An AI-powered Interview Agent that conducts technical interviews, evaluates candidate answers, and generates detailed performance reports.
✅ AI-generated interview questions
✅ Role-based interviews
- Python Developer
- ML Engineer
- GenAI Engineer
✅ Answer evaluation with scoring
✅ Strength and weakness analysis
✅ Final interview report generation
✅ FastAPI backend
✅ Streamlit frontend
✅ LangGraph workflow
✅ Gemini AI integration
✅ SQLite database support
| Technology | Purpose |
|---|---|
| Python | Core Programming |
| FastAPI | Backend API |
| Streamlit | Frontend UI |
| LangGraph | Interview Workflow |
| Google Gemini | AI Question Generation & Evaluation |
| SQLite | Database |
| SQLAlchemy | ORM |
ai-interview-agent/
│
├── Backend/
│ ├── main.py
│ ├── graph.py
│ ├── database.py
│ └── model.py
│
├── Frontend/
│ └── streamlitapp.py
│
├── Screenshots/
│
├── requirements.txt
├── README.md
└── .gitignore
git clone https://github.com/yourusername/ai-interview-agent.git
cd ai-interview-agentpip install -r requirements.txtGEMINI_API_KEY=your_api_keyuvicorn Backend.main:app --reloadstreamlit run Frontend/streamlitapp.py- Candidate enters personal and interview details.
- AI generates role-specific interview questions.
- Candidate submits answers through the Streamlit interface.
- Gemini AI evaluates each response.
- The system provides scores, strengths, weaknesses, and feedback.
- A final interview report is generated.
- Built an AI-powered interview automation system.
- Integrated Google Gemini for intelligent answer evaluation.
- Implemented LangGraph workflow for interview management.
- Developed a FastAPI backend and Streamlit frontend.
- Generated detailed candidate performance reports.
- Designed a scalable architecture for multiple technical roles.
Neha Nayar


