Update app.py

app.py

@@ -107,89 +107,8 @@ with tabs[2]:
     
 # Tab 4: Developers
 with tabs[3]:
-         
-    # Developer-related content
-    st.markdown(''' 
-
-        # Hugging Face Agent and Tools Code Overview
-        
-        ## Overview
-        The provided Python code implements an interactive Streamlit web application that allows users to interact with various tools through the Hugging Face API. The app integrates Hugging Face models and tools, enabling users to perform tasks such as text generation, sentiment analysis, and more.
-        
-        ## Imports
-        The code imports several external libraries and modules, including:
-        - `streamlit`: For building the web application.
-        - `os`: For interacting with the operating system.
-        - `base64`, `io`, `Image` (from `PIL`), `AudioSegment` (from `pydub`), `IPython`, `sf`: For handling images and audio.
-        - `requests`: For making HTTP requests.
-        - `pandas`: For working with DataFrames.
-        - `matplotlib.figure`, `numpy`: For visualization.
-        - `altair`, `Plot` (from `bokeh.models`), `px` (from `plotly.express`), `pdk` (from `pydeck`): For different charting libraries.
-        - `time`: For handling time-related operations.
-        - `transformers`: For loading tools and agents.
-        
-        ## ToolLoader Class
-        The `ToolLoader` class is responsible for loading tools based on their names. It has methods to load tools from a list of tool names and handles potential errors during loading.
-        
-        ## CustomHfAgent Class
-        The `CustomHfAgent` class extends the base `Agent` class from the `transformers` module. It is designed to interact with a remote inference API and includes methods for generating text based on a given prompt.
-        
-        ## Tool Loading and Customization
-        - Tool names are defined in the `tool_names` list.
-        - The `ToolLoader` instance (`tool_loader`) loads tools based on the provided names.
-        - The `CustomHfAgent` instance (`agent`) is created with a specified URL endpoint, token, and additional tools.
-        - New tools can be added by appending their names to the `tool_names` list.
-        
-        ## Streamlit App
-        The Streamlit app is structured as follows:
-        1. Tool selection dropdown for choosing the inference URL.
-        2. An expander for displaying tool descriptions.
-        3. An expander for selecting tools.
-        4. Examples and instructions for the user.
-        5. A chat interface for user interactions.
-        6. Handling of user inputs, tool selection, and agent responses.
-        
-        ## Handling of Responses
-        The code handles various types of responses from the agent, including images, audio, text, DataFrames, and charts. The responses are displayed in the Streamlit app based on their types.
-        
-        ## How to Run
-        1. Install required dependencies with `pip install -r requirements.txt`.
-        2. Run the app with `streamlit run <filename.py>`.
-        
-        ## Notes
-        - The code emphasizes customization and extensibility, allowing developers to easily add new tools and interact with the Hugging Face API.
-        - Ensure proper configuration, such as setting the Hugging Face token as an environment variable.
-
-      ''')
-
-
-        # Display logs in the frontend
-    logs_expander = st.expander("Logs")
-    with logs_expander:
-        log_output = st.empty()
-    
-        # Custom logging handler to append log messages to the chat
-        class ChatHandler(logging.Handler):
-            def __init__(self):
-                super().__init__()
-    
-            def emit(self, record):
-                log_message = self.format(record)
-                with st.chat_message("ai"):
-                    st.markdown(f"Log: {log_message}")
-    
-        # Add the custom handler to the transformers_logger
-        chat_handler = ChatHandler()
-        transformers_logger.addHandler(chat_handler)
-    
-        # Function to update logs in the frontend
-        def update_logs():
-            log_output.code("")  # Clear previous logs
-            # Do nothing here since logs are appended to the chat
-    
-        # Update logs when the button is clicked
-        if st.button("Update Logs"):
-            update_logs()
+    app_dev_desc()     
+   
     
     
 # Chat code (user input, agent responses, etc.)