fix: HF Spaces deployment

Dockerfile

@@ -1,9 +1,8 @@
 # Multi-stage Dockerfile for Hugging Face Spaces
-# This combines both frontend and backend into a single container
+# Combines frontend and backend into a single container
 
 # Stage 1: Build frontend
 FROM node:20-alpine AS frontend-builder
-
 WORKDIR /app/frontend
 COPY frontend/package*.json ./
 RUN npm ci
@@ -16,11 +15,15 @@ FROM python:3.11-slim
 # Install system dependencies
 RUN apt-get update && apt-get install -y \
     gcc \
+    g++ \
     nginx \
     supervisor \
     curl \
     && rm -rf /var/lib/apt/lists/*
 
+# Create non-root user for HF Spaces
+RUN useradd -m -u 1000 user
+
 # Set working directory
 WORKDIR /app
 
@@ -34,7 +37,7 @@ COPY backend/ ./backend/
 # Copy built frontend to nginx directory
 COPY --from=frontend-builder /app/frontend/dist /usr/share/nginx/html
 
-# Create nginx configuration for Hugging Face Spaces
+# Create nginx configuration
 RUN echo 'server { \n\
     listen 7860; \n\
     server_name localhost; \n\
@@ -53,12 +56,18 @@ RUN echo 'server { \n\
         proxy_set_header X-Real-IP $remote_addr; \n\
         proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; \n\
         proxy_set_header X-Forwarded-Proto $scheme; \n\
+        proxy_read_timeout 300s; \n\
+        proxy_connect_timeout 300s; \n\
     } \n\
-}' > /etc/nginx/sites-available/default
+}' > /etc/nginx/conf.d/default.conf
+
+# Remove default nginx config that listens on port 80
+RUN rm -f /etc/nginx/sites-enabled/default 2>/dev/null || true
 
 # Create supervisor configuration
 RUN echo '[supervisord] \n\
 nodaemon=true \n\
+user=root \n\
 \n\
 [program:backend] \n\
 command=python -m uvicorn backend.app.main:app --host 0.0.0.0 --port 8000 \n\
@@ -77,11 +86,18 @@ redirect_stderr=true \n\
 stdout_logfile=/dev/stdout \n\
 stdout_logfile_maxbytes=0' > /etc/supervisor/conf.d/supervisord.conf
 
-# Create data directory for SQLite
-RUN mkdir -p /app/data
+# Create persistent data directory and set permissions
+RUN mkdir -p /data && chown -R 1000:1000 /data
+RUN chown -R 1000:1000 /app
+RUN chown -R 1000:1000 /usr/share/nginx/html
+RUN touch /var/run/nginx.pid && chown 1000:1000 /var/run/nginx.pid
+RUN chown -R 1000:1000 /var/log/nginx /var/lib/nginx
+
+# Set environment for database path
+ENV DATABASE_PATH=/data/project_memory.db
 
 # Hugging Face Spaces expects port 7860
 EXPOSE 7860
 
 # Start both services with supervisor
-CMD ["/usr/bin/supervisord", "-c", "/etc/supervisor/conf.d/supervisord.conf"]
+CMD ["/usr/bin/supervisord", "-c", "/etc/supervisor/conf.d/supervisord.conf"]

README copy.md

@@ -0,0 +1,116 @@
+---
+title: ProjectMemory
+emoji: ⚡
+colorFrom: red
+colorTo: indigo
+sdk: docker
+pinned: false
+license: mit
+short_description: Semantic, shared AI project memory.
+tags:
+  - building-mcp-track-enterprise
+---
+
+
+## 🎯 Track 1: Building MCP - Enterprise Category
+
+**Project Memory** is a multi-user, multi-project AI memory system powered by MCP (Model Context Protocol). It creates shared project memory where every action gets logged and becomes searchable via semantic search and AI chat.
+
+## 🚀 What We Built
+
+An MCP server that extends LLM capabilities for enterprise teams by:
+- **Persistent Project Memory**: Every task completion generates AI documentation that becomes searchable knowledge
+- **Semantic Search**: Vector-based memory retrieval across all project activities
+- **MCP Tool Integration**: Exposes project management capabilities as MCP tools
+- **Multi-User Collaboration**: Teams can share and search collective knowledge
+
+## 🛠️ MCP Tools Exposed
+
+Our MCP server provides these tools:
+- `create_project`: Initialize a new project workspace
+- `list_projects`: View all available projects
+- `join_project`: Join an existing project
+- `list_tasks`: Get project tasks with status
+- `complete_task`: Mark task as done with AI-generated documentation
+- `memory_search`: Semantic search across project history
+- `list_activity`: View project activity feed
+
+## 📹 Demo Video
+
+[Watch our 3-minute demo showing MCP integration with Claude Desktop](#) *(link to be added)*
+
+## 🏗️ Architecture
+
+```
+┌─────────────────┐     ┌─────────────────┐
+│   Web Frontend  │────▶│  FastAPI Backend │
+│     (React)     │     │   (MCP Client)   │
+└─────────────────┘     └─────────────────┘
+                               │
+                               ▼
+                        ┌─────────────────┐
+                        │   MCP Server    │
+                        │  (TypeScript)   │
+                        └─────────────────┘
+                               │
+                               ▼
+                        ┌─────────────────┐
+                        │  SQLite + Vec   │
+                        │   (Embeddings)  │
+                        └─────────────────┘
+```
+
+## 💡 Key Features
+
+1. **Task Completion Pipeline**: Transforms user work into searchable documentation
+2. **Vector Search**: Semantic retrieval using sqlite-vec embeddings
+3. **Chat Interface**: Natural language queries using MCP tools
+4. **Activity Feed**: Real-time project activity tracking
+5. **Multi-Project Support**: Manage multiple projects with isolated memory
+
+## 🔧 Technical Stack
+
+- **MCP Server**: TypeScript with @modelcontextprotocol/sdk
+- **Backend**: FastAPI (Python) as MCP client
+- **Frontend**: React + Vite + Tailwind CSS
+- **Database**: SQLite with sqlite-vec for embeddings
+- **AI**: Google Generative AI (Gemini) for documentation generation
+- **Deployment**: Docker container for Hugging Face Spaces
+
+## 🎮 How to Use
+
+1. **Create or Join a Project**: Start by creating a new project or joining an existing one
+2. **Complete Tasks**: Mark tasks as done and provide context about your work
+3. **AI Documentation**: System automatically generates searchable documentation
+4. **Search Memory**: Use semantic search to find any past work or decision
+5. **Chat with Memory**: Ask questions about project history using natural language
+
+## 🚢 Deployment
+
+This Space runs as a Docker container combining:
+- FastAPI backend serving as MCP client
+- React frontend for user interface
+- MCP server handling all tool operations
+- SQLite database with vector search capabilities
+
+## 🔐 Environment Variables
+
+Configure in Space settings:
+- `GOOGLE_API_KEY`: For Gemini AI integration
+- `DATABASE_URL`: (Optional) Custom database connection
+
+## 👥 Team
+
+Aryman235 (Aryman Deshwal)
+AmalNLal (Amal Nimmy Lal)
+
+
+## 📝 License
+
+MIT License - See LICENSE file for details
+
+## 🔗 Links
+
+- [GitHub Repository](https://github.com/YOUR_USERNAME/project-memory)
+- [MCP Documentation](https://modelcontextprotocol.io)
+- [Hackathon Page](https://huggingface.co/MCP-1st-Birthday)

backend/app/database.py

@@ -3,9 +3,10 @@
 from sqlalchemy import create_engine
 from sqlalchemy.orm import sessionmaker, declarative_base
 from pathlib import Path
+import os
 
-# Database file location
-DB_PATH = Path(__file__).parent.parent / "project_memory.db"
+# Database file location - use env var for HF Spaces persistence
+DB_PATH = os.environ.get("DATABASE_PATH", str(Path(__file__).parent.parent / "project_memory.db"))
 DATABASE_URL = f"sqlite:///{DB_PATH}"
 
 # Create engine with SQLite-specific settings

backend/app/main.py

@@ -72,11 +72,10 @@ app = FastAPI(
     lifespan=lifespan
 )
 
-# Configure CORS
-frontend_url = os.getenv("FRONTEND_URL", "http://localhost:5173")
+# Configure CORS - allow all origins for HF Spaces
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=[frontend_url, "http://localhost:5173", "http://localhost:3000"],
+    allow_origins=["*"],
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],

backend/app/vectorstore.py

@@ -3,9 +3,10 @@ import sqlite_vec
 import struct
 from typing import Optional
 import os
+from pathlib import Path
 
-# Database path - same as main SQLite database
-DB_PATH = os.path.join(os.path.dirname(__file__), "..", "project_memory.db")
+# Database path - use env var for HF Spaces persistence
+DB_PATH = os.environ.get("DATABASE_PATH", str(Path(__file__).parent.parent / "project_memory.db"))
 
 
 def _serialize_vector(vec: list[float]) -> bytes: