README.md

metadata

title: linkedin_profile_chatbot
app_file: linkedin_profile_chatbot/app.py
sdk: gradio
sdk_version: 5.34.2
python_version: 3.12

LinkedIn Profile Chatbot

A Gradio-based chatbot that can answer questions about your LinkedIn profile using AI. Upload your LinkedIn profile PDF and summary, then deploy to Hugging Face Spaces for easy sharing and embedding.

Setup Instructions

1. Prepare Your Profile Data

Download Your LinkedIn Profile as PDF

1. Go to your LinkedIn profile page
2. Click the "More" button (three dots) in your profile section
3. Select "Save to PDF" from the dropdown menu
4. Save the PDF file as linkedin.pdf

Create a Profile Summary

1. Write a brief summary of your professional background, skills, and experience
2. Save this as a plain text file named summary.txt
3. Keep it concise but informative (2-3 paragraphs recommended)

Place Files in the /assets Folder

1. Copy your linkedin.pdf file to the /assets directory
2. Copy your summary.txt file to the /assets directory

Your file structure should look like:

/assets
├── linkedin.pdf
└── summary.txt

2. Local Development

Install Dependencies

# Install using uv (recommended)
uv sync

Run the Gradio App Locally

# Using uv
uv run linkedin_profile_chatbit/app.py

The Gradio interface will start on http://localhost:7860 by default.

3. Deployment to Hugging Face Spaces

Prerequisites

• A Hugging Face account
• Your repository pushed to GitHub or Hugging Face Hub

Deploy to Hugging Face Spaces

1. Go to Hugging Face Spaces
2. Click "Create new Space"
3. Choose a name for your space
4. Select "Docker" as the SDK
5. Connect your GitHub repository or upload your files
6. The space will automatically build using the provided Dockerfile

Configuration

The project is pre-configured for Hugging Face Spaces with:

• Dockerfile: Uses Python 3.12 with uv for fast dependency management
• README.md header: Configured for Docker deployment
• Port 7860: Standard Gradio port for Hugging Face Spaces

4. Environment Variables

Create a .env file in your project root with your OpenAI API key:

OPENAI_API_KEY=your_openai_api_key_here

For Hugging Face Spaces, add this as a secret in your Space settings.

Usage

Local Usage

1. Run the application locally using uv run run_gradio.py
2. Open your browser to http://localhost:7860
3. Start chatting with your LinkedIn profile chatbot

Deployed Usage

1. Once deployed to Hugging Face Spaces, your chatbot will be available at your Space URL
2. Share the URL with others or embed it in your website
3. The chatbot will answer questions about your professional background using your LinkedIn PDF and summary

Features

• Gradio Interface: Clean, user-friendly chat interface
• AI-Powered: Uses OpenAI's GPT models for intelligent responses
• Document Processing: Extracts information from your LinkedIn PDF
• Docker Deployment: Containerized for reliable deployment
• Fast Dependencies: Uses uv for quick installation and updates
• Responsive Design: Works on desktop and mobile devices

Technical Details

Architecture

• Frontend: Gradio web interface
• Backend: Python with OpenAI API integration
• Document Processing: PyPDF for PDF text extraction
• Deployment: Docker container with Python 3.12
• Dependency Management: uv for fast, reliable package management

Key Files

• run_gradio.py: Main entry point for the Gradio application
• linkedin_profile_chatbot/app.py: Gradio interface configuration
• linkedin_profile_chatbot/chat.py: Chat logic and AI integration
• linkedin_profile_chatbot/core.py: Core functionality and document processing
• Dockerfile: Container configuration for deployment
• pyproject.toml: Project configuration and dependencies

Troubleshooting

Common Issues

Build fails on Hugging Face:

• Ensure your .env file is not committed to the repository
• Add your OpenAI API key as a secret in Hugging Face Spaces settings
• Check that all required files are present in the /assets folder

Chatbot gives generic responses:

• Verify your linkedin.pdf and summary.txt files are in the /assets folder
• Check that the PDF contains readable text (not just images)
• Ensure your OpenAI API key is valid and has sufficient credits

Local development issues:

• Install uv: curl -LsSf https://astral.sh/uv/install.sh | sh
• Run uv sync to install dependencies
• Check that Python 3.12+ is available

Docker Build Issues

If you encounter Docker build issues locally:

# Build the Docker image
docker build -t linkedin-chatbot .

# Run the container
docker run -p 7860:7860 linkedin-chatbot

Support

For issues or questions:

1. Check the Hugging Face Spaces logs for deployment issues
2. Verify your OpenAI API key and credits
3. Ensure your profile documents are properly formatted and placed in /assets