README.md

metadata

title: Portfolio Chatbot
emoji: 🤖
colorFrom: green
colorTo: purple
sdk: docker
pinned: false

Portfolio Chatbot Agent

AI-powered chatbot for the portfolio using OpenAI Agents SDK with Gemini LLM.

Features

• FastAPI backend with async support
• OpenAI Agents SDK for conversational AI
• Gemini 2.0 Flash as the LLM provider
• UV package manager for fast dependency installation
• Docker support for HuggingFace Spaces deployment
• CORS enabled for frontend integration

Setup (Local Development)

1. Install UV Package Manager

# Linux/macOS
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

2. Create Virtual Environment & Install Dependencies

# Create virtual environment
uv venv .venv

# Activate virtual environment
# Linux/macOS:
source .venv/bin/activate
# Windows:
.venv\Scripts\activate

# Install dependencies with UV
uv pip install -r requirements.txt

3. Configure Environment

cp .env.example .env
# Edit .env and add your GEMINI_API_KEY

Get your Gemini API key from: https://aistudio.google.com/app/apikey

4. Run Locally

uvicorn main:app --reload --port 7860

Or:

python main.py

API Endpoints

• GET / - Health check
• GET /health - Health check for monitoring
• POST /chat - Chat endpoint
• GET /info - Get portfolio owner info

Chat Request Example

POST /chat
{
  "message": "Tell me about your projects",
  "history": []
}

Chat Response

{
  "response": "I have worked on several exciting projects...",
  "success": true
}

Deploy to HuggingFace Spaces

1. Create a new Space on HuggingFace (Docker type)
2. Upload all files from this directory
3. Add GEMINI_API_KEY as a secret in Space settings
4. The Space will automatically build and deploy

HuggingFace Spaces Settings

• Space hardware: CPU Basic (free tier works)
• Space type: Docker
• Secrets: Add GEMINI_API_KEY

Frontend Integration

Update your frontend .env file:

NEXT_PUBLIC_CHATBOT_API_URL=https://your-username-your-space.hf.space/chat

File Structure

chatbot-agent/
├── main.py           # FastAPI application
├── agent.py          # Portfolio chatbot agent
├── requirements.txt  # Python dependencies
├── Dockerfile        # Docker configuration (uses UV)
├── .env.example      # Environment template
├── .gitignore        # Git ignore rules
└── README.md         # This file

Tech Stack

• Framework: FastAPI
• AI SDK: OpenAI Agents SDK
• LLM: Google Gemini 2.0 Flash
• Package Manager: UV (fast Python package installer)
• Deployment: Docker / HuggingFace Spaces