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---
title: ResumeDataExtractor
emoji: 🦀
colorFrom: indigo
colorTo: pink
sdk: docker
pinned: false
app_port: 7860
---
# 🎯 AI Smart Resume Screen & Extractor
**ResumeDataExtractor** is an intelligent Application Tracking System (ATS) tool powered by **Google Gemini AI**. It parses PDF resumes, extracts structured data, and compares candidates against specific job descriptions to provide a match score, reasoning, and skill gap analysis.
🔗 **Live Demo:** [Hugging Face Space](https://huggingface.co/spaces/LovnishVerma/ResumeDataExtractor)
---
## 🚀 Key Features
* **📄 PDF Parsing**: extract raw text from PDF resumes reliably.
* **🤖 AI Analysis**: Uses Google's **Gemini 1.5 Pro/Flash** to interpret candidate data.
* **📊 Smart Scoring**: Compare a Resume against a Job Description (JD) to get a 0-100% match score.
* **🧩 Skill Gap Analysis**: Automatically identifies **Matching Skills** and **Missing Skills** based on the JD.
* **⚡ Hybrid Architecture**: Runs a **FastAPI** backend for logic/API processing and a **Streamlit** frontend for the UI in a single container.
---
## 🛠️ How It Works
This project uses a microservices-in-a-box approach:
1. **Backend (`main.py`)**: A **FastAPI** server running on port `8000`. It handles file uploads, text extraction, and communicates with the Google Gemini API.
2. **Frontend (`app.py`)**: A **Streamlit** dashboard running on port `7860`. It accepts user input and sends requests to the local backend.
3. **AI Engine**: The system dynamically selects the best available Gemini model (e.g., `gemini-1.5-flash` or `gemini-pro`) to process the text.
---
## 💻 Local Setup & Installation
### Prerequisites
* Python 3.11+
* A Google Gemini API Key ([Get it here](https://aistudio.google.com/app/apikey))
### 1. Clone the Repository
```bash
git clone [https://huggingface.co/spaces/LovnishVerma/ResumeDataExtractor](https://huggingface.co/spaces/LovnishVerma/ResumeDataExtractor)
cd ResumeDataExtractor
```
### 2. Install Dependencies
```bash
pip install -r requirements.txt
```
### 3. Set Environment Variables
Create a `.env` file in the root directory:
```env
GEMINI_API_KEY=your_actual_api_key_here
```
### 4. Run the Application
You can run the startup script (Linux/Mac/WSL) which launches both services:
```bash
chmod +x start.sh
./start.sh
```
**Or run them manually in separate terminals:**
*Terminal 1 (Backend):*
```bash
uvicorn main:app --host 0.0.0.0 --port 8000
```
*Terminal 2 (Frontend):*
```bash
streamlit run app.py --server.port 7860
```
Access the UI at: `http://localhost:7860`
---
## 🐳 Docker Deployment
This project is configured to run effortlessly in Docker (standard for Hugging Face Spaces).
```bash
# Build the image
docker build -t resume-extractor .
# Run the container (Pass your API Key)
docker run -p 7860:7860 -e GEMINI_API_KEY="your_key" resume-extractor
```
---
## 📂 Project Structure
* **`app.py`**: Streamlit frontend interface.
* **`main.py`**: FastAPI backend server.
* **`parser_logic.py`**: Core logic for PDF extraction and interaction with Google Gemini.
* **`start.sh`**: Entry point script to run both servers simultaneously.
* **`Dockerfile`**: Container configuration.
---
## 🛡️ License & Disclaimer
This project uses Google Generative AI. Ensure you comply with their usage policies. Resume data is processed in memory and not permanently stored on the server.
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