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| title: deepfake-fastapi | |
| emoji: "🚀" | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: docker | |
| # Model FastAPI | |
| FastAPI service that provides two capabilities: | |
| - Deepfake image detection using a SigLIP image-classification backbone with a LoRA adapter. | |
| - A news-aware chatbot that selectively performs web search via Tavily and responds with verified sources when evidence is found. | |
| ## Project Layout | |
| - main app entry: main.py | |
| - Deepfake detector: app/detector.py, app/core/detector/_, app/services/detector/_, model/output/siglip-lora-optimized/ | |
| - Chatbot: app/chatbot.py, app/core/chatbot/_, app/services/chatbot/_, app/schemas/chat.py | |
| - Database migration (chat history table): supabase/migrations/ | |
| ## Requirements | |
| - Python 3.13+ | |
| - uv package manager (https://docs.astral.sh/uv/) | |
| - Access to the SigLIP base model and the LoRA adapter stored at model/output/siglip-lora-optimized/ | |
| ## Environment Variables | |
| - DATABASE_URL (or SUPABASE_DATABASE_URL): PostgreSQL connection string for chat history (if save_to_db=true). | |
| - GROQ_API_KEY: required by the Groq LLM used for responses. | |
| - OPENROUTER_API_KEY: required by the OpenRouter model used for classification/query rewriting. | |
| - TAVILY_API_KEY: required for Tavily search. | |
| - Optional: set CUDA-visible devices as needed for GPU inference. | |
| ## Setup (local) | |
| ```bash | |
| uv sync | |
| uv run main.py # starts FastAPI on 0.0.0.0:7860 | |
| ``` | |
| The detector will load the SigLIP base model and apply the LoRA adapter from model/output/siglip-lora-optimized/. | |
| ## API | |
| - POST /detect | |
| - Form-data file field: file (image). Returns predicted_class (index), predicted_label (from id2label), prediction (real/fake thresholded at P(real) >= 0.90), confidence, and class probabilities. | |
| - POST /chat | |
| - JSON: {"query": "...", "session_id": "optional", "save_to_db": true|false} | |
| - Auto-classifies need for search, optionally queries Tavily, then responds. Returns response.content, session_id, used_search, and search_reason. | |
| - DELETE /chat/{session_id} | |
| - Clears chat history (both in-memory guest sessions and DB rows). | |
| ### Quick cURL examples | |
| ```bash | |
| # Deepfake detection | |
| curl -X POST "http://localhost:7860/detect" \ | |
| -F "file=@path/to/image.jpg" | |
| # Chat (without forcing search decision) | |
| curl -X POST "http://localhost:7860/chat" \ | |
| -H "Content-Type: application/json" \ | |
| -d '{"query": "Is the latest SpaceX launch successful?", "save_to_db": false}' | |
| ``` | |
| ## Docker | |
| A Dockerfile is provided using the uv base image. | |
| ```bash | |
| docker build -t model-fast-api . | |
| docker run -p 7860:7860 --env-file .env model-fast-api | |
| ``` | |
| The image installs dependencies with uv sync and runs `uv run main.py`. | |
| ## Notes | |
| - The news verification prompt only returns source links when the claim is supported by the retrieved results; otherwise it replies with UNDETERMINED and no links. | |
| - If chat history persistence is disabled (save_to_db=false), sessions are stored in-memory. | |