https://github.com/xenoswarlocks/video-processing-tool
The project will be divided into three phases for structured development and feature enhancement.
https://github.com/xenoswarlocks/video-processing-tool
Last synced: about 2 months ago
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The project will be divided into three phases for structured development and feature enhancement.
- Host: GitHub
- URL: https://github.com/xenoswarlocks/video-processing-tool
- Owner: XenosWarlocks
- License: apache-2.0
- Created: 2024-12-16T13:49:38.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-03-30T13:29:34.000Z (about 2 months ago)
- Last Synced: 2025-03-30T14:24:14.782Z (about 2 months ago)
- Language: Python
- Size: 859 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# AI-Powered Video Analysis Platform
## Overview
A user-friendly Python script that handles the installation of all required packages for this project. The script features a clean progress tracking interface and automated NLTK data downloads.
## Features
* **Chunked Video Upload:** Efficiently upload large video files in smaller chunks, ensuring smooth processing and minimizing upload failures.
* **AI-Powered Analysis:**
* **Frame Extraction:** Extracts keyframes from videos at configurable intervals.
* **Gemini AI Integration:** Utilizes the Gemini AI model to generate insightful summaries, identify key concepts, and analyze the content of each frame.
* **Sentiment Analysis:** Detects and analyzes the emotional tone of the video content.
* **Interactive Chat UI:** Engage in a conversation with an AI assistant to ask questions about the video analysis results, fostering a deeper understanding of the content.
* **Visually Appealing UI:**
* **Custom Theme:** A modern and cohesive design with a well-defined color palette and typography.
* **Reusable UI Components:** Modular and maintainable UI elements like buttons, cards, and inputs, built using Streamlit and custom CSS.
* **Responsive Design:** Ensures optimal viewing experience across different devices and screen sizes.
* **Payment Integration:** Supports donations through platforms like Buy Me a Coffee and Patreon, allowing users to contribute to the project's development.
## Getting Started
### Prerequisites
* Python 3.8 or higher
* pip package manager
* A Gemini API key (set as the `GEMINI_API_KEY` environment variable)
* Stripe API keys (if using Stripe for payments)
* Buy Me a Coffee/Patreon tokens (if applicable)
## Project structure
The project follows a modular and well-organized structure:
```bash
video-processing-tool/
│
├── src/
│ ├── core/
│ │ ├── __init__.py
│ │ ├── base_processor.py # Base abstract class for processors
│ │ ├── token_counter.py # AI Token counter
│ │ └── config_manager.py # Centralized configuration management
│ │
│ ├── video_processing/
│ │ ├── __init__.py
│ │ ├── video_handler.py # Enhanced video handling
│ │ ├── frame_extractor.py # Advanced frame extraction
│ │ ├── ocr_processor.py # Improved OCR processing
│ │ └── text_analyzer.py # Text analysis and deduplication
│ │
│ ├── api/
│ │ ├── __init__.py
│ │ ├── vid_api.py
│ │ ├── chunk_api.py
│ │
│ ├── ai_integration/
│ │ ├── __init__.py
│ │ ├── gemini_processor.py # Gemini AI integration
│ │ └── text_enrichment.py # AI-powered text enhancement
│ │
│ └── ui/
│ ├── ai_integration/
│ │ ├── inputs.py
│ │ ├── cards.py
│ │ ├── button.py
│ │ └── theme.py
│ ├── __init__.py
│ ├── chat_ui.py
│ └── streamlit.py # Advanced Streamlit UI
│
├── tests/
│ ├── sample/
│ │ ├── __init__.py
│ │ ├── test_vid.mp4
│ ├── test_video_processing.py
│ └── test_ai_integration.py
│
├── configs/
│ ├── app_config.yaml
│ └── ai_config.yaml
│
├── logs/
│ └── app.log
│
├── requirements.txt
├── Dockerfile
├── docker-composer.yml
├── .env.example
├── .env
├── setup.py
└── README.md
```
## Workflow
## How It Works
1. **Video Upload:** Users upload videos through the Streamlit UI. The `VideoChunkUploader` in `vid_upload.py` splits the video into smaller chunks and uploads them to the server.
2. **Chunk Storage:** The `ChunkedUploadManager` in `chunk_api.py` handles the storage of these chunks in the `uploads/chunks` directory.
3. **Video Assembly:** Once all chunks are uploaded, the `VideoUploadManager` in `vid_api.py` assembles them into a complete video file in the `uploads/videos` directory.
4. **Video Processing:** The `VideoProcessor` in `video_handler.py` extracts frames from the video at specified intervals.
5. **AI Analysis:** The `GeminiProcessor` in `gemini_processor.py` analyzes each frame, generating summaries, identifying key concepts, and performing sentiment analysis.
6. **Results Storage:** The `vid_api.py` stores the analysis results in JSON format in the `uploads/processed` directory.
7. **UI Interaction:** The `streamlit.py` file orchestrates the Streamlit UI, allowing users to upload videos, configure settings, view analysis results, and interact with the AI chat assistant.
8. **Chat Interface:** The `chat_ui.py` file provides the chat interface, powered by the `ChatAPI` in `chat_api.py`, which interacts with the Gemini AI model to answer user questions.
9. **Payment Processing:** The `payment_ui.py` and `payment_processor.py` files handle payment integration, allowing users to support the project through donations.
### Installation
1. Clone the repository:
```bash
https://github.com/XenosWarlocks/Video-Processing-Tool.git
cd Video-Processing-Tool
```
2. Create a virtual environment (recommended):
```bash
python3 -m venv venv
source venv/bin/activate # On Linux/macOS
venv\Scripts\activate # On Windows
```
3. Install dependencies:
```bash
python setup.py
```
### Notes
- Make sure your requirements.txt is in the same directory as setup.py
- For development setup, add your development dependencies to requirements.txt
- If you encounter any permission errors, try running your terminal as administrator
- Automatically install required base packages (tqdm, colorama, nltk)
- Install all project dependencies from requirements.txt
- Download necessary NLTK data packages
- Show installation progress with status updates
4. Set up environment variables:
* Create a `.env` file based on the provided `.env.example`.
* Fill in your Gemini API key, Stripe keys, and other relevant tokens.
### Running the Application
1. Navigate to the project root directory:
```bash
cd Video-Processing-Tool
```
2. Activate the virtual environment (if you created one):
```bash
source venv/bin/activate # On Linux/macOS
venv\Scripts\activate # On Windows
```
3. Run the Streamlit app:
```bash
streamlit run src/ui/streamlit.py
```
4. Open your web browser and go to the URL provided by Streamlit (usually `http://localhost:8501`).
## Contributing
Contributions are welcome! Please feel free to submit issues, feature requests, or pull requests.