TalentScout AI 🤖

An intelligent AI-powered talent screening and technical interview assistant built with Streamlit. TalentScout AI automates the initial screening process by collecting candidate information and conducting technical interviews based on their tech stack.

🚀 Features

• Automated Candidate Screening: Collects essential candidate information (name, email, phone, experience, position, location)
• Smart Tech Stack Analysis: Parses and validates candidate's technology stack
• Dynamic Technical Interviews: Generates personalized technical questions based on the candidate's tech stack
• Interactive Chat Interface: User-friendly conversational interface built with Streamlit
• Data Validation: Comprehensive validation for email, phone, and experience inputs
• Session Management: Maintains conversation state and candidate information throughout the interview process

📋 Prerequisites

• Python 3.7 or higher
• pip (Python package installer)

🛠️ Installation

1. Clone the repository

   git clone https://github.com/ManivardhanDonuri/TalentScout-Al.git
   cd TalentScout-Al

2. Install dependencies

   pip install -r requirements.txt

🎯 Usage

Local Development

1. Run the application

   streamlit run app.py

2. Open your browser

   • The application will automatically open in your default browser
   • If not, navigate to http://localhost:8501

3. Start the interview process

   • Enter the candidate's name when prompted
   • Follow the guided conversation to collect candidate information
   • The AI will automatically generate technical questions based on the candidate's tech stack

🚀 Deployment on Render

Step-by-Step Render Deployment

1. Sign up for Render

   • Go to render.com
   • Sign up with your GitHub account

2. Create a New Web Service

   • Click "New +" button
   • Select "Web Service"
   • Connect your GitHub repository: ManivardhanDonuri/TalentScout-Al

3. Configure the Service

   • Name: talent-scout-ai (or your preferred name)
   • Environment: Python 3
   • Build Command: pip install -r requirements.txt
   • Start Command: streamlit run app.py --server.port=$PORT --server.address=0.0.0.0

4. Deploy

   • Click "Create Web Service"
   • Wait for the build process (usually 2-3 minutes)

Your App Will Be Live At:

https://talent-scout-ai.onrender.com

📁 Project Structure

TalentScout-Al/
├── app.py                 # Main application entry point
├── conversation_handler.py # Core conversation logic and interview flow
├── ui_components.py       # Streamlit UI components and styling
├── config.py             # Configuration constants and messages
├── utils.py              # Utility functions and validators
├── requirements.txt      # Python dependencies
├── .streamlit/          # Streamlit configuration
│   └── config.toml     # Deployment settings
└── README.md            # This file

🔧 Configuration

The application uses several configuration files:

• config.py: Contains conversation stages, error messages, and success messages
• utils.py: Validation functions for email, phone, and experience inputs
• ui_components.py: Streamlit UI components and styling
• .streamlit/config.toml: Streamlit deployment configuration

🎨 Features in Detail

Candidate Information Collection

• Name: Basic name validation
• Email: Email format validation
• Phone: Phone number format validation
• Experience: Years of experience validation
• Position: Desired job position
• Location: Geographic location
• Tech Stack: Technology stack parsing and validation

Technical Interview Process

• Automatically generates 5 technical questions per technology mentioned
• Questions cover:
  • Key features and capabilities
  • Real-world application scenarios
  • Best practices
  • Troubleshooting approaches
  • Latest trends and updates

Conversation Flow

1. Greeting Stage: Collect candidate's name
2. Information Collection: Gather all candidate details
3. Question Generation: Create personalized technical questions
4. Technical Interview: Conduct the interview
5. Completion: Provide summary and next steps

🛠️ Render Deployment Benefits

• ✅ Free Tier Available: Deploy for free with Render's free tier
• ✅ Automatic HTTPS: SSL certificates included
• ✅ Custom Domains: Add your own domain name
• ✅ Auto-Deploy: Automatic deployments from GitHub
• ✅ Scalable: Easy to scale as your app grows
• ✅ Monitoring: Built-in monitoring and logs

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author

Manivardhan Donuri

• GitHub: @ManivardhanDonuri

🙏 Acknowledgments

• Built with Streamlit for the web interface
• Deployed on Render for reliable hosting
• Designed for modern talent acquisition workflows
• Inspired by the need for efficient technical screening processes

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