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| title: ML Use Cases RAG Assistant (BYOK) | |
| emoji: 🧠 | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 5.44.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # ML/AI Use Cases RAG Assistant (Bring Your Own Key) | |
| An AI-powered assistant that provides business advice based on real ML/AI implementations from 60+ companies with 400+ use cases. This app uses Retrieval-Augmented Generation (RAG) to find relevant company examples and provides actionable recommendations. | |
| **🔑 Bring Your Own Key:** This version requires users to provide their own HuggingFace API key, ensuring zero cost to the space owner while maintaining full functionality. | |
| ## Features | |
| - **🔑 BYOK (Bring Your Own Key)**: Use your own HuggingFace API key for secure, cost-effective access | |
| - **🔍 Semantic Search**: Find relevant ML/AI use cases from a comprehensive database | |
| - **🤖 AI-Powered Advice**: Get personalized recommendations using HuggingFace Inference API | |
| - **📊 Model Recommendations**: Discover fine-tuned and foundation models for your specific use case | |
| - **🏢 Real Company Examples**: Learn from actual implementations across various industries | |
| - **🔒 Privacy-First**: Only embeddings are used - no raw company data is exposed | |
| - **💰 Zero Cost to Owner**: No API costs for the space owner - users bring their own keys | |
| ## How It Works | |
| 1. **🔑 API Key Setup**: Provide your HuggingFace API key for secure access | |
| 2. **📝 Query Processing**: Your business problem is analyzed and converted to embeddings | |
| 3. **🔍 Semantic Search**: The system searches through 400+ pre-processed ML use cases | |
| 4. **📚 Context Building**: Relevant company examples are selected as context | |
| 5. **🤖 AI Generation**: Your API key powers the language model to generate tailored advice | |
| 6. **📊 Model Matching**: HuggingFace API provides relevant model recommendations using your key | |
| ## Technology Stack | |
| - **Backend**: FastAPI with async support and BYOK architecture | |
| - **Vector Database**: ChromaDB for semantic search | |
| - **Embeddings**: Sentence Transformers (all-MiniLM-L6-v2) | |
| - **Language Model**: HuggingFace Inference API (Gemma 2 2B with fallbacks) | |
| - **Frontend**: Modern HTML/CSS/JavaScript with Tailwind CSS | |
| - **Security**: User API keys never stored, used only for requests | |
| ## Security & Privacy | |
| - **🔐 API Key Security**: Your API key is never stored permanently, only used for requests | |
| - **📊 No Raw Data**: Only vector embeddings and metadata are stored | |
| - **🏢 Company Privacy**: Original datasets remain private | |
| - **🛡️ Secure Processing**: All processing happens within the secure HuggingFace environment | |
| - **💾 Local Storage**: API keys stored locally in your browser for convenience | |
| ## Getting Started | |
| ### 1. Get Your HuggingFace API Key | |
| 1. Visit [HuggingFace Settings](https://huggingface.co/settings/tokens) | |
| 2. Click "Create new token" | |
| 3. Select "Read" access (sufficient for this app) | |
| 4. Copy your token (starts with `hf_`) | |
| ### 2. Use the Assistant | |
| 1. Enter your API key in the secure input field | |
| 2. Describe your business problem in natural language: | |
| - "I want to reduce customer churn in my SaaS business" | |
| - "How can I implement fraud detection for my e-commerce platform" | |
| - "What ML approach works best for demand forecasting in retail" | |
| ### 3. Get AI-Powered Results | |
| - **Solution Approach**: Detailed technical recommendations | |
| - **Company Examples**: Real implementations from similar businesses | |
| - **Model Recommendations**: Specific HuggingFace models for your use case | |
| ## Model Information | |
| This space uses pre-computed ChromaDB embeddings generated from a curated dataset of ML/AI use cases. The language model runs efficiently on CPU with fallback options for reliability. | |
| ## Requirements & Limitations | |
| ### Requirements | |
| - Valid HuggingFace API key (free to obtain) | |
| - Internet connection for API calls | |
| ### Limitations | |
| - Responses are generated based on training data patterns | |
| - Model recommendations are sourced from HuggingFace Hub API | |
| - Processing time may vary based on query complexity and API response times | |
| - API rate limits apply based on your HuggingFace account tier | |
| --- | |
| *Built with ❤️ using HuggingFace Spaces* |