⚙️ GenEx — AI-Powered Requirement Extraction Tool

🚀 Built during the Barclays Hackathon – Hack-O-Hire

GenEx is a full-stack web application that automates the extraction and generation of software requirements from various document types such as PDF, DOCX, Excel, audio, and video files. It combines Natural Language Processing (NLP), Whisper (for transcription), and custom rule-based logic to first extract and categorize functional and non-functional requirements, and then assign MoSCoW priorities to them. Users can also export the extracted requirements as PDF, Excel, or JSON documents for further use.

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🔑 Key Features

1. Upload documents (PDF, DOCX, XLSX) or media (audio/video).
2. AI-powered extraction of clear and structured requirements.
3. Categorization into functional and non-functional requirements with MoSCoW prioritization.
4. Role-based access and user management.
5. Visualization of extracted requirements.

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🛠️ Tech Stack

• Frontend: React.js + Vite
• Backend: Node.js + Express
• Python Engine: PyPDF2, Whisper, pandas, and other NLP libraries
• Database: MongoDB Atlas

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🧪 How to Run the Project

1. Clone the repository

   git clone https://github.com/your-username/GenEx.git

2. Add environment variables
   Fill in the .env files inside both backend2/ and project/ folders with appropriate config values.

3. Install dependencies

   # For frontend and project folder
   cd project
   npm install
   cd ../backend2
   npm install
   pip install -r requirements.txt

4. Install Whisper (Local) and FFmpeg

   • Install OpenAI Whisper locally.
   • Make sure ffmpeg is installed and added to your system path.

5. Start the servers

   • Run backend and frontend servers.
   • Login using:

     Email: sample1234@gmail.com
     Password: 12345678
     

6. Create a new project
   Use the sidebar to create a new project, scroll down to upload your documents, and click run.

7. Troubleshooting
   If a Python file is missing or not found, install it manually.

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🎥 Demo Video

🔔 Tip: Enable subtitles or use the video timestamps for a clearer understanding of the process.

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