|
|
---
|
|
|
title: Semantic Similarity Explainer
|
|
|
emoji: 🚀
|
|
|
colorFrom: blue
|
|
|
colorTo: red
|
|
|
sdk: streamlit
|
|
|
sdk_version: 1.28.0
|
|
|
app_file: app.py
|
|
|
pinned: false
|
|
|
license: mit
|
|
|
thumbnail: >-
|
|
|
https://cdn-uploads.huggingface.co/production/uploads/688dfa71393005de0077ca78/EUX1LCDAD4tnusOBUEqz4.jpeg
|
|
|
short_description: See how AI understands language with live examples.
|
|
|
---
|
|
|
|
|
|
# 🔍 Semantic Similarity Explainer with AI |
|
|
|
|
|
This Streamlit app calculates the **semantic similarity** between two sentences using transformer-based embeddings (all-MiniLM-L6-v2) and uses AI to explain why that specific score makes sense. |
|
|
|
|
|
## Features |
|
|
|
|
|
- **Semantic Similarity Calculation**: Uses the `all-MiniLM-L6-v2` transformer model to generate 384-dimensional embeddings |
|
|
- **AI-Powered Explanations**: Leverages OpenRouter API to explain similarity scores in detail |
|
|
- **Technical Details**: Shows embedding dimensions, cosine similarity calculations, and vector statistics |
|
|
- **Interactive Interface**: Easy-to-use Streamlit interface with real-time calculations |
|
|
- **History Tracking**: Keeps track of previous calculations during the session |
|
|
|
|
|
## How It Works |
|
|
|
|
|
1. **Input**: Enter two sentences you want to compare |
|
|
2. **Embedding Generation**: The app uses the `all-MiniLM-L6-v2` model to convert sentences into dense vector representations |
|
|
3. **Similarity Calculation**: Computes cosine similarity between the embeddings |
|
|
4. **AI Explanation**: Uses GPT-3.5-turbo via OpenRouter to explain the similarity score |
|
|
5. **Technical Details**: Shows detailed information about the embeddings and calculations |
|
|
|
|
|
## Usage |
|
|
|
|
|
### Without API Key |
|
|
- You can use the similarity calculation feature without an API key |
|
|
- The app will show similarity scores and technical details |
|
|
- AI explanations will be skipped |
|
|
|
|
|
### With OpenRouter API Key |
|
|
- Get your API key from [OpenRouter](https://openrouter.ai/keys) |
|
|
- Enter it in the sidebar to enable AI explanations |
|
|
- The app will provide detailed explanations of why sentences have specific similarity scores |
|
|
|
|
|
## Examples |
|
|
|
|
|
- **High Similarity**: "The car is fast" vs "The automobile is quick" → ~0.90 |
|
|
- **Moderate Similarity**: "I love dogs" vs "I hate dogs" → ~0.60 (same topic, opposite sentiment) |
|
|
- **Low Similarity**: "The cat sat on the mat" vs "Python is a programming language" → ~0.10 |
|
|
|
|
|
## Technical Details |
|
|
|
|
|
- **Model**: sentence-transformers/all-MiniLM-L6-v2 |
|
|
- **Embedding Dimensions**: 384 |
|
|
- **Similarity Metric**: Cosine similarity |
|
|
- **AI Model**: GPT-3.5-turbo (via OpenRouter) |
|
|
|
|
|
## Installation & Local Development |
|
|
|
|
|
```bash |
|
|
# Clone the repository |
|
|
git clone https://github.com/Umer-K/semantic-similarity-explainer.git |
|
|
cd semantic-similarity-explainer |
|
|
|
|
|
|
|
|
# Install dependencies |
|
|
pip install -r requirements.txt |
|
|
|
|
|
# Run the app |
|
|
streamlit run app.py |
|
|
``` |
|
|
|
|
|
## Environment Variables |
|
|
|
|
|
- `OPENROUTER_API_KEY`: Your OpenRouter API key (optional, can also be entered in the UI) |
|
|
|
|
|
## License |
|
|
|
|
|
MIT License - see LICENSE file for details. |
