated a widget (`TextInput`).\n",
+    "2. defined a function `echo_message`\n",
+    "3. bounded the function to the widget's *param* value\n",
+    "4. laid out the the widget and the bound reference\n",
+    "\n",
+    "![recap](images/recap.png)\n",
+    "\n",
+    "Here's all the code cells collected into one!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import panel as pn\n",
+    "pn.extension()\n",
+    "\n",
+    "message_input = pn.widgets.TextInput(value=\"Hello World!\")\n",
+    "\n",
+    "def echo_message(message):\n",
+    "    return f\"<i>{message}</i>\"\n",
+    "\n",
+    "message_ref = pn.bind(echo_message, message=message_input.param.value)\n",
+    "\n",
+    "pn.Column(message_input, message_ref)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Challenge\n",
+    "\n",
+    "Doing this repeatedly is key to creating more complex apps with Panel, so let's do a quick exercise.\n",
+    "\n",
+    "Your goal is to create a widget that will toggle the message to upper case if activated by filling out the ellipses (`...`)!\n",
+    "\n",
+    "Hint: check out the [Component gallery](https://panel.holoviz.org/reference/index.html) to see what widgets are available to accomplish this goal (one of them starts with a `T`, but there are multiple solutions!)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import panel as pn\n",
+    "pn.extension()\n",
+    "\n",
+    "message_input = pn.widgets.TextInput(value=\"Hello World!\")\n",
+    "toggle_upper = ...  # Fill this out\n",
+    "\n",
+    "def echo_message(message, toggle_upper):\n",
+    "    ...  # Fill this out\n",
+    "    return f\"<i>{message}</i>\"\n",
+    "\n",
+    "message_ref = pn.bind(echo_message, message=message_input.param.value, toggle_upper=...)  # Fill this out\n",
+    "\n",
+    "pn.Column(message_input, message_ref)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Congrats on building an interactive Panel app! 🎉"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introducing Panel ChatInterface"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, introducing `pn.chat.ChatInterface`, which is a component that packages all the steps you just learned to provide convenient features for developing a Chat UI with LLMs!\n",
+    "\n",
+    "### Widget\n",
+    "\n",
+    "Try typing a message and pressing enter to send!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chat = pn.chat.ChatInterface()\n",
+    "chat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You might have noticed that it echoes the message you entered, but it doesn't reply... not fun (yet).\n",
+    "\n",
+    "### Interactivity\n",
+    "\n",
+    "To make it reply, all we have to do is set a `callback`, like `pn.bind`, but with a caveat: it needs these three arguments: `contents`, `user`, and `instance`.\n",
+    "\n",
+    "Now when you try sending a message in the chat interface, it will be echoed back in italics!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def echo_message(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+    "    return f\"<i>{contents}</i>\"\n",
+    "\n",
+    "chat.callback = echo_message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Streaming\n",
+    "\n",
+    "You might have seen services, like OpenAI and Mistral, stream tokens as they arrive.\n",
+    "\n",
+    "We can simulate streaming tokens by looping through the contents of the user's input, concatenating the characters to the final message, and `yield`ing it in italics.\n",
+    "\n",
+    "Since there's no serious computation, it'll run too fast for us to perceive streaming--thus `time.sleep`.\n",
+    "\n",
+    "Here's the latest code collected into one (and also `callback` within instantation)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import panel as pn\n",
+    "pn.extension()\n",
+    "\n",
+    "def stream_echo_message(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+    "    message = \"\"\n",
+    "    for char in contents:\n",
+    "        time.sleep(0.1)  # to simulate a serious computation\n",
+    "        message += char\n",
+    "        yield f\"<i>{message}</i>\"\n",
+    "\n",
+    "chat = pn.chat.ChatInterface(callback=stream_echo_message)\n",
+    "chat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### More Interactivity\n",
+    "\n",
+    "`pn.chat.ChatInterface` can be used with other widgets too!\n",
+    "\n",
+    "Here, we include a `pn.widgets.FloatSlider` to control how long to wait between each character streamed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import panel as pn\n",
+    "pn.extension()\n",
+    "\n",
+    "def stream_echo_message(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+    "    message = \"\"\n",
+    "    for char in contents:\n",
+    "        time.sleep(slider.value)  # to simulate a serious computation\n",
+    "        message += char\n",
+    "        yield f\"<i>{message}</i>\"\n",
+    "\n",
+    "slider = pn.widgets.FloatSlider(start=0.01, value=0.5, name=\"Sleep (s)\", align=\"center\")\n",
+    "chat = pn.chat.ChatInterface(callback=stream_echo_message, min_height=350)\n",
+    "\n",
+    "pn.Column(slider, chat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Other Inputs\n",
+    "\n",
+    "`pn.chat.ChatInterface` also supports multi-modal inputs, like images, videos, PDFs, and more!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from io import BytesIO\n",
+    "import panel as pn\n",
+    "\n",
+    "pn.extension()\n",
+    "\n",
+    "def display_info(contents: BytesIO, user: str, instance: pn.chat.ChatInterface):\n",
+    "    size = len(contents.getvalue())\n",
+    "    return f\"Size of input: {size / (1024 * 1024):.2f} MB\"\n",
+    "\n",
+    "file_input = pn.widgets.FileInput(accept=\".jpeg,.png,.gif,.mp4,.pdf\")\n",
+    "chat = pn.chat.ChatInterface(widgets=[file_input], callback=display_info)\n",
+    "chat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's it for a crash course on Panel! These techniques, used repeatedly, will allow you to build increasingly complex web apps with just Python.\n",
+    "\n",
+    "To learn more about `pn.chat.ChatInterface`, click [here](https://panel.holoviz.org/reference/chat/ChatInterface.html). It inherits from `pn.chat.ChatFeed`, so check that out [here](https://panel.holoviz.org/reference/chat/ChatFeed.html) too!\n",
+    "\n",
+    "For more tutorials delving into Panel in general, click [here](https://panel.holoviz.org/tutorials/index.html) or check out the app gallery [here](https://panel.holoviz.org/gallery/index.html).\n",
+    "\n",
+    "There is also a HoloViz Discourse if you want to ask questions [here](https://discourse.holoviz.org/)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ahuang@anaconda.com-ahuang@anaconda.com-ragna-panel-presentations",
+   "language": "python",
+   "name": "conda-env-ahuang_anaconda.com-ahuang_anaconda.com-ragna-panel-presentations-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/05-panel-local-llm.ipynb b/05-panel-local-llm.ipynb
new file mode 100644
index 0000000..c4266ca
--- /dev/null
+++ b/05-panel-local-llm.ipynb
@@ -0,0 +1,315 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Local LLMs with Panel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the [previous notebook](04-panel-intro.ipynb), `pn.chat.ChatInterface` was introduced with a callback that simply echoed the sent message.\n",
+    "\n",
+    "In this section, we will make it much more interesting by connecting a local LLM, specfically Llama-3 from earlier.\n",
+    "\n",
+    "## ExLlama2\n",
+    "\n",
+    "### Initialize\n",
+    "\n",
+    "Let's first initialize the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from local_llm import Llama38BInstruct\n",
+    "from ragna import Rag, source_storages\n",
+    "import panel as pn\n",
+    "pn.extension()\n",
+    "\n",
+    "documents = [\n",
+    "    \"files/psf-report-2021.pdf\",\n",
+    "    \"files/psf-report-2022.pdf\",\n",
+    "]\n",
+    "\n",
+    "chat = Rag().chat(\n",
+    "    documents=documents,\n",
+    "    source_storage=source_storages.Chroma,\n",
+    "    assistant=Llama38BInstruct,\n",
+    ")\n",
+    "\n",
+    "await chat.prepare();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Migrate\n",
+    "\n",
+    "We can first do a test run to see if it works with the example from before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "message = await chat.answer(\"Who is the Python Developer in Residence?\", stream=True)\n",
+    "\n",
+    "async for chunk in message:\n",
+    "    print(chunk, end=\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's migrate this functionality into `pn.chat.ChatInterface` with a callback.\n",
+    "\n",
+    "To do this, we copy paste the prior cell's code into a function, and then:\n",
+    "\n",
+    "1. prefix the `def` with `async` to make it async\n",
+    "2. replace the hard-coded string with `contents`\n",
+    "3. concatenate the chunks into a `response` string\n",
+    "4. yield the `response`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "async def reply(contents, user, instance):\n",
+    "    message = await chat.answer(contents, stream=True)\n",
+    "\n",
+    "    response = \"\"\n",
+    "    async for chunk in message:\n",
+    "        response += chunk\n",
+    "        yield response\n",
+    "\n",
+    "chat_interface = pn.chat.ChatInterface(callback=reply)\n",
+    "chat_interface"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now try entering \"Who is the Python Developer in Residence?\" into the chat. It should give you a similar response as before!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LlamaCpp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For posterity, we can use `llama-cpp-python` for quantized models too!\n",
+    "\n",
+    "`llama-cpp` can run on both CPU and GPU, and has an API that mimics OpenAI's API.\n",
+    "\n",
+    "Personally, I use it because I don't have any spare GPUs lying around and it runs extremely well on my local Mac M2 Pro! It also handles chat template formats internally so it's just a matter of specifying a the proper `chat_format` key.\n",
+    "\n",
+    "Here, we:\n",
+    "1. download the quantized model (if it doesn't exist already) in GGUF format\n",
+    "2. instantiate the model; first checking the cache\n",
+    "3. serialize all messages into `transformers` format (new)\n",
+    "4. calls the chat completion Openai-like API on the messages\n",
+    "5. stream the chunks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import llama_cpp\n",
+    "import panel as pn\n",
+    "from huggingface_hub import hf_hub_download\n",
+    "pn.extension()\n",
+    "\n",
+    "model_path = hf_hub_download(\n",
+    "    \"TheBloke/Mistral-7B-Instruct-v0.2-GGUF\",\n",
+    "    \"mistral-7b-instruct-v0.2.Q5_K_M.gguf\",\n",
+    "    local_dir=str(Path.home() / \"shared/analyst/models\")\n",
+    ")  # 1.\n",
+    "\n",
+    "# 2.\n",
+    "if model_path in pn.state.cache:\n",
+    "    llama = pn.state.cache[model_path]\n",
+    "else:\n",
+    "    llama = llama_cpp.Llama(\n",
+    "        model_path=model_path,\n",
+    "        n_gpu_layers=-1,\n",
+    "        chat_format=\"mistral-instruct\",\n",
+    "        n_ctx=2048,\n",
+    "        logits_all=True,\n",
+    "        verbose=False,\n",
+    "    )\n",
+    "    pn.state.cache[model_path] = llama\n",
+    "\n",
+    "def reply(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+    "    messages = instance.serialize()  # 3.\n",
+    "    message = llama.create_chat_completion_openai_v1(messages=messages, stream=True)  # 4.\n",
+    "\n",
+    "    response = \"\"\n",
+    "    for chunk in message:\n",
+    "        part = chunk.choices[0].delta.content or \"\"\n",
+    "        response += part\n",
+    "        yield response  # 5.\n",
+    "\n",
+    "chat_interface = pn.chat.ChatInterface(callback=reply)\n",
+    "chat_interface"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can even give the model a personality by setting a system message!\n",
+    "\n",
+    "Update the callback with the a system message.\n",
+    "\n",
+    "Note, Mistral Instruct does NOT support the `system` role so we use `user` instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system_message = \"You are an excessively passionate Pythonista.\"\n",
+    "\n",
+    "def reply(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+    "    messages = [\n",
+    "        {\"role\": \"user\", \"content\": system_message}  # updated here\n",
+    "    ] + instance.serialize()\n",
+    "    message = llama.create_chat_completion_openai_v1(messages=messages, stream=True)\n",
+    "\n",
+    "    response = \"\"\n",
+    "    for chunk in message:\n",
+    "        part = chunk.choices[0].delta.content or \"\"\n",
+    "        response += part\n",
+    "        yield response\n",
+    "\n",
+    "chat_interface.callback = reply\n",
+    "chat_interface"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Challenge\n",
+    "\n",
+    "Your turn! Try aggregating all you've learned to customize the personality of the chatbot on the go!\n",
+    "\n",
+    "Again, replace the ellipses with the appropriate code snippets!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import llama_cpp\n",
+    "import panel as pn\n",
+    "from pydantic import BaseModel\n",
+    "from huggingface_hub import hf_hub_download\n",
+    "\n",
+    "pn.extension()\n",
+    "\n",
+    "model_path = hf_hub_download(\n",
+    "    \"TheBloke/Mistral-7B-Instruct-v0.2-GGUF\",\n",
+    "    \"mistral-7b-instruct-v0.2.Q5_K_M.gguf\",\n",
+    "    local_dir=str(Path.home() / \"shared/analyst/models\")\n",
+    ")\n",
+    "\n",
+    "if model_path in pn.state.cache:\n",
+    "    llama = pn.state.cache[model_path]\n",
+    "else:\n",
+    "    llama = llama_cpp.Llama(\n",
+    "        model_path=model_path,\n",
+    "        n_gpu_layers=-1,\n",
+    "        chat_format=\"mistral-instruct\",\n",
+    "        n_ctx=2048,\n",
+    "        logits_all=True,\n",
+    "        verbose=False,\n",
+    "    )\n",
+    "    pn.state.cache[model_path] = llama\n",
+    "\n",
+    "def reply(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
+    "    messages = [\n",
+    "        {\"role\": \"user\", \"content\": ...}  # Fill this out\n",
+    "    ] + instance.serialize()\n",
+    "    message = llama.create_chat_completion_openai_v1(\n",
+    "        messages=messages, stream=True\n",
+    "    )\n",
+    "\n",
+    "    response = \"\"\n",
+    "    for chunk in message:\n",
+    "        part = chunk.choices[0].delta.content or \"\"\n",
+    "        response += part\n",
+    "        yield response\n",
+    "\n",
+    "\n",
+    "system_input = ...  # Fill this out\n",
+    "chat_interface = pn.chat.ChatInterface(callback=reply, min_height=350)\n",
+    "layout = pn.Column(\n",
+    "    system_input,\n",
+    "    chat_interface,\n",
+    ")\n",
+    "layout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's all for now. Click [here](https://holoviz-topics.github.io/panel-chat-examples/) to see more on how you can integrate `pn.chat.ChatInterface` with other services!\n",
+    "\n",
+    "Again, there is also a HoloViz Discourse if you want to ask questions [here](https://discourse.holoviz.org/)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ahuang@anaconda.com-ahuang@anaconda.com-ragna-panel-presentations",
+   "language": "python",
+   "name": "conda-env-ahuang_anaconda.com-ahuang_anaconda.com-ragna-panel-presentations-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/06-panel-intro.ipynb b/06-panel-intro.ipynb
deleted file mode 100644
index b1261b8..0000000
--- a/06-panel-intro.ipynb
+++ /dev/null
@@ -1,797 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Intro to HoloViz"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "HoloViz is a suite of high-level Python tools that are designed to work together to make visualizing data a breeze, from conducting exploratory data analysis to deploying complex dashboards.\n",
-    "\n",
-    "The core HoloViz projects are as follows:\n",
-    "\n",
-    "- [Panel](https://panel.holoviz.org): Create interactive dashboards in Jupyter notebooks or standalone apps\n",
-    "- [hvPlot](https://hvplot.holoviz.org): Quickly and interactively explore data with a familiar API\n",
-    "- [HoloViews](https://holoviews.org): Interactive plotting experience\n",
-    "- [GeoViews](http://geoviews.org): Geographic extension of HoloViews\n",
-    "- [Datashader](https://datashader.org): Render big data images in a browser\n",
-    "- [Lumen](https://lumen.holoviz.org/): Construct no-code dashboards from simple YAML specifications\n",
-    "- [Colorcet](https://colorcet.holoviz.org/): Plot with perceptually based colormaps\n",
-    "- [Param](https://param.holoviz.org): Declaratively code in Python"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## What is Panel\n",
-    "\n",
-    "Today, the focus is on Panel.\n",
-    "\n",
-    "Panel packs many pre-built frontend components that are **usable with Python**.\n",
-    "\n",
-    "That means you can convert your static Python scripts into interactive ones--**no Javascript necessary**!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import panel as pn\n",
-    "pn.extension()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Basic Panel Tutorial\n",
-    "\n",
-    "Let's start out building an interactive app that allows the user to print a custom message.\n",
-    "\n",
-    "Currently it's hard coded to `\"Hello World\"`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"Hello World!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can give the user more control by introducing a `TextInput` widget."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "message_input = pn.widgets.TextInput(value=\"Hello World!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then, we can `pn.bind` the widget's `param.value` to the callback, `echo_message`, which simply echos the input value on change.\n",
-    "\n",
-    "Note: it's important to prefix `value` with `param`--without it, there will be no updates!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def echo_message(message):\n",
-    "    return f\"<i>{message}</i>\"\n",
-    "\n",
-    "message_ref = pn.bind(echo_message, message=message_input.param.value)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, create a simple layout to see the results.\n",
-    "\n",
-    "Try typing unique in the widget to see the message update!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pn.Column(message_input, message_ref)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To recap, we:\n",
-    "\n",
-    "1. instantiated a widget (`TextInput`).\n",
-    "2. defined a function `echo_message`\n",
-    "3. bounded the function to the widget's *param* value\n",
-    "4. laid out the the widget and the bound reference\n",
-    "\n",
-    "![recap](images/.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's all the code cells collected into one!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import panel as pn\n",
-    "pn.extension()\n",
-    "\n",
-    "message_input = pn.widgets.TextInput(value=\"Hello World!\")\n",
-    "\n",
-    "def echo_message(message):\n",
-    "    return f\"<i>{message}</i>\"\n",
-    "\n",
-    "message_ref = pn.bind(echo_message, message=message_input.param.value)\n",
-    "\n",
-    "pn.Column(message_input, message_ref)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Doing this repeatedly is key to creating more complex apps with Panel, so let's do a quick exercise.\n",
-    "\n",
-    "Your goal is to create a widget that will toggle the message to upper case if activated by filling out the ellipses (`...`)!\n",
-    "\n",
-    "Hint: check out the [Component gallery](https://panel.holoviz.org/reference/index.html) to see what widgets are available to accomplish this goal (one of them starts with a T, but there are multiple solutions!)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import panel as pn\n",
-    "pn.extension()\n",
-    "\n",
-    "message_input = pn.widgets.TextInput(value=\"Hello World!\")\n",
-    "toggle_upper = ...\n",
-    "\n",
-    "def echo_message(message, toggle_upper):\n",
-    "    ...\n",
-    "    return f\"<i>{message}</i>\"\n",
-    "\n",
-    "message_ref = pn.bind(echo_message, message=message_input.param.value, toggle_upper=...)\n",
-    "\n",
-    "pn.Column(message_input, message_ref)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Congrats on building an interactive Panel app! 🎉"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Basic Panel ChatInterface"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, introducing `pn.chat.ChatInterface`, which is a component that packages all the steps you just learned to provide convenient features for developing a Chat UI with LLMs!\n",
-    "\n",
-    "Try typing a message and pressing enter to send!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chat = pn.chat.ChatInterface()\n",
-    "chat"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You might have noticed that it echoes the message you entered, but it doesn't reply... not fun (yet).\n",
-    "\n",
-    "To make it reply, all we have to do is set a `callback`, like `pn.bind`, but with a caveat: it needs these three arguments: `contents`, `user`, and `instance`.\n",
-    "\n",
-    "Now when you try sending a message in the chat interface, it will be echoed back in italics!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def echo_message(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
-    "    return f\"<i>{contents}</i>\"\n",
-    "\n",
-    "chat.callback = echo_message"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You might have seen services, like OpenAI and Mistral, stream tokens as they arrive.\n",
-    "\n",
-    "We can simulate streaming tokens by looping through the contents of the user's input, concatenating the characters to the final message, and `yield`ing it.\n",
-    "\n",
-    "Since there's no serious computation, it'll run too fast for us to perceive streaming--thus `time.sleep`.\n",
-    "\n",
-    "Here's the latest code collected into one (and also `callback` within instantation)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "import panel as pn\n",
-    "pn.extension()\n",
-    "\n",
-    "def stream_echo_message(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
-    "    message = \"\"\n",
-    "    for char in contents:\n",
-    "        time.sleep(0.1)  # to simulate a serious computation\n",
-    "        message += char\n",
-    "        yield f\"<i>{message}</i>\"\n",
-    "\n",
-    "chat = pn.chat.ChatInterface(callback=stream_echo_message)\n",
-    "chat"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Awesome! Now let's make it much more interesting by connecting an LLM, like the quantized Mistral Instruct 7B model through ExLlama (so no API key necessary)!\n",
-    "\n",
-    "Here, we:\n",
-    "1. download the quantized model (if it doesn't exist already) in exl2 format\n",
-    "2. instantiate the model; first checking the cache\n",
-    "3. calls the chat completion through the streaming generator\n",
-    "4. stream the chunks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import panel as pn\n",
-    "from huggingface_hub import snapshot_download\n",
-    "from exllamav2 import(\n",
-    "    ExLlamaV2,\n",
-    "    ExLlamaV2Config,\n",
-    "    ExLlamaV2Cache,\n",
-    "    ExLlamaV2Tokenizer,\n",
-    ")\n",
-    "\n",
-    "from exllamav2.generator import (\n",
-    "    ExLlamaV2BaseGenerator,\n",
-    "    ExLlamaV2Sampler\n",
-    ")\n",
-    "from exllamav2.generator import ExLlamaV2Sampler, ExLlamaV2StreamingGenerator\n",
-    "\n",
-    "model_directory = snapshot_download(\n",
-    "    repo_id=\"turboderp/Mistral-7B-v0.2-exl2\", revision=\"2.5bpw\"\n",
-    ")  # 1.\n",
-    "\n",
-    "# 2.\n",
-    "if model_directory in pn.state.cache:\n",
-    "    generator = pn.state.cache[model_directory]\n",
-    "else:\n",
-    "    config = ExLlamaV2Config()\n",
-    "    config.model_dir = model_directory\n",
-    "    config.prepare()\n",
-    "    model = ExLlamaV2(config)\n",
-    "    cache = ExLlamaV2Cache(model, lazy=True)\n",
-    "    model.load_autosplit(cache)\n",
-    "    tokenizer = ExLlamaV2Tokenizer(config)\n",
-    "    generator = ExLlamaV2BaseGenerator(model, cache, tokenizer)\n",
-    "    settings = ExLlamaV2Sampler.Settings()\n",
-    "    settings.temperature = 0.85\n",
-    "    settings.top_k = 50\n",
-    "    settings.top_p = 0.8\n",
-    "    settings.token_repetition_penalty = 1.01\n",
-    "    settings.disallow_tokens(tokenizer, [tokenizer.eos_token_id])\n",
-    "    generator = ExLlamaV2StreamingGenerator(model, cache, tokenizer)\n",
-    "    pn.state.cache[model_directory] = generator\n",
-    "\n",
-    "\n",
-    "def stream_response(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
-    "    input_ids = tokenizer.encode(contents, add_bos=False)\n",
-    "    generator.begin_stream_ex(input_ids, settings)\n",
-    "\n",
-    "    message = \"\"\n",
-    "    for _ in range(256):\n",
-    "        result = generator.stream_ex()\n",
-    "        if result[\"eos\"]:\n",
-    "            break\n",
-    "        message += result[\"chunk\"]  # 4.\n",
-    "        yield message\n",
-    "\n",
-    "\n",
-    "chat = pn.chat.ChatInterface(callback=stream_response)\n",
-    "chat"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For posterity, we can use `llama-cpp-python` for quantized models too!\n",
-    "\n",
-    "`llama-cpp` can run on both CPU and GPU, and has an API that mimics OpenAI's API. Personally, I use it because I don't have any spare GPUs lying around and it runs extremely well on my local Mac M2 Pro! It also handles chat template formats internally so it's just a matter of specifying a the proper `chat_format` key.\n",
-    "\n",
-    "Here, we:\n",
-    "1. download the quantized model (if it doesn't exist already) in GGUF format\n",
-    "2. instantiate the model; first checking the cache\n",
-    "3. serialize all messages into `transformers` format (new)\n",
-    "4. calls the chat completion Openai-like API on the messages\n",
-    "5. stream the chunks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "</div>\n",
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fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
-       "  var render_items = [{\"docid\":\"0e4775a0-c3ac-4fa6-8d04-71f9126436be\",\"roots\":{\"3f218a26-eb24-472c-b81d-c30c0cd11b12\":\"c7f979d8-95b0-42a4-92dc-2345e6812842\"},\"root_ids\":[\"3f218a26-eb24-472c-b81d-c30c0cd11b12\"]}];\n",
-       "  var docs = Object.values(docs_json)\n",
-       "  if (!docs) {\n",
-       "    return\n",
-       "  }\n",
-       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
-       "  function embed_document(root) {\n",
-       "    var Bokeh = get_bokeh(root)\n",
-       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
-       "    for (const render_item of render_items) {\n",
-       "      for (const root_id of render_item.root_ids) {\n",
-       "\tconst id_el = document.getElementById(root_id)\n",
-       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
-       "\t  const root_el = id_el.children[0]\n",
-       "\t  root_el.id = root_el.id + '-rendered'\n",
-       "\t}\n",
-       "      }\n",
-       "    }\n",
-       "  }\n",
-       "  function get_bokeh(root) {\n",
-       "    if (root.Bokeh === undefined) {\n",
-       "      return null\n",
-       "    } else if (root.Bokeh.version !== py_version) {\n",
-       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
-       "\treturn null\n",
-       "      }\n",
-       "      return root.Bokeh.versions.get(py_version);\n",
-       "    } else if (root.Bokeh.version === py_version) {\n",
-       "      return root.Bokeh\n",
-       "    }\n",
-       "    return null\n",
-       "  }\n",
-       "  function is_loaded(root) {\n",
-       "    var Bokeh = get_bokeh(root)\n",
-       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
-       "  }\n",
-       "  if (is_loaded(root)) {\n",
-       "    embed_document(root);\n",
-       "  } else {\n",
-       "    var attempts = 0;\n",
-       "    var timer = setInterval(function(root) {\n",
-       "      if (is_loaded(root)) {\n",
-       "        clearInterval(timer);\n",
-       "        embed_document(root);\n",
-       "      } else if (document.readyState == \"complete\") {\n",
-       "        attempts++;\n",
-       "        if (attempts > 200) {\n",
-       "          clearInterval(timer);\n",
-       "\t  var Bokeh = get_bokeh(root)\n",
-       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
-       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
-       "\t  } else {\n",
-       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
-       "\t    embed_document(root)\n",
-       "\t  }\n",
-       "        }\n",
-       "      }\n",
-       "    }, 25, root)\n",
-       "  }\n",
-       "})(window);</script>"
-      ],
-      "text/plain": [
-       "ChatInterface(_button_data={'send': _ChatButtonData(i...}, _buttons={'send': Button(align='cen...}, _input_container=Row, _input_layout=Row, _placeholder=ChatMessage, _widgets={'TextInput': TextInput(cs...}, callback=<function stream_response ..., show_button_name=True, sizing_mode='stretch_width', widgets=[TextInput(css_classes=['c...])"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {
-      "application/vnd.holoviews_exec.v0+json": {
-       "id": "3f218a26-eb24-472c-b81d-c30c0cd11b12"
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import llama_cpp\n",
-    "import panel as pn\n",
-    "from huggingface_hub import hf_hub_download\n",
-    "pn.extension()\n",
-    "\n",
-    "model_path = hf_hub_download(\n",
-    "    \"TheBloke/Mistral-7B-Instruct-v0.2-GGUF\",\n",
-    "    \"mistral-7b-instruct-v0.2.Q5_K_M.gguf\",\n",
-    ")  # 1.\n",
-    "\n",
-    "# 2.\n",
-    "if model_path in pn.state.cache:\n",
-    "    llama = pn.state.cache[model_path]\n",
-    "else:\n",
-    "    llama = llama_cpp.Llama(\n",
-    "        model_path=model_path,\n",
-    "        n_gpu_layers=-1,\n",
-    "        chat_format=\"mistral-instruct\",\n",
-    "        n_ctx=2048,\n",
-    "        logits_all=True,\n",
-    "        verbose=False,\n",
-    "    )\n",
-    "    pn.state.cache[model_path] = llama\n",
-    "\n",
-    "def stream_response(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
-    "    messages = instance.serialize()  # 3.\n",
-    "    response = llama.create_chat_completion_openai_v1(messages=messages, stream=True)  # 4.\n",
-    "\n",
-    "    message = \"\"\n",
-    "    for chunk in response:\n",
-    "        part = chunk.choices[0].delta.content or \"\"\n",
-    "        message += part\n",
-    "        yield message  # 5.\n",
-    "\n",
-    "chat = pn.chat.ChatInterface(callback=stream_response)\n",
-    "chat"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can even give the model a personality by setting a system message!\n",
-    "\n",
-    "Note, Mistral Instruct does NOT support the `system` role."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import llama_cpp\n",
-    "import panel as pn\n",
-    "from huggingface_hub import hf_hub_download\n",
-    "\n",
-    "pn.extension()\n",
-    "\n",
-    "system_message = \"You are an excessively passionate Pythonista.\"\n",
-    "\n",
-    "model_path = hf_hub_download(\n",
-    "    \"TheBloke/Mistral-7B-Instruct-v0.2-GGUF\",\n",
-    "    \"mistral-7b-instruct-v0.2.Q5_K_M.gguf\",\n",
-    ")  # 1.\n",
-    "\n",
-    "# 2.\n",
-    "if model_path in pn.state.cache:\n",
-    "    llama = pn.state.cache[model_path]\n",
-    "else:\n",
-    "    llama = llama_cpp.Llama(\n",
-    "        model_path=model_path,\n",
-    "        n_gpu_layers=-1,\n",
-    "        chat_format=\"mistral-instruct\",\n",
-    "        n_ctx=2048,\n",
-    "        logits_all=True,\n",
-    "        verbose=False,\n",
-    "    )\n",
-    "    pn.state.cache[model_path] = llama\n",
-    "\n",
-    "def stream_response(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
-    "    messages = [\n",
-    "        {\"role\": \"user\", \"content\": system_message}\n",
-    "    ] + instance.serialize()  # 3.\n",
-    "    response = llama.create_chat_completion_openai_v1(\n",
-    "        messages=messages, stream=True\n",
-    "    )  # 4.\n",
-    "\n",
-    "    message = \"\"\n",
-    "    for chunk in response:\n",
-    "        part = chunk.choices[0].delta.content or \"\"\n",
-    "        message += part\n",
-    "        yield message  # 5.\n",
-    "\n",
-    "\n",
-    "chat = pn.chat.ChatInterface(callback=stream_response)\n",
-    "chat"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can make this Chat UI improved by using templates."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "template = pn.template.FastListTemplate(main=[chat], title=\"Chatbot\", accent=\"#A01346\")\n",
-    "template.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Your turn! Try aggregating all you've learned to customize the personality of the chatbot on the go!\n",
-    "\n",
-    "Again, replace the ellipses with the appropriate code snippets!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import llama_cpp\n",
-    "import panel as pn\n",
-    "from pydantic import BaseModel\n",
-    "from huggingface_hub import hf_hub_download\n",
-    "\n",
-    "pn.extension()\n",
-    "\n",
-    "system_message = \"You are an excessively passionate Pythonista.\"\n",
-    "\n",
-    "model_path = hf_hub_download(\n",
-    "    \"TheBloke/Mistral-7B-Instruct-v0.2-GGUF\",\n",
-    "    \"mistral-7b-instruct-v0.2.Q5_K_M.gguf\",\n",
-    ")  # 1.\n",
-    "\n",
-    "# 2.\n",
-    "if model_path in pn.state.cache:\n",
-    "    llama = pn.state.cache[model_path]\n",
-    "else:\n",
-    "    llama = llama_cpp.Llama(\n",
-    "        model_path=model_path,\n",
-    "        n_gpu_layers=-1,\n",
-    "        chat_format=\"mistral-instruct\",\n",
-    "        n_ctx=2048,\n",
-    "        logits_all=True,\n",
-    "        verbose=False,\n",
-    "    )\n",
-    "    pn.state.cache[model_path] = llama\n",
-    "\n",
-    "def stream_response(contents: str, user: str, instance: pn.chat.ChatInterface):\n",
-    "    messages = [\n",
-    "        {\"role\": \"user\", \"content\": ...}\n",
-    "    ] + instance.serialize()  # 3.\n",
-    "    response = llama.create_chat_completion_openai_v1(\n",
-    "        messages=messages, stream=True\n",
-    "    )  # 4.\n",
-    "\n",
-    "    message = \"\"\n",
-    "    for chunk in response:\n",
-    "        part = chunk.choices[0].delta.content or \"\"\n",
-    "        message += part\n",
-    "        yield message  # 5.\n",
-    "\n",
-    "\n",
-    "system_input = ...\n",
-    "chat = pn.chat.ChatInterface(callback=stream_response)\n",
-    "template = pn.template.FastListTemplate(\n",
-    "    main=[chat], sidebar=[...], title=\"Chatbot\", accent=\"#A01346\"\n",
-    ")\n",
-    "template.show()"
-   ]
-  }
- ],
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-  "kernelspec": {
-   "display_name": "ahuang@anaconda.com-ahuang@anaconda.com-ragna-panel-presentations",
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