---
title: veun
app_file: app.py
sdk: gradio
sdk_version: 5.41.0
---
# 🎥 AI Video Chat Assistant with RAG Knowledge System

An intelligent, multi-modal chatbot that can analyze video content, answer your questions, and remember past interactions using a sophisticated Retrieval-Augmented Generation (RAG) system.

![Demo Video](https://place-hold.it/800x450/667eea/ffffff?text=App+Screenshot+Here&fontsize=40)

---

## ✨ Features

-   **Intelligent Video Analysis**: Upload a video, and the assistant will use the Gemini 1.5 Flash model to understand its content.
-   **Multi-Modal Chat**: Ask questions about the uploaded video, and receive detailed, context-aware answers.
-   **Persistent Memory**:
    -   **Short-Term Memory**: Uses **ChromaDB** to remember the conversation history within a single session.
    -   **Long-Term Knowledge**: Uses a **FAISS Vector Store** to create a persistent, searchable knowledge base from all interactions (video analyses, Q&A), enabling cross-session insights.
-   **RAG-Powered Context**: Follow-up questions are enhanced with relevant context retrieved from both the current conversation and the long-term knowledge base.
-   **Interactive UI**: A user-friendly interface built with **Gradio**, featuring distinct sections for video interaction and knowledge base management.
-   **Robust Backend**: Powered by **FastAPI**, providing a scalable and efficient API.
-   **Debugging & Management**: The UI includes tools to directly query the knowledge base, add test data, and view system statistics.

---

## 🏗️ Architecture

The application operates on a decoupled frontend-backend model. The Gradio UI serves as a pure client, making HTTP requests to the FastAPI backend, which houses all the AI logic, data processing, and state management.

The core of the architecture is its **Dual-Memory System**:
1.  **ChromaDB for Conversational Context**: Provides fast, session-specific memory. It answers the question, "What have we been talking about *right now*?".
2.  **FAISS for Enduring Knowledge**: Creates a permanent, long-term knowledge base from key insights. It answers the question, "What has the assistant learned from *all past interactions*?".

### System Flow Diagram

This diagram illustrates how a user request flows through the system, interacting with the dual-memory stores and the Gemini AI model.

![System Flow Diagram](https://raw.githubusercontent.com/HEMANT2027/Vuen_Code_Hackathon/fe52de20f0e4cbd6f90ce446018ed9631a1d6f90/Model_Architecture.png)

## ⚙️ Setup and Installation

Follow these steps to get the application running on your local machine.

### 1. Prerequisites
-   Python 3.8 or higher
-   Git

### 2. Clone the Repository
```bash
git clone [https://github.com/your-username/ai-video-chat-assistant.git](https://github.com/your-username/ai-video-chat-assistant.git)
cd ai-video-chat-assistant
```

### 3. Create a Virtual Environment
It's highly recommended to use a virtual environment.
```bash
# For Windows
python -m venv venv
venv\Scripts\activate

# For macOS/Linux
python3 -m venv venv
source venv/bin/activate
```

### 4. Install Dependencies
Install all the required packages from the `requirements.txt` file.
```bash
pip install -r requirements.txt
```

### 5. Set Up Environment Variables
Create a file named `.env` in the root directory of the project. This file will hold your Gemini API key.

```
# .env
GEMINI_API_KEY="YOUR_GEMINI_API_KEY_HERE"
```

---

## 🚀 Running the Application

Once the setup is complete, you can start the application with a single command:

```bash
python app/main.py
```

You will see output indicating that the FastAPI backend and Gradio frontend are running:

```
🚀 Starting AI Video Chat Assistant...
📊 FastAPI Backend: [http://127.0.0.1:8000](http://127.0.0.1:8000)
🎨 Gradio Frontend: [http://127.0.0.1:7860](http://127.0.0.1:7860)
🧠 RAG System: with RAG Integration
======================================================================
```

-   Open your web browser and navigate to **`http://127.0.0.1:7860`** to use the application.

---

## 📖 How to Use

1.  **Upload a Video**: Click the "🎬 Click to Upload Video" button and select a video file.
2.  **Ask a Question**: Once the video is uploaded, the text box will become active. Type a question about the video (e.g., "What is happening in this video?") and press Enter or click "Send 🚀".
3.  **Chat**: The AI will respond. You can ask follow-up questions. The system will remember the context of the current conversation.
4.  **Explore the Knowledge Base**:
    -   Click the **"🧠 Knowledge Base"** tab.
    -   **Search**: Type a query into the search box to find relevant information from all past interactions.
    -   **Debug**: Use the **"🎯 Add Test Data"** button to populate the RAG store for testing and **"📊 Show Stats"** to see its current state.
5.  **Start a New Conversation**: Click the **"🔄 New Conversation"** button to clear the current state and start fresh with a new session ID.

---

## 📡 API Endpoints

The FastAPI backend exposes several endpoints. You can test them at `http://127.0.0.1:8000/docs`.

| Method | Endpoint               | Description                                                                 |
|--------|------------------------|-----------------------------------------------------------------------------|
| `POST` | `/chat/video`          | Main endpoint to chat with a video. Requires a video file and a prompt.     |
| `POST` | `/chat/text`           | Handles text-only follow-up questions.                                      |
| `POST` | `/rag/query`           | Directly queries the FAISS RAG knowledge base.                              |
| `GET`  | `/rag/stats`           | Retrieves comprehensive statistics about the RAG system.                    |
| `POST` | `/rag/debug`           | Adds pre-defined test data to the RAG vector store.                         |
| `POST` | `/rag/reinitialize`    | Forces a re-initialization of the RAG vector store.                         |
| `GET`  | `/health`              | A simple health check endpoint to verify system status.                     |

---

## 🧪 Debugging and Testing

The project includes standalone scripts for testing the RAG system independently.

-   **`scripts/debug_rag.py`**: A comprehensive test suite that checks imports, adds data, runs test queries, and offers an interactive query mode. Run it with `python scripts/debug_rag.py`.
-   **`scripts/query_rag.py`**: A simple script to perform a similarity search on the RAG vector store. Modify the `query` variable inside the script and run with `python scripts/query_rag.py`.