🧠 Resume Intelligence Analyzer

An AI-powered backend service built with FastAPI and Google Gemini that automatically analyzes and interprets resumes AND GitHub profiles. The system extracts key technical skills, experience, and technology categories from uploaded resumes (PDF, DOCX, or TXT) and analyzes GitHub repositories to provide scored, structured, and explainable outputs.

🎯 Project Objective

Build an intelligent API that:

✅ Accepts resume files in multiple formats
🔍 Extracts and preprocesses text content
🐙 Analyzes GitHub profiles and repositories
🤖 Uses Google Gemini's large language model for analysis
📊 Returns structured insights with confidence scores
🏆 Provides skill scoring (1-10 scale) for ranking and matching
🎯 Cross-validates skills between resume and GitHub activity

🏗️ Architecture

1️⃣ Text Extraction Layer (`extractors.py`)

Purpose: Accept resumes in different formats
Supported Formats: PDF, DOCX, TXT
Output: Raw text content
Technologies: PyPDF2, python-docx

2️⃣ Text Preprocessing Layer (`preprocessor.py`)

Purpose: Clean and normalize text for AI analysis
Operations:
- Remove extra whitespace
- Clean special characters
- Preserve structure (headings, bullets)
Technologies: Python regex (re)

3️⃣ AI Analysis Layer (`gemini_analyzer.py`)

Purpose: Leverage Google Gemini for intelligent extraction
Capabilities:
- Identify skills and technologies
- Assess skill prominence and frequency
- Estimate experience duration
- Categorize technologies (Languages, Frameworks, Tools, Libraries)
Output: Structured JSON with confidence scores

4️⃣ Skill Scoring Mechanism

Gemini assigns scores from 1-10 for each skill:

9-10: Very prominent, deeply integrated
6-8: Moderate frequency, practical experience
3-5: Mentioned once or twice
1-2: Inferred or minimal mention

5️⃣ API Layer (`main.py`)

Framework: FastAPI
Primary Endpoint: POST /analyze
Features:
- File upload handling
- Error handling and validation
- Structured JSON responses
- Interactive API documentation

📋 API Endpoints

`POST /analyze`

Main endpoint for resume analysis

Request:

curl -X POST "http://localhost:8000/analyze" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@resume.pdf"

`GET /analyze/github/{username}`

NEW: GitHub profile analysis endpoint

Request:

curl -X GET "http://localhost:8000/analyze/github/tulika-anand"

Response:

{
  "status": "success",
  "username": "tulika-anand",
  "github_url": "https://github.com/tulika-anand",
  "user_info": {
    "name": "Tulika Anand",
    "bio": "Software Engineer | AI/ML Enthusiast",
    "followers": 150,
    "public_repos": 25
  },
  "stats": {
    "languages_used": {"Python": 12, "JavaScript": 8, "Java": 5},
    "total_stars_earned": 420,
    "total_forks": 38
  },
  "analysis": {
    "overall_experience_level": "Intermediate",
    "experience_years_estimate": 2.5,
    "skills_with_scores": {
      "Python": 9,
      "React": 8,
      "FastAPI": 7,
      "Machine Learning": 8
    },
    "dominant_tech_stack": {
      "languages": ["Python", "JavaScript"],
      "frameworks": ["React", "FastAPI", "Django"],
      "domains": ["AI/ML", "Web Development"]
    },
    "project_analysis": {
      "notable_projects": [
        {
          "name": "PharmLensAI",
          "description": "AI-powered lab report analyzer",
          "complexity_score": 9,
          "impact_score": 8,
          "technologies": ["Python", "TensorFlow", "FastAPI"],
          "stars": 45
        }
      ]
    },
    "strengths": ["Strong Python expertise", "Active in AI/ML domain"],
    "areas_for_growth": ["Contribute to more open-source projects"]
  }
}

`POST /analyze/combined`

NEW: Combined resume + GitHub analysis

Request:

curl -X POST "http://localhost:8000/analyze/combined?github_username=tulika-anand" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@resume.pdf"

Use Cases:

Complete candidate assessment
Cross-validate resume claims with GitHub activity
Comprehensive technical profile evaluation

`POST /extract-text`

Utility endpoint for text extraction only

`GET /health`

Health check endpoint

`GET /` and `GET /docs`

API documentation

🚀 Getting Started

Prerequisites

Python 3.8+
Google Gemini API Key

Installation

Clone the repository:

cd c:\Users\acer\projects\GutHib

Install dependencies:

pip install -r requirements.txt

Set up environment variables:

# Copy example env file
copy .env.example .env

# Edit .env and add your Google API key
# GOOGLE_API_KEY=your_actual_api_key_here

Get Google Gemini API Key:
- Go to Google AI Studio
- Create a new API key
- Add it to your .env file

Running the Application

Development mode:

python main.py

Or using uvicorn directly:

uvicorn main:app --reload --host 0.0.0.0 --port 8000

The API will be available at:

🌐 API: http://localhost:8000
📚 Interactive Docs: http://localhost:8000/docs
📖 ReDoc: http://localhost:8000/redoc

📁 Project Structure

GutHib/
├── main.py                 # FastAPI application & endpoints
├── config.py              # Configuration management
├── models.py              # Pydantic models for validation
├── extractors.py          # Text extraction from files
├── preprocessor.py        # Text cleaning & preprocessing
├── gemini_analyzer.py     # Gemini AI integration
├── requirements.txt       # Python dependencies
├── .env                   # Environment variables (create from .env.example)
├── .env.example          # Example environment file
├── .gitignore            # Git ignore rules
├── README.md             # This file
└── uploads/              # Temporary file storage (auto-created)

🧪 Testing the API

Using cURL:

# Analyze a PDF resume
curl -X POST "http://localhost:8000/analyze" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@sample_resume.pdf"

# Analyze GitHub profile
curl http://localhost:8000/analyze/github/tulika-anand

# Combined analysis
curl -X POST "http://localhost:8000/analyze/combined?github_username=tulika-anand" \
  -F "file=@resume.pdf"

# Health check
curl http://localhost:8000/health

Using Python:

import requests

# Resume analysis
url = "http://localhost:8000/analyze"
files = {"file": open("resume.pdf", "rb")}
response = requests.post(url, files=files)
print(response.json())

# GitHub analysis
url = "http://localhost:8000/analyze/github/tulika-anand"
response = requests.get(url)
print(response.json())

Using Test Scripts:

# Test resume analysis
python test_sample.py

# Test GitHub analysis
python test_github.py

Using the Interactive Docs:

Navigate to http://localhost:8000/docs and use the built-in interface to test all endpoints.

🔧 Configuration

Edit config.py or .env file:

# Google Gemini
GOOGLE_API_KEY=your_key_here

# Server
HOST=0.0.0.0
PORT=8000
DEBUG=True

# File Upload
MAX_FILE_SIZE=10485760  # 10MB

🎯 Use Cases

Recruitment Automation: Automatically screen and rank candidates
Skill Matching: Match candidates to job requirements
Resume Optimization: Provide feedback to job seekers
Talent Analytics: Generate insights on candidate pools
ATS Integration: Enhance applicant tracking systems
🆕 GitHub Profile Verification: Validate resume claims with actual code
🆕 Developer Assessment: Evaluate coding activity and project complexity
🆕 Technical Screening: Automated technical candidate evaluation
🆕 Portfolio Analysis: Assess real-world project experience

🛣️ Roadmap

Add resume comparison feature
Implement job description matching
Add support for more file formats
Create visualization dashboard
Add database for storing analysis results
Implement batch processing
Add authentication/authorization
Deploy as containerized service

⚠️ Notes

Files are temporarily stored and automatically deleted after processing
Maximum file size: 10MB (configurable)
Gemini API usage is subject to Google's quotas and pricing
For production use, implement proper authentication and rate limiting

📝 License

This project is for educational and demonstration purposes.

🤝 Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

Built with ❤️ using FastAPI and Google Gemini

Navigation Menu

Repository files navigation

🧠 Resume Intelligence Analyzer

🎯 Project Objective

🏗️ Architecture

1️⃣ Text Extraction Layer (`extractors.py`)

2️⃣ Text Preprocessing Layer (`preprocessor.py`)

3️⃣ AI Analysis Layer (`gemini_analyzer.py`)

4️⃣ Skill Scoring Mechanism

5️⃣ API Layer (`main.py`)

📋 API Endpoints

`POST /analyze`

`GET /analyze/github/{username}`

`POST /analyze/combined`

`POST /extract-text`

`GET /health`

`GET /` and `GET /docs`

🚀 Getting Started

Prerequisites

Installation

Running the Application

📁 Project Structure

🧪 Testing the API

Using cURL:

Using Python:

Using Test Scripts:

Using the Interactive Docs:

🔧 Configuration

🎯 Use Cases

🛣️ Roadmap

⚠️ Notes

📝 License

🤝 Contributing

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Name		Name	Last commit message	Last commit date
.env.example		.env.example
.gitignore		.gitignore
GITHUB_FEATURE_SUMMARY.md		GITHUB_FEATURE_SUMMARY.md
Procfile		Procfile
README.md		README.md
config.py		config.py
extractors.py		extractors.py
gemini_analyzer.py		gemini_analyzer.py
github_analyzer.py		github_analyzer.py
github_fetcher.py		github_fetcher.py
github_models.py		github_models.py
main.py		main.py
models.py		models.py
preprocessor.py		preprocessor.py
render.yaml		render.yaml
requirements.txt		requirements.txt
test_github.py		test_github.py
test_sample.py		test_sample.py

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🧠 Resume Intelligence Analyzer

🎯 Project Objective

🏗️ Architecture

1️⃣ Text Extraction Layer (extractors.py)

2️⃣ Text Preprocessing Layer (preprocessor.py)

3️⃣ AI Analysis Layer (gemini_analyzer.py)

4️⃣ Skill Scoring Mechanism

5️⃣ API Layer (main.py)

📋 API Endpoints

POST /analyze

GET /analyze/github/{username}

POST /analyze/combined

POST /extract-text

GET /health

GET / and GET /docs

🚀 Getting Started

Prerequisites

Installation

Running the Application

📁 Project Structure

🧪 Testing the API

Using cURL:

Using Python:

Using Test Scripts:

Using the Interactive Docs:

🔧 Configuration

🎯 Use Cases

🛣️ Roadmap

⚠️ Notes

📝 License

🤝 Contributing

About

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Stars

Watchers

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Releases

Packages

Contributors

Languages

1️⃣ Text Extraction Layer (`extractors.py`)

2️⃣ Text Preprocessing Layer (`preprocessor.py`)

3️⃣ AI Analysis Layer (`gemini_analyzer.py`)

5️⃣ API Layer (`main.py`)

`POST /analyze`

`GET /analyze/github/{username}`

`POST /analyze/combined`

`POST /extract-text`

`GET /health`

`GET /` and `GET /docs`