README.md

metadata

title: PsyPredict [Backend]
emoji: 🧠
colorFrom: indigo
colorTo: purple
sdk: docker
pinned: false

PsyPredict - Backend

FastAPI backend for PsyPredict — production-grade multimodal clinical AI system.

What Runs Here

Service	Technology
API Framework	FastAPI + Uvicorn
LLM Inference	Ollama / Llama3 (local)
Text Emotion	DistilBERT (bhadresh-savani/distilbert-base-uncased-emotion)
Crisis Detection	Zero-shot NLI (MiniLM)
Face Emotion	Keras CNN (custom trained, emotion_model_trained.h5)
Remedies	CSV lookup (MEDICATION.csv)

Endpoints

Method	Path	Description
POST	/api/chat	Main therapist — returns PsychReport
POST	/api/predict/emotion	Facial emotion detection
GET	/api/get_advice	Remedy/condition lookup
POST	/api/analyze/text	Text emotion + crisis score
GET	/api/health	System health check

Running Locally

# 1. Install Ollama + LLaMA 3 (one-time)
winget install Ollama.Ollama
ollama pull llama3

# 2. Install dependencies
pip install -r requirements.txt

# 3. Start server
uvicorn app.main:app --host 0.0.0.0 --port 7860 --reload

Swagger docs: http://localhost:7860/docs

Key Files

app/
├── main.py                   # FastAPI app factory
├── config.py                 # Pydantic Settings
├── schemas.py                # All request/response models (PsychReport etc.)
├── services/
│   ├── ollama_engine.py      # LLaMA 3 async client
│   ├── text_emotion_engine.py# DistilBERT classifier
│   ├── crisis_engine.py      # Zero-shot NLI crisis detection
│   ├── fusion_engine.py      # Multimodal weighted fusion
│   ├── emotion_engine.py     # Keras CNN face emotion (preserved)
│   └── remedy_engine.py      # CSV remedy lookup (preserved)
└── api/endpoints/
    ├── therapist.py          # POST /api/chat
    ├── facial.py             # POST /api/predict/emotion
    ├── remedies.py           # GET  /api/get_advice
    └── analysis.py           # POST /api/analyze/text + GET /api/health