Update README.md

README.md

@@ -5,6 +5,120 @@ colorFrom: indigo
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 sdk: docker
 pinned: false
+app_port: 7860
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🎯 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.