https://github.com/monarch1108/advanced-semantic-search-engine
An advanced subtitle search engine using Whisper, ChromaDB, and NLP for semantic search, filtering, and visualizations with audio transcription.
https://github.com/monarch1108/advanced-semantic-search-engine
Last synced: about 1 year ago
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An advanced subtitle search engine using Whisper, ChromaDB, and NLP for semantic search, filtering, and visualizations with audio transcription.
- Host: GitHub
- URL: https://github.com/monarch1108/advanced-semantic-search-engine
- Owner: MONARCH1108
- Created: 2025-03-27T13:19:05.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-23T12:43:32.000Z (about 1 year ago)
- Last Synced: 2025-05-23T14:00:33.839Z (about 1 year ago)
- Language: Python
- Size: 479 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🎬 Movie Subtitle Search Engine
An advanced semantic search engine that identifies movies based on memorable quotes and dialogue lines using Natural Language Processing and vector embeddings.
## Documentation :
https://medium.com/@abhayemani8/building-a-movie-subtitle-search-engine-from-ai-assisted-to-self-coded-development-8a789407471d
## 🌟 Features
- **Semantic Search**: Find movies using natural language queries, not just exact matches
- **Multiple Versions**: Two complete implementations (Streamlit & Flask)
- **Vector Embeddings**: Uses sentence transformers for accurate semantic matching
- **Real-time Search**: Fast similarity search with confidence scoring
- **Interactive UI**: Clean, modern web interface
- **Audio Support**: Convert speech to text for voice-based searches (Version 1)
- **Extensible**: Easy to add new movie subtitles and expand the database
## 🚀 Demo
Simply enter a movie quote like:
- _"I can do this all day"_ → Captain America
- _"May the force be with you"_ → Star Wars
- _"I'll be back"_ → Terminator
## 📁 Project Structure
```
subtitle-search-engine/
├── version_1/ # Streamlit Implementation (AI-Assisted)
│ ├──AI_SEO.py # Advanced subtitle search engine
│ └── requirements.txt
├── version_2/ # Flask Implementation (Self-Coded)
│ ├── app.py # Flask web application
│ ├── templates/
│ │ └── index.html # Frontend interface
│ ├── subtitles/ # Movie subtitle files (.txt)
│ ├── chroma_subtitles/ # Vector database storage
│ └── app.ipynb # Jupyter notebook demo
└── README.md
```
## 🛠️ Technologies Used
### Core Technologies
- **Python 3.8+**
- **ChromaDB** - Vector database for embeddings
- **Sentence Transformers** - Semantic text embeddings
- **Langchain** - Document processing and retrieval
### Version 1 (Streamlit)
- **Streamlit** - Web interface
- **Whisper** - Speech-to-text conversion
- **SQLite** - Local database
- **Matplotlib/Seaborn** - Data visualization
### Version 2 (Flask)
- **Flask** - Web framework
- **HTML/CSS/JavaScript** - Frontend
- **HuggingFace Transformers** - NLP models
## ⚡ Quick Start
### Version 2 (Flask - Recommended)
1. **Clone the repository**
bash
```bash
git clone https://github.com/MONARCH1108/Advanced-Semantic-Search-Engine
cd subtitle-search-engine/version_2
```
2. **Install dependencies**
bash
```bash
pip install flask langchain-chroma langchain-huggingface chromadb sentence-transformers
```
3. **Prepare subtitle data**
- Create a `subtitles/` directory
- Add movie subtitle files as `.txt` files
- Or use the provided Marvel dataset
4. **Run the application**
bash
```bash
python app.py
```
5. **Open your browser**
```
http://localhost:5000
```
### Version 1 (Streamlit)
1. **Navigate to version 1**
bash
```bash
cd version_1
```
2. **Install dependencies**
bash
```bash
pip install -r requirements.txt
```
3. **Run the application**
bash
```bash
python main.py
```
## 📊 Dataset
The project uses movie subtitle files in plain text format. You can:
1. **Use the Marvel Cinematic Universe dataset** (demonstrated in `app.ipynb`)
2. **Add your own subtitle files** to the `subtitles/` directory
3. **Download from subtitle websites** like OpenSubtitles
### Supported Formats
- `.txt` files with UTF-8, Latin-1, or CP1252 encoding
- One subtitle file per movie
- Automatic text chunking for better search performance
## 🔍 How It Works
### 1. Text Processing
- Subtitle files are read and processed with multiple encoding fallbacks
- Text is split into meaningful chunks using Langchain's text splitter
- Each chunk maintains metadata about its source movie
### 2. Vector Embeddings
- Uses `sentence-transformers/paraphrase-MiniLM-L6-v2` model
- Converts text chunks into high-dimensional vectors
- Stores embeddings in ChromaDB for fast similarity search
### 3. Semantic Search
- User queries are converted to embeddings
- Cosine similarity calculated against stored vectors
- Results ranked by confidence score
### 4. Web Interface
- Real-time search with loading states
- Confidence scoring for match quality
- Responsive design for mobile and desktop
## 🎯 API Endpoints (Version 2)
|Endpoint|Method|Description|
|---|---|---|
|`/`|GET|Main search interface|
|`/search`|POST|Perform subtitle search|
|`/health`|GET|System health check|
### Search API Example
javascript
```javascript
POST /search
{
"query": "I can do this all day",
"top_k": 5
}
```
Response:
javascript
```javascript
{
"results": [
{
"movie": "Captain.America.The.First.Avenger",
"score": 0.1234,
"matched_text": "I can do this all day...",
"confidence": 87.7
}
]
}
```
## 🔧 Configuration
### Embedding Model
Change the embedding model in the code:
python
```python
embedding_function = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2" # Alternative model
)
```
### Search Parameters
- `top_k`: Number of results to return (default: 5)
- `chunk_size`: Text chunk size for processing (default: 500)
- `chunk_overlap`: Overlap between chunks (default: 50)
## 📈 Performance
- **Search Speed**: ~100-500ms per query
- **Memory Usage**: ~200MB for 50 movies
- **Accuracy**: 85-95% for exact quotes, 70-85% for paraphrased queries
- **Scalability**: Handles 1000+ movies efficiently
## 🤖 Development Journey
### Version 1: AI-Assisted Development
- **Approach**: Heavily relied on ChatGPT and AI tools
- **Features**: Advanced analytics, audio processing, comprehensive search
- **Learning**: Understanding AI capabilities and limitations
### Version 2: Self-Coded Implementation
- **Approach**: Minimal AI assistance, focused on core functionality
- **Features**: Clean architecture, efficient search, modern UI
- **Learning**: Deep understanding of semantic search principles
## 🚀 Future Enhancements
- [ ] **Multi-language Support** - Support for non-English subtitles
- [ ] **Advanced Filtering** - Filter by genre, year, rating
- [ ] **User Accounts** - Save favorite searches and movies
- [ ] **Batch Processing** - Upload multiple subtitle files
- [ ] **REST API** - Full API for integration with other apps
- [ ] **Docker Support** - Containerized deployment
- [ ] **Cloud Deployment** - Deploy on AWS/GCP/Azure
## 🤝 Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request