Add Hugging Face Space configuration

Dockerfile

@@ -1,48 +1,28 @@
 FROM python:3.13-slim
 
-WORKDIR /app
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
 
-# Install git and git-lfs for downloading large files (if needed)
-RUN apt-get update && \
-    apt-get install -y git git-lfs && \
-    apt-get clean && \
-    rm -rf /var/lib/apt/lists/*
+WORKDIR /app
 
 # Copy requirements file
-COPY requirements.txt .
+COPY --chown=user requirements.txt .
 
 # Install dependencies
-RUN pip install --no-cache-dir -r requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
 # Download NLTK data
 RUN python -c "import nltk; nltk.download('punkt'); nltk.download('wordnet'); nltk.download('stopwords')"
 
 # Create necessary directories
 RUN mkdir -p data/users data/sessions data/conversations data/feedback
-RUN mkdir -p mental_health_model_artifacts/chroma_db
 
 # Copy application files
-COPY api_mental_health.py .
-COPY .env.example .env
-COPY create_vector_db.py .
-
-# Copy model artifacts if they exist, or they should be mounted/downloaded at runtime
-# Note: For Hugging Face Spaces deployment, you'll need to use Git LFS or 
-# provide a way to download these models at container startup
-COPY mental_health_model_artifacts/ mental_health_model_artifacts/
-
-# Create a startup script to handle potential model downloading
-RUN echo '#!/bin/bash\n\
-# Check if models exist\n\
-if [ ! -f "mental_health_model_artifacts/crisis_classifier.pkl" ]; then\n\
-    echo "Warning: Model artifacts not found. Please mount them or implement a download method."\n\
-fi\n\
-# Start the API server\n\
-exec uvicorn api_mental_health:app --host 0.0.0.0 --port 7860\n\
-' > /app/start.sh && chmod +x /app/start.sh
+COPY --chown=user . /app
 
 # Expose the port Hugging Face Spaces expects
 EXPOSE 7860
 
-# Use the startup script
-CMD ["/app/start.sh"]
+# Command to run the application
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,13 +1,26 @@
 ---
-title: Thera Guide Ai
-emoji: 🚀
-colorFrom: green
-colorTo: purple
+title: Mental Health Counselor API
+emoji: 🧠
+colorFrom: purple
+colorTo: indigo
 sdk: docker
 pinned: false
-license: mit
-short_description: crisis-detection
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-# This is a test update
+# Mental Health Counselor API
+
+This is a backend API for a mental health counseling application. It provides endpoints for analyzing patient messages, suggesting counselor responses, and managing counseling sessions.
+
+## API Endpoints
+
+- `/`: Root endpoint showing API status
+- `/health`: Health check endpoint
+- And many more specialized endpoints from the full API
+
+## Deployment
+
+This API is deployed on Hugging Face Spaces using Docker.
+
+## Frontend
+
+The frontend for this application is deployed separately on Vercel.

app.py

@@ -0,0 +1,42 @@
+from fastapi import FastAPI
+import os
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Initialize FastAPI app
+app = FastAPI(title="Mental Health Counselor API")
+
+# Create necessary directories
+os.makedirs("data/users", exist_ok=True)
+os.makedirs("data/sessions", exist_ok=True)
+os.makedirs("data/conversations", exist_ok=True)
+os.makedirs("data/feedback", exist_ok=True)
+
+# Define a simple health check route
+@app.get("/health")
+async def health_check():
+    return {"status": "ok", "message": "Mental Health Counselor API is running"}
+
+# Define a simple root route
+@app.get("/")
+async def root():
+    return {
+        "app": "Mental Health Counselor API",
+        "status": "running",
+        "endpoints": [
+            "/health",
+            "/api-docs"
+        ]
+    }
+
+# Import the actual API if the file exists
+try:
+    from api_mental_health import app as full_app
+    # Merge the routes from the full app
+    app.routes.extend(full_app.routes)
+    logger.info("Loaded full API functionality")
+except ImportError:
+    logger.warning("Could not import full API functionality")