AgenticChat_LLM

1. Set up Python Virtual Environment (venv):
   • Open your terminal in the directory where you want to create the app files.
   • Create a virtual environment: python -m venv venv
   • Activate the virtual environment:
     • On Linux/macOS: source venv/bin/activate
     • On Windows: venv\Scripts\activate
2. Install Python Packages:
   • Navigate to the directory containing this Python code in your terminal.
   • Run: pip install -r requirements.txt

Explanation and Features:

llm_handler.py: LLMHandler class initializes ChatGoogleGenerativeAI from Langchain. It's designed to be extensible for other LLM providers (Ollama, OpenAI) by modifying _init_llm. generate_response constructs the multimodal input (text and PIL Image objects) for gemini-pro-vision and handles chat history. app.py: Uses gr.Blocks for a custom Gradio UI. gr.Chatbot displays the conversation. gr.Textbox for text input. gr.File for uploading files. It attempts to load uploads as images using PIL. chat_fn is the core Gradio callback: It takes the user's message, uploaded file, and current chat history. If a file is an image, it's loaded into a PIL Image object. Other files are acknowledged. It calls llm_handler_instance.generate_response. It updates the chat history and clears the input fields. The function is an async generator (yield) to allow the UI to update with the user's message immediately before the AI responds. Chat History: Managed using gr.State and passed between the client and server. LLM Swappability: The LLMHandler's constructor can be modified to select different LLMs. The app.py currently defaults to Google, but you could add UI elements (like a dropdown) to change DEFAULT_LLM_PROVIDER and DEFAULT_MODEL_NAME dynamically. Agentic Behavior (Initial): Chat: Handles conversational flow with history. Image: If gemini-pro-vision is used, it can understand images provided alongside text. The llm_handler prepares this input. Voice/Video: Currently, it only acknowledges uploaded audio/video files. True processing would require adding specific tools (e.g., speech-to-text for audio, frame extraction/analysis for video) and integrating them into an agent framework within LLMHandler. This would involve using Langchain Agents and Tools more formally. Next Steps for Enhancement (Agentic Features & LLM Swapping):

Full Agent Implementation:

Modify LLMHandler to initialize a Langchain AgentExecutor. Define tools (e.g., AudioTranscriptionTool, VideoAnalysisTool, WebSearchTool). The agent would then decide which tool to use based on the input. The generate_response method would invoke agent_executor.arun() or agent_executor.ainvoke(). Voice Processing:

Integrate a speech-to-text library (e.g., SpeechRecognition, AssemblyAI, Google Speech-to-Text via API). Create a Langchain Tool for transcription. Modify chat_fn to pass audio file paths to this tool via the agent. Video Processing (Basic):

Use OpenCV (cv2) to extract frames or audio from video. Create a Langchain Tool for this. The agent could then, for example, describe a keyframe or transcribe the audio. Enhanced LLM Swappability:

In app.py, add a Gradio dropdown to select the LLM provider (Google, Ollama, OpenAI) and model. Pass this choice to LLMHandler to initialize the correct LLM. The LLMHandler's generate_response might need to adapt how it prepares input (especially for images) based on the selected LLM's capabilities. For example, if a non-vision model is chosen, it might ignore images or use an external tool to describe the image first. UI Resemblance to Gemini Chat:

Further customize Gradio's CSS or use gr.themes for styling. Adjust layout elements for a closer look and feel.