🤖 InterviewAce

AI-Powered Career Preparation

🚀 An intelligent interview simulator leveraging advanced AI and NLP to provide personalized career preparation experiences with real-time feedback. 🚀

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📋 Table of Contents

• 🚀 Project Overview
• ✨ Key Features
• 🛠️ Tech Stack
• 🎬 Demo
• 🚀 Getting Started
• 🤝 Contributing
• 📄 License
• 📬 Contact

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🚀 Project Overview

InterviewAce is an intelligent platform designed to help users prepare for job interviews using the power of Artificial Intelligence. It provides a realistic interview simulation experience with real-time feedback and personalized coaching to help users improve their performance.

Leveraging advanced Natural Language Processing (NLP) and integrated with leading AI models like OpenAI and Gemini, InterviewAce offers personalized question generation and in-depth analysis of responses, making it a comprehensive tool for career preparation.

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✨ Key Features

✅ AI-Driven Interview Simulations: Practice with realistic interview scenarios generated by AI. ✅ Real-time Feedback System: Get instant analysis on your responses, including content, tone, and structure. ✅ Personalized Question Generation: AI adapts questions based on your profile and performance. ✅ Voice Recognition: Practice speaking your answers and get feedback on delivery. ✅ Behavioral Assessments: Receive insights into your communication style and behavioral patterns. ✅ Personalized Improvement Recommendations: Get tailored tips to enhance your interview skills.

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🛠️ Tech Stack

• Frontend: React
• Backend/AI: Python, VAPI, OpenAI API, Gemini API, NLP
• Deployment: Vercel

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🎬 Demo

Experience the live demo here: https://ai-interview-liart-five.vercel.app/

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🚀 Getting Started

Follow these steps to set up and run the project locally.

Prerequisites

• Node.js (for React frontend)
• Python (for backend/AI)
• Git
• API keys for OpenAI and Gemini

Installation

1. Clone the repository:

   git clone https://github.com/Pawandasila/ai-interview.git
   cd ai-interview

2. Set up Frontend:

   # Navigate to the frontend directory (adjust path if necessary)
   cd frontend
   npm install # or yarn install or pnpm install

3. Set up Backend:

   # Navigate to the backend directory (adjust path if necessary)
   cd backend
   pip install -r requirements.txt

4. Set up Environment Variables:

   Create a .env file in the appropriate directories (frontend and/or backend) and add your API keys:

   OPENAI_API_KEY=your_openai_key
   GEMINI_API_KEY=your_gemini_key
   # Add any other necessary environment variables

5. Run Locally:

   Start the backend server and the frontend development server in separate terminals.

   # Start backend (adjust command based on your backend setup)
   python app.py 

   # Start frontend (in the frontend directory)
   npm start # or yarn start or pnpm start

   Open http://localhost:3000 (or the specified frontend port) in your browser.

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🤝 Contributing

We welcome contributions! Please see the CONTRIBUTING.md file (if it exists) or follow these general steps:

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

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📄 License

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

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📬 Contact

If you have any questions or feedback, feel free to reach out:

• GitHub: @Pawandasila
• Live Demo: InterviewAce Live

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Built with ❤️ by Pawan Dasila for the developer community.