Add Hugging Face Spaces deployment configuration

- Dockerfile: Multi-stage Docker build for HF Spaces
- README.md: Space metadata and feature description
- DEPLOYMENT.md: Complete guide for local and cloud deployment
- Updated .streamlit/config.toml for production

Ready to deploy as public demo on Hugging Face Spaces

.streamlit/config.toml

@@ -1,8 +1,8 @@
-[theme]
-base="light"
-primaryColor="#3B82F6"
-backgroundColor="#FAFAFA"
-secondaryBackgroundColor="#F3F4F6"
-textColor="#1F2937"
-font="sans serif"
+[server]
+headless = true
+port = 8501
+enableCORS = false
+enableXsrfProtection = false
 
+[browser]
+gatherUsageStats = false

DEPLOYMENT.md

@@ -0,0 +1,176 @@
+# FocusFlow Deployment Guide
+
+## Local Deployment (Offline Mode - Default)
+
+**Best for**: Personal study, maximum privacy, offline access
+
+### Prerequisites
+- Python 3.10+
+- Ollama installed
+- 4GB+ RAM
+
+### Setup
+
+```bash
+# Clone repository
+git clone https://github.com/thesivarohith/hack.git
+cd hack
+
+# Create virtual environment
+python3 -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Install Ollama models
+ollama pull llama3.2:1b
+ollama pull nomic-embed-text
+```
+
+### Run
+
+```bash
+# Terminal 1 - Start backend
+uvicorn backend.main:app --host 0.0.0.0 --port 8000 --reload
+
+# Terminal 2 - Start frontend
+streamlit run app.py
+```
+
+Access at: `http://localhost:8501`
+
+---
+
+## Hugging Face Spaces Deployment (Cloud Demo)
+
+**Best for**: Sharing publicly, showcasing
+
+### Prerequisites
+- Hugging Face account (free)
+- Hugging Face API token
+
+### Step 1: Get HF API Token
+
+1. Go to [huggingface.co/settings/tokens](https://huggingface.co/settings/tokens)
+2. Click "New token"
+3. Name: `focusflow`
+4. Type: **Read**
+5. Copy the token: `hf_xxxxx`
+
+### Step 2: Create Space
+
+1. Go to [huggingface.co](https://huggingface.co)
+2. Click profile → **New Space**
+3. Settings:
+   - **Name**: `focusflow`
+   - **License**: MIT
+   - **SDK**: Docker
+   - **Hardware**: CPU basic (free)
+4. Click **Create Space**
+
+### Step 3: Configure Environment
+
+1. In your space, go to **Settings** → **Variables**
+2. Add secret:
+   - Key: `HUGGINGFACE_API_TOKEN`
+   - Value: `hf_xxxxx` (your token)
+
+### Step 4: Push Code
+
+```bash
+# Clone your space
+git clone https://huggingface.co/spaces/YOUR_USERNAME/focusflow
+cd focusflow
+
+# Copy files from this repo
+cp -r /path/to/hack/* .
+
+# Add and commit
+git add .
+git commit -m "Initial deployment"
+git push
+```
+
+### Step 5: Wait for Build
+
+- Build takes ~10-15 minutes
+- Watch logs in the Space
+- Your app will be live at: `https://huggingface.co/spaces/YOUR_USERNAME/focusflow`
+
+---
+
+## Comparison
+
+| Feature | Local (Ollama) | Cloud (HF Spaces) |
+|---------|----------------|-------------------|
+| **Internet** | ❌ Not required | ✅ Required |
+| **Model** | llama3.2:1b (1B params) | Llama-3-8B (8B params) |
+| **Speed** | ⚡ Very fast | 🐢 Slower (CPU) |
+| **Privacy** | 🔒 100% private | 🌐 Public demo |
+| **Cost** | 💰 Free | 💰 Free |
+| **Best For** | Daily studying | Sharing/demos |
+
+---
+
+## Switching Modes Locally
+
+### Test Cloud Mode Locally
+
+```bash
+# Set environment variables
+export LLM_PROVIDER=huggingface
+export HUGGINGFACE_API_TOKEN=hf_xxxxx
+
+# Run normally
+streamlit run app.py
+```
+
+### Back to Local Mode
+
+```bash
+# Unset or just restart terminal
+unset LLM_PROVIDER
+streamlit run app.py
+```
+
+---
+
+## Troubleshooting
+
+### Local Mode Issues
+
+**Ollama not found:**
+```bash
+# Install Ollama
+curl -fsSL https://ollama.com/install.sh | sh
+ollama pull llama3.2:1b
+ollama pull nomic-embed-text
+```
+
+**Port already in use:**
+```bash
+# Find and kill process on port 8501
+lsof -ti:8501 | xargs kill -9
+```
+
+### Cloud Mode Issues
+
+**API token error:**
+- Make sure `HUGGINGFACE_API_TOKEN` is set in HF Space variables
+- Token must have at least **Read** permission
+
+**Slow responses:**
+- This is normal on free CPU tier
+- Responses take 10-30 seconds (vs instant on local)
+
+---
+
+## Support
+
+- **GitHub Issues**: [github.com/thesivarohith/hack/issues](https://github.com/thesivarohith/hack/issues)
+- **Documentation**: See [TECHNICAL_DOCUMENTATION.md](./TECHNICAL_DOCUMENTATION.md)
+
+---
+
+**Recommended**: Use local deployment for daily studying, cloud deployment only for demos/sharing.

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements and install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY . .
+
+# Create data directories
+RUN mkdir -p data chroma_db
+
+# Expose ports for backend (8000) and frontend (8501)
+EXPOSE 8501 8000
+
+# Set environment to use Hugging Face
+ENV LLM_PROVIDER=huggingface
+
+# Create startup script
+RUN echo '#!/bin/bash\n\
+# Start FastAPI backend in background\n\
+uvicorn backend.main:app --host 0.0.0.0 --port 8000 &\n\
+\n\
+# Wait for backend to start\n\
+sleep 2\n\
+\n\
+# Start Streamlit frontend\n\
+streamlit run app.py --server.port 8501 --server.address 0.0.0.0 --server.headless true\n\
+' > /app/start.sh && chmod +x /app/start.sh
+
+# Run startup script
+CMD ["/app/start.sh"]

README.md

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+---
+title: FocusFlow
+emoji: 📚
+colorFrom: blue
+colorTo: purple
+sdk: docker
+app_port: 8501
+pinned: false
+---
+
+# 📚 FocusFlow - AI Study Companion
+
+An intelligent study assistant powered by AI that transforms your learning materials into personalized, adaptive study experiences.
+
+## ✨ Features
+
+- **📖 Multi-Subject Study Planning**: Upload PDFs and get automated multi-day study plans
+- **💬 RAG-Powered Q&A**: Ask questions and get answers with source citations
+- **📝 Adaptive Quizzes**: Context-based quizzes that adapt to your performance
+- **📊 Progress Tracking**: Track mastery levels and quiz history
+- **🎯 Smart Day Progression**: Automatically unlocks new topics as you complete them
+- **🔍 Source Citations**: Every answer cites the exact source and page number
+
+## 🤖 Models Used
+
+- **LLM**: Meta-Llama-3-8B-Instruct (via Hugging Face Inference API)
+- **Embeddings**: nomic-embed-text (for semantic search)
+
+## 🚀 How to Use
+
+1. **Upload PDFs**: Add your study materials in the Sources panel
+2. **Generate Plan**: Ask the Calendar to create a study plan (e.g., "Make a 5-day plan")
+3. **Study**: Click on topics to view lessons and ask questions
+4. **Take Quizzes**: Test your knowledge and unlock new topics
+
+## 📝 Demo Note
+
+This is a **cloud demo version** running on Hugging Face Spaces using the Llama-3-8B model.
+
+**For offline/local use** with enhanced privacy and llama3.2:1b (no internet required):
+- 📂 [GitHub Repository](https://github.com/thesivarohith/hack)
+- 📖 [Local Setup Guide](https://github.com/thesivarohith/hack/blob/main/RUN_GUIDE.md)
+
+The local version works completely offline and keeps all your data private on your machine.
+
+## 🛠️ Tech Stack
+
+- **Frontend**: Streamlit
+- **Backend**: FastAPI + LangChain
+- **Vector DB**: ChromaDB
+- **LLM**: Hugging Face Inference API
+
+## 📄 License
+
+MIT License - See [LICENSE](https://github.com/thesivarohith/hack/blob/main/LICENSE) for details.
+
+---
+
+Built with ❤️ for better learning experiences