Interview Coach AI

An intelligent FastAPI-based application that helps candidates prepare for interviews by analyzing job descriptions, matching resumes, generating interview questions, and providing personalized feedback.

📋 Features

1. Resume Analysis & Job Matching

Analyze how well your resume matches a job description
Get a detailed match score (0-100) with eligibility assessment
Receive actionable strengths, weaknesses, and improvement suggestions
Framework: MUST-HAVE vs NICE-TO-HAVE requirement matching

2. Interview Question Generation

Generate 10 targeted interview questions based on job description
Questions prioritize job requirements over general topics
Leverages both job description and resume data for relevance
Helps candidates prepare for role-specific scenarios

3. Comprehensive Candidate Profiling

Store and manage candidate resumes
Track skills, experience, education, and projects
Support for multiple resume versions per user

🏗️ Architecture

├── AuthService/                    # User authentication & registration
│   ├── api/
│   │   ├── login_api.py
│   │   ├── register_api.py
│   │   └── resetpassword_api.py
│   ├── authservice/
│   ├── controllers/
│   │   └── emailservice/           # Email verification & OTP
│   ├── schemas/
│   └── utils/

├── JobMaching/                     # Resume-Job Matching
│   ├── api/
│   │   └── analyse.py             # POST /api/analyseresume
│   ├── analyser/
│   │   └── resumeanalise.py       # Matching logic
│   ├── loader/
│   │   └── job_descpLoad.py       # Job description processing
│   └── resumeCompare/
│       ├── getterdata.py          # Data aggregation
│       └── llmcompare.py          # LLM-based comparison

├── interviewService/              # Interview Question Generation
│   ├── api/
│   │   └── question_gen.py        # POST /question_gen/generate
│   ├── QuestionGenService/
│   │   └── Questiongen.py         # Question generation logic
│   └── loader/
│       └── get_data.py            # Data loading

├── ResumeService/                 # Resume Management
│   ├── api/
│   │   ├── uploadresume.py
│   │   ├── getresumedata.py
│   │   └── deleteresume.py
│   ├── analyzer/
│   ├── preprocessing/
│   ├── services/
│   └── utils/

├── Models/                        # Database Models
│   ├── userReg/
│   │   ├── user.py               # User model
│   │   └── otp.py                # OTP model
│   └── resumeservice/
│       ├── resume_models.py
│       └── resumeschema.py

├── middlewares/                  # Custom middleware
│   └── auth.middleware.py

├── Dbconfig/                     # Database configuration
│   └── config.py

├── utils/                        # Utility functions
│   ├── apierror.py              # Custom error handling
│   ├── apiresponse.py           # Response formatting
│   └── token.py                 # JWT token utilities

├── app.py                        # Main FastAPI application
└── requirements.txt              # Project dependencies

🚀 Installation & Setup

Prerequisites

Python 3.8+
MongoDB (must be running)
GROQ API Key (for LLM capabilities)

1. Clone & Navigate

cd "InterviewCoach AI"

2. Create Virtual Environment

python -m venv project_2
source project_2/Scripts/activate  # Windows
# or
source project_2/bin/activate      # macOS/Linux

3. Install Dependencies

pip install -r requirements.txt

4. Set Environment Variables

Create a .env file in the project root:

GROQ_API_KEY=your_groq_api_key_here
MONGODB_URI=mongodb://localhost:27017/interviewcoach
JWT_SECRET=your_secret_key_here
SMTP_EMAIL=your_email@gmail.com
SMTP_PASSWORD=your_app_password

5. Start MongoDB

# Windows
mongod

# macOS
brew services start mongodb-community

# Linux
sudo systemctl start mongod

6. Run Application

python app.py
# or
uvicorn app:app --reload

The API will be available at: http://localhost:8000

7. Access API Documentation

Swagger UI: http://localhost:8000/docs
ReDoc: http://localhost:8000/redoc

📡 API Endpoints

Authentication & User Management

POST   /auth/register           # Register new user
POST   /auth/login              # Login user
POST   /auth/reset-password     # Reset password
POST   /auth/verify-otp         # Verify OTP

Resume Management

POST   /resume/upload           # Upload resume
GET    /resume/get/{resume_id}  # Get resume data
DELETE /resume/delete/{id}      # Delete resume

Resume-Job Matching (Job Matching Service)

POST   /api/analyseresume

Request Body:

{
  "resume_id": "user_resume_id",
  "description": "Job description text (copy from job posting)"
}

Response:

{
  "status": "success",
  "data": {
    "score": 65,
    "eligible": "PARTIAL",
    "strengths": "• Python and MongoDB experience matches job requirements\n• Project management experience",
    "weaknesses": "• Limited cloud platform experience (AWS, Azure required)\n• No microservices architecture background",
    "suggestions": "• Take AWS certification courses\n• Build projects using microservices\n• Gain hands-on cloud deployment experience"
  }
}

Interview Question Generation

POST   /question_gen/generate

Request Body:

{
  "resume_id": "user_resume_id",
  "description": "Job description text"
}

Response:

{
  "questions": [
    "1. Can you describe your experience with Python and how it relates to this role?",
    "2. Tell us about a project where you used MongoDB...",
    "... (8 more questions)"
  ]
}

📊 Workflow Example

Step 1: Upload Resume

POST /resume/upload
- Upload your resume (PDF/DOCX)
- System extracts: skills, experience, education, projects

Step 2: Get Resume ID

GET /resume/get/{user_id}
- Returns: resume_id for use in other endpoints

Step 3: Analyze Job Match

POST /api/analyseresume
{
  "resume_id": "65a1b2c3d4e5f6g7h8i9",
  "description": "Senior Backend Developer...
  
  Responsibilities:
  - Design scalable microservices
  - Work with AWS/Azure
  - Lead technical discussions
  
  Requirements:
  - 5+ years backend development
  - Kubernetes, Docker
  - System design experience"
}

Response: Score 72, PARTIAL eligible

Step 4: Generate Interview Questions

POST /question_gen/generate
{
  "resume_id": "65a1b2c3d4e5f6g7h8i9",
  "description": "Senior Backend Developer..."
}

Response: 10 targeted interview questions

🧠 AI/LLM Features

LLM Provider: Groq (Llama 3.1 8B Instant)

Resume Matching Logic

Framework: MUST-HAVE vs NICE-TO-HAVE requirements
Scoring: 0-100 based on actual job requirements
Output: Score, Eligibility (YES/PARTIAL/NO), Strengths, Weaknesses, Suggestions

Question Generation Logic

Approach: Job description prioritization with resume context
Output: 10 unique, role-specific interview questions
Quality: Targets technical depth and practical scenarios

🔐 Security Features

JWT Authentication
- Secure token-based user authentication
- Token expiration & refresh mechanisms
Password Management
- Secure password hashing
- OTP-based password reset
- Email verification
Custom Middleware
- Authentication middleware for protected routes
- Error handling middleware
Error Handling
- Custom APIError class for consistent error responses
- Proper HTTP status codes
- Descriptive error messages

📦 Dependencies

Core dependencies:

fastapi==0.104.1
uvicorn==0.24.0
mongoengine==0.23.1
python-dotenv==1.0.0
langchain==0.1.0
langchain-groq==0.1.0
pydantic==2.0.0
pydantic-settings==2.0.0

See requirements.txt for complete list.

🛠️ Development Guide

Adding New Features

Create API Route (in api/ folder)
Create Service Logic (in respective service folder)
Update Models (if needed in Models/)
Add Tests (recommended)
Update Documentation (README, docstrings)

Code Structure Best Practices

Keep API layer thin (validation only)
Move business logic to service layer
Use models for database operations
Centralize error handling

🐛 Troubleshooting

MongoDB Connection Error

Solution: Ensure MongoDB is running
Windows: mongod
macOS: brew services start mongodb-community

GROQ API Key Error

Check: GROQ_API_KEY is set in .env file
Test: Make a test API call to verify key validity

Import Errors

Solution: pip install -r requirements.txt
Verify: Python path includes project directory

📝 API Response Formats

Success Response

{
  "status": "success",
  "data": {
    // endpoint-specific data
  }
}

Error Response

{
  "detail": {
    "error": "Error description",
    "error_code": "ERROR_CODE"
  }
}

🚦 HTTP Status Codes

200 OK - Successful request
400 Bad Request - Invalid input/validation error
401 Unauthorized - Authentication failed
404 Not Found - Resource not found
500 Internal Server Error - Server error

📈 Performance Tips

Resume Upload: Keep files < 5MB for faster processing
Job Description: 500-2000 characters optimal length
Batch Requests: Limit concurrent API calls to 5-10/second
Caching: Consider caching frequently matched job descriptions

🤝 Contributing

Create feature branch: git checkout -b feature/your-feature
Make changes and test
Commit with clear messages
Push to branch
Create Pull Request

📞 Support

For issues or questions:

Check documentation above
Review error messages and codes
Check MongoDB connection
Verify API keys
Check logs: uvicorn app:app --log-level debug

📄 License

🎯 Roadmap

Support for multiple resume versions per user
Resume parsing improvements (PDF, DOCX formatting)
Interview mock sessions with real-time feedback
Performance metrics and analytics dashboard
Integration with LinkedIn job postings
Multi-language support
Mobile app
Interview video recording and analysis

🔄 Version History

v1.0.0 (Current)

✅ Resume analysis & job matching
✅ Interview question generation
✅ User authentication
✅ Resume management
✅ Email verification & OTP

Last Updated: March 19, 2026

Maintainer: Interview Coach AI Team

Name		Name	Last commit message	Last commit date
Latest commit History 13 Commits
AuthService		AuthService
Dbconfig		Dbconfig
JobMaching		JobMaching
Models		Models
ResumeService		ResumeService
interviewService		interviewService
middlewares		middlewares
uploads		uploads
utils		utils
BACKEND_FRONTEND_INTEGRATION.md		BACKEND_FRONTEND_INTEGRATION.md
QUICK_START.md		QUICK_START.md
README.md		README.md
app.py		app.py
requirements.txt		requirements.txt
sampleenv		sampleenv

Folders and files

Latest commit

History

Repository files navigation

Interview Coach AI

📋 Features

1. Resume Analysis & Job Matching

2. Interview Question Generation

3. Comprehensive Candidate Profiling

🏗️ Architecture

🚀 Installation & Setup

Prerequisites

1. Clone & Navigate

2. Create Virtual Environment

3. Install Dependencies

4. Set Environment Variables

5. Start MongoDB

6. Run Application

7. Access API Documentation

📡 API Endpoints

Authentication & User Management

Resume Management

Resume-Job Matching (Job Matching Service)

Interview Question Generation

📊 Workflow Example

Step 1: Upload Resume

Step 2: Get Resume ID

Step 3: Analyze Job Match

Step 4: Generate Interview Questions

🧠 AI/LLM Features

LLM Provider: Groq (Llama 3.1 8B Instant)

Resume Matching Logic

Question Generation Logic

🔐 Security Features

📦 Dependencies

🛠️ Development Guide

Adding New Features

Code Structure Best Practices

🐛 Troubleshooting

MongoDB Connection Error

GROQ API Key Error

Import Errors

📝 API Response Formats

Success Response

Error Response

🚦 HTTP Status Codes

📈 Performance Tips

🤝 Contributing

📞 Support

📄 License

🎯 Roadmap

🔄 Version History

v1.0.0 (Current)

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