Merge branch 'main' into machow-feat-data-grid-polars

* main:
  test(controllers): Refactor column sort and filter methods for Dataframe class (posit-dev#1496)
  Follow up to posit-dev#1453: allow user roles when normalizing a dictionary (posit-dev#1495)
  fix(layout_columns): Fix coercion of scalar row height to list for python <= 3.9 (posit-dev#1494)
  Add `shiny.ui.Chat` (posit-dev#1453)
  docs(Theme): Fix example and clarify usage (posit-dev#1491)
  chore(pyright): Pin pyright version to `1.1.369` to avoid CI failures (posit-dev#1493)
  tests(dataframe): Add additional tests for dataframe (posit-dev#1487)
  bug(data frame): Export `render.StyleInfo` (posit-dev#1488)

CHANGELOG.md

@@ -19,6 +19,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### New features
 
+* Added a new `shiny.ui.Chat` class for building conversational interfaces with fully customizable and performant response generation. (#1453)
+
 * Expose `shiny.playwright`, `shiny.run`, and `shiny.pytest` modules that allow users to testing their Shiny apps.  (#1448, #1456, #1481)
   * `shiny.playwright` contains `controller` and `expect` submodules. `controller` will contain many classes to interact with (and verify!) your Shiny app using Playwright. `expect` contains expectation functions that enhance standard Playwright expectation methods.
   * `shiny.run` contains the `run_shiny_app` command and the return type `ShinyAppProc`. `ShinyAppProc` can be used to type the Shiny app pytest fixtures.

docs/_quartodoc-core.yml

@@ -96,6 +96,11 @@ quartodoc:
         - ui.input_file
         - ui.download_button
         - ui.download_link
+    - title: Chat interface
+      desc: Build a chatbot interface
+      contents:
+        - ui.Chat
+        - ui.chat_ui
     - title: Custom UI
       desc: Lower-level UI functions for creating custom HTML/CSS/JS
       contents:

docs/_quartodoc-express.yml

@@ -76,6 +76,10 @@ quartodoc:
         - express.ui.navset_underline
         - express.ui.navset_pill_list
         - express.ui.navset_hidden
+    - title: Chat interface
+      desc: Build a chatbot interface
+      contents:
+        - express.ui.Chat
     - title: Reactive programming
       desc: Create reactive functions and dependencies.
       contents:

examples/chat/.gitignore

@@ -0,0 +1 @@
+.env

examples/chat/RAG/recipes/app.py

@@ -0,0 +1,57 @@
+# ------------------------------------------------------------------------------------
+# A simple recipe extractor chatbot that extracts recipes from URLs using the OpenAI API.
+# To run it, you'll need an OpenAI API key.
+# To get one, follow the instructions at https://platform.openai.com/docs/quickstart
+# ------------------------------------------------------------------------------------
+import os
+
+from openai import AsyncOpenAI
+from utils import recipe_prompt, scrape_page_with_url
+
+from shiny.express import ui
+
+# Provide your API key here (or set the environment variable)
+llm = AsyncOpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
+
+# Set some Shiny page options
+ui.page_opts(
+    title="Recipe Extractor Chat",
+    fillable=True,
+    fillable_mobile=True,
+)
+
+# Initialize the chat (with a system prompt and starting message)
+chat = ui.Chat(
+    id="chat",
+    messages=[
+        {"role": "system", "content": recipe_prompt},
+        {
+            "role": "assistant",
+            "content": "Hello! I'm a recipe extractor. Please enter a URL to a recipe page. For example, <https://www.thechunkychef.com/epic-dry-rubbed-baked-chicken-wings/>",
+        },
+    ],
+)
+
+chat.ui(placeholder="Enter a recipe URL...")
+
+
+# A function to transform user input
+# Note that, if an exception occurs, the function will return a message to the user
+# "short-circuiting" the conversation and asking the user to try again.
+@chat.transform_user_input
+async def try_scrape_page(input: str) -> str | None:
+    try:
+        return await scrape_page_with_url(input)
+    except Exception:
+        await chat.append_message(
+            "I'm sorry, I couldn't extract content from that URL. Please try again. "
+        )
+        return None
+
+
+@chat.on_user_submit
+async def _():
+    response = await llm.chat.completions.create(
+        model="gpt-4o", messages=chat.messages(), temperature=0, stream=True
+    )
+    await chat.append_message_stream(response)

examples/chat/RAG/recipes/utils.py

@@ -0,0 +1,106 @@
+import aiohttp
+from bs4 import BeautifulSoup
+
+recipe_prompt = """
+You are RecipeExtractorGPT.
+Your goal is to extract recipe content from text and return a JSON representation of the useful information.
+
+The JSON should be structured like this:
+
+```
+{
+  "title": "Scrambled eggs",
+  "ingredients": {
+    "eggs": "2",
+    "butter": "1 tbsp",
+    "milk": "1 tbsp",
+    "salt": "1 pinch"
+  },
+  "directions": [
+    "Beat eggs, milk, and salt together in a bowl until thoroughly combined.",
+    "Heat butter in a large skillet over medium-high heat. Pour egg mixture into the hot skillet; cook and stir until eggs are set, 3 to 5 minutes."
+  ],
+  "servings": 2,
+  "prep_time": 5,
+  "cook_time": 5,
+  "total_time": 10,
+  "tags": [
+    "breakfast",
+    "eggs",
+    "scrambled"
+  ],
+  "source": "https://recipes.com/scrambled-eggs/",
+}
+```
+
+The user will provide text content from a web page.
+It is not very well structured, but the recipe is in there.
+Please look carefully for the useful information about the recipe.
+IMPORTANT: Return the result as JSON in a Markdown code block surrounded with three backticks!
+"""
+
+
+async def scrape_page_with_url(url: str, max_length: int = 14000) -> str:
+    """
+    Given a URL, scrapes the web page and return the contents. This also adds adds the
+    URL to the beginning of the text.
+
+    Parameters
+    ----------
+    url:
+      The URL to scrape
+    max_length:
+      Max length of recipe text to process. This is to prevent the model from running
+      out of tokens. 14000 bytes translates to approximately 3200 tokens.
+    """
+    contents = await scrape_page(url)
+    # Trim the string so that the prompt and reply will fit in the token limit.. It
+    # would be better to trim by tokens, but that requires using the tiktoken package,
+    # which can be very slow to load when running on containerized servers, because it
+    # needs to download the model from the internet each time the container starts.
+    contents = contents[:max_length]
+    return f"From: {url}\n\n" + contents
+
+
+async def scrape_page(url: str) -> str:
+    # Asynchronously send an HTTP request to the URL.
+    async with aiohttp.ClientSession() as session:
+        async with session.get(url) as response:
+            if response.status != 200:
+                raise aiohttp.ClientError(f"An error occurred: {response.status}")
+            html = await response.text()
+
+    # Parse the HTML content using BeautifulSoup
+    soup = BeautifulSoup(html, "html.parser")
+
+    # Remove script and style elements
+    for script in soup(["script", "style"]):
+        script.decompose()
+
+    # List of element IDs or class names to remove
+    elements_to_remove = [
+        "header",
+        "footer",
+        "sidebar",
+        "nav",
+        "menu",
+        "ad",
+        "advertisement",
+        "cookie-banner",
+        "popup",
+        "social",
+        "breadcrumb",
+        "pagination",
+        "comment",
+        "comments",
+    ]
+
+    # Remove unwanted elements by ID or class name
+    for element in elements_to_remove:
+        for e in soup.find_all(id=element) + soup.find_all(class_=element):
+            e.decompose()
+
+    # Extract text from the remaining HTML tags
+    text = " ".join(soup.stripped_strings)
+
+    return text

examples/chat/README.md

@@ -0,0 +1,5 @@
+# Shiny `Chat` examples
+
+This folder contains a collection of examples illustrating `shiny.ui.Chat` usage. Many of them require API keys from providers such as OpenAI, Anthropic, etc. In those cases, the example should have commentary explaining how to obtain keys as well as how to provide them to the app.
+
+To get started with an app that doesn't require an API key, see the `hello-world` example. This example has both a Shiny Core and Express app to illustrate how it's used in either mode.

examples/chat/enterprise/aws-bedrock-anthropic/app.py

@@ -0,0 +1,47 @@
+# ------------------------------------------------------------------------------------
+# A basic Shiny Chat powered by Anthropic's Claude model with Bedrock.
+# To run it, you'll need an AWS Bedrock configuration.
+# To get started, follow the instructions at https://aws.amazon.com/bedrock/claude/
+# as well as https://github.com/anthropics/anthropic-sdk-python#aws-bedrock
+# ------------------------------------------------------------------------------------
+from anthropic import AnthropicBedrock
+
+from shiny.express import ui
+
+# Although you can set the AWS credentials here, it's recommended to put them in an .env
+# file and load them with `dotenv` so your keys aren't exposed with your code.
+# from dotenv import load_dotenv
+# _ = load_dotenv()
+llm = AnthropicBedrock(
+    # aws_secret_key="..."
+    # aws_access_key="..."
+    # aws_region="..."
+    # aws_account_id="..."
+)
+
+# Set some Shiny page options
+ui.page_opts(
+    title="Hello Anthropic Claude Chat",
+    fillable=True,
+    fillable_mobile=True,
+)
+
+# Create and display empty chat
+chat = ui.Chat(id="chat")
+chat.ui()
+
+
+# Define a callback to run when the user submits a message
+@chat.on_user_submit
+async def _():
+    # Get messages currently in the chat
+    messages = chat.messages()
+    # Create a response message stream
+    response = await llm.messages.create(
+        model="anthropic.claude-3-sonnet-20240229-v1:0",
+        messages=messages,
+        stream=True,
+        max_tokens=1000,
+    )
+    # Append the response stream into the chat
+    await chat.append_message_stream(response)

examples/chat/enterprise/azure-openai/app.py

@@ -0,0 +1,55 @@
+# ------------------------------------------------------------------------------------
+# A basic Shiny Chat example powered by OpenAI running on Azure.
+# To run it, you'll need OpenAI API key.
+# To get setup, follow the instructions at https://learn.microsoft.com/en-us/azure/ai-services/openai/quickstart?tabs=command-line%2Cpython-new&pivots=programming-language-python#create-a-new-python-application
+# ------------------------------------------------------------------------------------
+import os
+
+from openai import AzureOpenAI
+
+from shiny.express import ui
+
+# Although you can set API keys here, it's recommended to put it in an .env file
+# and load it with `dotenv` so your keys aren't exposed with your code.
+# from dotenv import load_dotenv
+# _ = load_dotenv()
+llm = AzureOpenAI(
+    api_key=os.getenv("AZURE_OPENAI_API_KEY"),
+    api_version="2024-02-01",
+    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
+)
+
+deployment_name = "REPLACE_WITH_YOUR_DEPLOYMENT_NAME"
+
+# Set some Shiny page options
+ui.page_opts(
+    title="Hello OpenAI Chat",
+    fillable=True,
+    fillable_mobile=True,
+)
+
+# Create a chat instance, with an initial message
+chat = ui.Chat(
+    id="chat",
+    messages=[
+        {"content": "Hello! How can I help you today?", "role": "assistant"},
+    ],
+)
+
+# Display the chat
+chat.ui()
+
+
+# Define a callback to run when the user submits a message
+@chat.on_user_submit
+async def _():
+    # Get messages currently in the chat
+    messages = chat.messages()
+    # Create a response message stream
+    response = await llm.chat.completions.create(
+        model=deployment_name,
+        messages=messages,
+        stream=True,
+    )
+    # Append the response stream into the chat
+    await chat.append_message_stream(response)

examples/chat/hello-providers/anthropic/app.py

@@ -0,0 +1,43 @@
+# ------------------------------------------------------------------------------------
+# A basic Shiny Chat example powered by Anthropic's Claude model.
+# To run it, you'll need an Anthropic API key.
+# To get one, follow the instructions at https://docs.anthropic.com/en/api/getting-started
+# ------------------------------------------------------------------------------------
+import os
+
+from anthropic import AsyncAnthropic
+
+from shiny.express import ui
+
+# Although you can set the API key here, it's recommended to put it in an .env file
+# and load it with `dotenv` so your key isn't exposed with your code.
+# from dotenv import load_dotenv
+# _ = load_dotenv()
+llm = AsyncAnthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
+
+# Set some Shiny page options
+ui.page_opts(
+    title="Hello Anthropic Claude Chat",
+    fillable=True,
+    fillable_mobile=True,
+)
+
+# Create and display empty chat
+chat = ui.Chat(id="chat")
+chat.ui()
+
+
+# Define a callback to run when the user submits a message
+@chat.on_user_submit
+async def _():
+    # Get messages currently in the chat
+    messages = chat.messages()
+    # Create a response message stream
+    response = await llm.messages.create(
+        model="claude-3-opus-20240229",
+        messages=messages,
+        stream=True,
+        max_tokens=1000,
+    )
+    # Append the response stream into the chat
+    await chat.append_message_stream(response)