3.0.3 | Homepage information update + set_llm refactor

.gitignore

@@ -1,4 +1,4 @@
-.streamlit
+.streamlit/secrets.toml
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

.streamlit/config.toml

@@ -0,0 +1,74 @@
+[logger]
+level = "info"
+# String format for logging messages. If logger.datetimeFormat is set,
+# logger messages will default to `%(asctime)s.%(msecs)03d %(message)s`. See
+# Python's documentation for available attributes:
+# https://docs.python.org/2.6/library/logging.html#formatter-objects
+# Default: "%(asctime)s %(message)s"
+messageFormat = "%(asctime)s %(message)s"
+
+[client]
+# Controls whether uncaught app exceptions and deprecation warnings
+# are displayed in the browser. By default, this is set to True and
+# Streamlit displays app exceptions and associated tracebacks, and
+# deprecation warnings, in the browser.
+#
+# If set to False, deprecation warnings and full exception messages
+# will print to the console only. Exceptions will still display in the
+# browser with a generic error message. For now, the exception type and
+# traceback show in the browser also, but they will be removed in the
+# future.
+# Default: true
+showErrorDetails = false
+
+# Controls whether the default sidebar page navigation in a multipage app is
+# displayed.
+# Default: true
+showSidebarNavigation = true
+
+[runner]
+
+# Allows you to type a variable or string by itself in a single line of
+# Python code to write it to the app.
+# Default: true
+magicEnabled = false
+
+[server]
+
+# List of folders that should not be watched for changes. This
+# impacts both "Run on Save" and @st.cache.
+# Relative paths will be taken as relative to the current working directory.
+# Example: ['/home/user1/env', 'relative/path/to/folder']
+# Default: []
+folderWatchBlacklist = ['.streamlit/']
+
+# Automatically rerun script when the file is modified on disk.
+# Default: false
+runOnSave = true
+
+[browser]
+# Whether to send usage statistics to Streamlit.
+# Default: true
+gatherUsageStats = true
+
+[theme]
+
+# The preset Streamlit theme that your custom theme inherits from.
+# One of "light" or "dark".
+# base =
+
+# Primary accent color for interactive elements.
+# primaryColor =
+
+# Background color for the main content area.
+# backgroundColor =
+
+# Background color used for the sidebar and most interactive widgets.
+# secondaryBackgroundColor =
+
+# Color used for almost all text.
+# textColor =
+
+# Font family for all text in the app, except code blocks. One of "sans serif",
+# "serif", or "monospace".
+# font =

README.md

@@ -147,4 +147,5 @@ A conglomerate privacy policy governing FreeStream is planned. For the time bein
 
 - [OpenAI Privacy Policy](https://openai.com/policies/privacy-policy)
 - [Google AI Studio](https://transparency.google/our-policies/privacy-policy-terms-of-service/)
-  - I couldn't find a privacy policy specific to Google AI Studio.
+  - I couldn't find a privacy policy specific to Google AI Studio.
+- [Streamlit](https://streamlit.io/privacy-policy/)

freestream/pages/1_🤖_RAGbot.py

@@ -52,7 +52,6 @@
         temperature=0.7,  # Set the temperature for the model's responses
         streaming=True,  # Enable streaming responses for the model
     ),
-
     "Gemini-Pro": ChatGoogleGenerativeAI(
         model="gemini-pro",
         google_api_key=st.secrets.GOOGLE.google_api_key,
@@ -62,15 +61,17 @@
         convert_system_message_to_human=True,
         max_output_tokens=512,
         max_retries=1,
-    )
+    ),
 }
 
 # Create a dropdown menu for selecting a chat model
 selected_model = st.selectbox(
     label="Choose your chat model:",  # Set the label for the dropdown menu
     options=list(model_names.keys()),  # Set the available model options
     key="model_selector",  # Set a unique key for the dropdown menu
-    on_change=lambda: set_llm(st.session_state.model_selector, model_names),  # Set the callback function
+    on_change=lambda: set_llm(
+        st.session_state.model_selector, model_names
+    ),  # Set the callback function
 )
 
 # Load the selected model dynamically

freestream/pages/utils/utility_funcs.py

@@ -173,42 +173,23 @@ def on_retriever_end(self, documents, **kwargs):
         self.status.update(state="complete")
 
 
-# Define a callback function for when a model is selected
+# Define a callback function for selecting a model
 def set_llm(selected_model: str, model_names: dict):
     """
-    Updates the large language model (LLM) in the session state based on the user's selection.
+    Sets the large language model (LLM) in the session state based on the user's selection.
     Also, displays an alert based on the selected model.
-
-    Parameters:
-    - None
-
-    Returns:
-    - None
-
-    This function has the following side effects:
-    1. It updates the `llm` key in the `st.session_state` dictionary with the selected model.
-    2. It displays a warning message when the user switches to the "ChatOpenAI GPT-3.5 Turbo" model.
-    3. It displays a failure warning message when the user fails to change the model (e.g., due to unsupported models).
     """
     try:
         # Set the model in session state
         st.session_state.llm = model_names[selected_model]
-        
+
         # Show an alert based on what model was selected
-        if selected_model == "GPT-3.5 Turbo":
-            st.success(body="Switched to GPT-3.5 Turbo!", icon="⚠️")
-        elif selected_model == "Gemini-Pro":
-            st.success(body="Switched to Gemini-Pro!", icon="⚠️")
-
-        # Add more if statements for each added model
-        # if st.session_state.model_selector == model_names["GPT-4"]:
-        #     ...
-
-        else:
-            # This should not happen if all models are covered above
-            raise ValueError(f"Unsupported model selected: {selected_model}")
+        st.success(body=f"Switched to {selected_model}!", icon="✅")
+
     except Exception as e:
         # Log the detailed error message
-        logging.error(f"Error changing model: {e}")
+        logging.error(
+            f"Unsupported model selected or Error changing model: {e}\n{selected_model}"
+        )
         # Display a more informative error message to the user
-        st.error(f"Failed to change model! Error: {e}")
+        st.error(f"Failed to change model! Error: {e}\n{selected_model}")

freestream/🏡_Home.py

@@ -1,31 +1,31 @@
 import streamlit as st
 
-st.set_page_config(
-    page_title="FreeStream: Unlimited Access to AI Tools",
-    page_icon="🏡"
-)
+st.set_page_config(page_title="FreeStream: Unlimited Access to AI Tools", page_icon="🏡")
 
 st.title("FreeStream")
 st.header(":green[_Unlimited Access to AI Tools_]", divider="red")
 # Project Overview
-st.subheader("What is FreeStream?")
+st.subheader(":blue[What is FreeStream?]")
+
 st.write(
     """
-    FreeStream is a project I'm working on to make AI tools more accessible. It's not just about having a chatbot; it's about exploring how AI can help us in our daily lives, today and in the future. Here's what you can do with FreeStream:
-
-    *   **Explore AI Tools:** Dive into a wide range of AI tools, from chatbots to document analysis, to discover what's possible.
-    *   **Educate Yourself:** Engage in interactive experiences designed to deepen your understanding of AI and its capabilities.
-    *   **Solve Real-World Problems:** Utilize AI tools to simplify and enhance your daily tasks, showcasing the power of AI in action.
+    AI tools often seem complex or even intimidating, but FreeStream aims to change that. This project is about making AI accessible and understandable, showing how it can solve real-world problems in your daily life.
     """
 )
-
+st.divider()
 st.subheader("What tools are currently available?")
 st.write(
     """
-    *   **RAGbot**: An AI chatbot designed to answer questions about documents.
+
+    ### :blue[RAGbot]:
+
+    :orange[*FreeStream's RAGbot can answer your questions directly from the documents you provide.*]
+
+    It works by allowing you to upload PDFs, Word documents, or plain text files, and then ask specific questions based on the information in your documents. The RAGbot uses a method called Corrective Retrieval Augmented Generation (CRAG), which involves retrieving documents, grading them for relevance, and generating answers if at least one document is relevant. If all documents are ambiguous or incorrect, it retrieves from an external source and uses that as context for answer generation. This process ensures a neat workflow where retrieval is done similarly to basic RAG, but with an added step of reasoning about the documents to ensure accurate and helpful responses.
     """
 )
 
+
 st.markdown(
     """
     #### References
@@ -40,7 +40,7 @@
 
 # Create a footer using community suggestion:
 # https://discuss.streamlit.io/t/streamlit-footer/12181
-footer="""<style>
+footer = """<style>
 a:link , a:visited{
     color: #ffffff; /* White */
     background-color: transparent;
@@ -72,4 +72,4 @@
 <p>Developed with ❤ by <a href="https://www.linkedin.com/in/daemon-carino/" target="_blank">Daethyra</a></p>
 </div>
 """
-st.markdown(footer,unsafe_allow_html=True)
+st.markdown(footer, unsafe_allow_html=True)

pyproject.toml

@@ -1,7 +1,7 @@
 [tool.poetry]
 
 name = "FreeStream"
-version = "3.0.2"
+version = "3.0.3"
 description = "An effort to give easy access to generative AI."
 authors = ["Daethyra <109057945+Daethyra@users.noreply.github.com>"]
 readme = "README.md"