🧠 Virtual Teaching Assistant – Server (Flask Backend)

This is the Flask-based backend for the Virtual Teaching Assistant platform, a smart educational system that uses AI to transcribe lectures, generate summaries, create quizzes, and parse learning materials.

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🚀 Features

• 🎤 Audio Upload & Transcription – Accept .wav files and transcribe using OpenAI Whisper
• 🧾 AI-Powered Summarization – Summarize transcripts using LLMs (LLaMA 3 via Groq API)
• 🧪 Quiz Generation – Extract questions from lecture content with retry logic
• 📂 PDF Parsing – Extract text from uploaded PDFs using pdfplumber
• 🔗 RESTful API – Clean endpoints for frontend interaction
• 🌐 Cross-Origin Support – Enabled via Flask-CORS
• 🔐 Environment-based Config – Secure API keys via .env

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🛠️ Tech Stack

Category	Tech/Library
🖥️ Server Framework	Flask, waitress (production WSGI)
🎶 Audio Processing	ffmpeg, whisper, pydub, noisereduce, soundfile, scipy
💬 LLM Integration	langchain, langchain_groq, langchain_experimental, llama3
📝 NLP Utilities	Custom prompt chaining via langchain
📄 File Parsing	pdfplumber for PDF content extraction
🔐 Environment Mgmt	python-dotenv
🌍 CORS Handling	Flask-CORS

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🔧 Setup Instructions

✅ Prerequisites

• Python 3.9+
• FFmpeg installed (for audio preprocessing)
• .env file with your Groq API key

🧪 Installation

# Clone and enter project
cd server-python

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

📄 .env Example

GROQ_API_KEY=your_groq_api_key_here

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▶️ Running the Server

python app.py

Runs at: http://localhost:5000

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🌐 API Endpoints

Method	Endpoint	Description
📤 POST	/getTranscript	Upload .wav file and generate summary + quiz
📝 POST	/summary	Generate summary JSON from transcript
🧪 POST	/quiz	Generate quiz JSON from transcript
📄 POST	/pdf	Extract raw text from uploaded PDF file

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🧪 Testing & Debugging

• Use Swagger alternatives like Postman to test endpoints.
• Debug statements included in logs for API calls to internal services.

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🛡️ Production Deployment

You can deploy using:

• waitress (already integrated)
• Docker (optional Dockerfile)
• Render, Heroku, or any Linux server with Python 3.9+

Example production run:

waitress-serve --host 0.0.0.0 --port 5000 app:app

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🤝 Contributing

We welcome all contributors! You can:

• 🔍 File bugs
• 🌱 Suggest improvements
• 📦 Submit pull requests

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👩‍🏫 Let's build better classrooms with AI — one lecture at a time! 🚀