Update for Hugging Face Spaces deployment

Dockerfile

@@ -23,8 +23,8 @@ RUN mkdir -p data/documents data/vector_db
 # Set environment variable to avoid TOKENIZERS_PARALLELISM warning
 ENV TOKENIZERS_PARALLELISM=false
 
-# Expose the Streamlit port
-EXPOSE 8501
+# Expose the port required by Hugging Face Spaces
+EXPOSE 7860
 
-# Set the entrypoint command to run the Streamlit app
-CMD ["streamlit", "run", "app/ui/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"] 
+# Set the entrypoint command to run the Streamlit app on port 7860
+CMD ["streamlit", "run", "app/ui/streamlit_app.py", "--server.port=7860", "--server.address=0.0.0.0"] 

README.md

@@ -1,95 +1,41 @@
-# Personal AI Assistant with RAG (Hugging Face Edition)
+---
+title: Personal AI Assistant with RAG
+emoji: 🤗
+colorFrom: indigo
+colorTo: purple
+sdk: docker
+app_port: 7860
+pinned: true
+license: mit
+---
 
-A powerful personal AI assistant built with LangChain, integrating Retrieval-Augmented Generation (RAG) with a vector database (Qdrant) for improved contextual awareness and memory. This version uses Hugging Face models and can be deployed to Hugging Face Spaces for free hosting.
+# Personal AI Assistant with RAG
 
-[![Open In Spaces](https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-spaces-sm.svg)](https://huggingface.co/spaces)
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com)
+A powerful personal AI assistant that uses Retrieval-Augmented Generation (RAG) to provide responses based on your documents and notes.
 
 ## Features
 
-- Large Language Model integration using Hugging Face's free models
-- RAG-based memory system with vector database storage
-- Document ingestion pipeline for various file types
-- Simple web UI built with Streamlit
-- Conversation history tracking and retrieval
-- Free deployment on Hugging Face Spaces
+- Uses free Hugging Face models for language processing and embeddings
+- Stores and retrieves information in a vector database 
+- Upload PDF, TXT, and CSV files to expand the knowledge base
+- Add direct text input to your knowledge base
+- View sources for AI responses
+- Conversation history tracking
 
-## Project Structure
+## How to Use
 
-```
-.
-├── README.md
-├── requirements.txt
-├── .env.example
-├── app.py                 # Main entry point for Hugging Face Spaces
-├── space.py               # Hugging Face Spaces SDK integration
-├── app/
-│   ├── main.py            # FastAPI application entry point
-│   ├── config.py          # Configuration settings
-│   ├── ui/
-│   │   └── streamlit_app.py # Streamlit web interface
-│   ├── core/
-│   │   ├── llm.py         # LLM integration (Hugging Face)
-│   │   ├── memory.py      # RAG and vector store integration
-│   │   ├── agent.py       # Agent orchestration
-│   │   └── ingestion.py   # Document processing pipeline
-│   └── utils/
-│       └── helpers.py     # Utility functions
-└── data/
-    ├── documents/         # Store for uploaded documents
-    └── vector_db/         # Local vector database storage
-```
+1. **Upload Documents**: Use the sidebar to upload files (PDF, TXT, CSV)
+2. **Add Text**: Enter text directly into the knowledge base
+3. **Ask Questions**: Chat with the assistant about your documents
+4. **View Sources**: See where information is coming from
 
-## Setup
+## Built With
 
-1. Clone this repository
-2. Install dependencies:
-   ```
-   pip install -r requirements.txt
-   ```
-3. Copy `.env.example` to `.env` and fill in your Hugging Face API keys (optional)
-4. Start the Streamlit UI:
-   ```
-   streamlit run app/ui/streamlit_app.py
-   ```
+- Hugging Face Models
+  - LLM: google/flan-t5-large
+  - Embeddings: sentence-transformers/all-MiniLM-L6-v2
+- LangChain for orchestration
+- Qdrant for vector storage
+- Streamlit for UI
 
-## Usage
-
-1. Upload documents through the web interface
-2. Chat with your assistant, which can now reference your documents
-3. The assistant will automatically leverage your document knowledge to provide more personalized responses
-
-## Deployment to Hugging Face Spaces
-
-This app can be easily deployed to Hugging Face Spaces for free hosting:
-
-1. Create a Hugging Face account at [huggingface.co](https://huggingface.co)
-2. Set environment variables:
-   ```
-   export HF_USERNAME=your-username
-   export HF_TOKEN=your-huggingface-token
-   export SPACE_NAME=personal-rag-assistant  # optional
-   ```
-3. Run the deployment script:
-   ```
-   python space.py
-   ```
-4. Visit your deployed app at `https://huggingface.co/spaces/{your-username}/{space-name}`
-
-Alternatively, you can manually create a new Space on Hugging Face and link it to your GitHub repository.
-
-## Models Used
-
-This implementation uses the following free models from Hugging Face:
-
-- LLM: [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) - A powerful instruction-tuned model
-- Embeddings: [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - Efficient embedding model
-
-You can change these in the `.env` file.
-
-## Extending
-
-- Add more document loaders in `ingestion.py`
-- Integrate additional tools in `agent.py`
-- Customize the UI in `streamlit_app.py`
-- Switch to a different LLM in `llm.py` and `.env` 
+Created by [p3rc03](https://huggingface.co/p3rc03) 

huggingface-space.yml

@@ -3,7 +3,7 @@ emoji: 🤗
 colorFrom: indigo
 colorTo: purple
 sdk: docker
-app_port: 8501
+app_port: 7860
 pinned: true
 license: mit
 duplicated_from: huggingface/transformers-examples