AryanVBW/ai_cor

Coursera Study Buddy

A web application that helps you interact with Coursera video transcripts using AI. Features include summarization, question-answering, question generation, and exam preparation.

Features

• Upload Coursera Transcripts: Upload PDF transcripts from Coursera courses.
• Generate Summaries: Get concise summaries of course content by week.
• Ask Questions: Chat with an AI that answers questions based on course content.
• Generate Quiz Questions: Create customized quiz questions from course material.
• Exam Preparation: Generate practice exams tailored to course content.

Technologies Used

• Frontend: Streamlit
• Backend: Python
• AI/ML: OpenAI API, LlamaIndex for RAG (Retrieval-Augmented Generation)
• Storage:
  • Supabase (metadata & file storage)
  • Pinecone (vector database for embeddings)

Setup

Prerequisites

• Python 3.9+
• Accounts on:
  • OpenAI (API key with credits)
  • Supabase (free tier)
  • Pinecone (free tier)

Installation

1. Clone this repository:

   git clone <repository-url>
   cd coursera-study-buddy
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up environment variables:

   Create a .env file in the project root with the following:

   OPENAI_API_KEY=your-openai-api-key
   SUPABASE_URL=your-supabase-url
   SUPABASE_KEY=your-supabase-key
   PINECONE_API_KEY=your-pinecone-api-key
   PINECONE_ENVIRONMENT=gcp-starter
   

Supabase Setup

1. Create a new project on Supabase
2. Create a storage bucket called transcripts
3. Create a table called transcripts with the following schema:

CREATE TABLE transcripts (
  id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
  course_name TEXT NOT NULL,
  week_number INTEGER,
  transcript_name TEXT NOT NULL,
  file_path TEXT NOT NULL,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

Pinecone Setup

1. Create a new index named coursera-transcripts
2. Use cosine as the similarity metric
3. Use dimension of 1536 (for OpenAI embeddings)

Running the Application

Start the Streamlit app:

streamlit run app.py

The application will be available at http://localhost:8501

Usage

1. Upload Tab: Upload Coursera PDF transcripts with course name, week, and lecture name.
2. Summarize Tab: Generate summaries of uploaded transcripts by course and week.
3. Ask Questions Tab: Ask specific questions about course content.
4. Generate Quiz Tab: Create custom quiz questions based on course material.
5. Exam Prep Tab: Generate practice exams simulating Coursera quizzes.

Deployment

Streamlit Cloud (Free)

1. Push your code to GitHub
2. Sign up for Streamlit Cloud
3. Create a new app pointing to your GitHub repository
4. Add your environment variables in the Streamlit Cloud dashboard

Limitations

• Free tier limits:
  • Supabase: 500MB storage (sufficient for ~50 PDF transcripts)
  • Pinecone: Limited to one index in the free tier
  • OpenAI API: Requires a paid API key (~$1-2/month for typical usage)

Troubleshooting

Supabase File Upload Issues

If you're experiencing issues with file uploads to Supabase:

1. Check Supabase credentials:

   • Verify your SUPABASE_URL and SUPABASE_KEY are correct
   • Ensure you're using the "anon" key (public) for the SUPABASE_KEY

2. Bucket permissions:

   • In the Supabase dashboard, go to Storage → Buckets → transcripts
   • Check that bucket policies allow uploads (RLS policies)
   • You may need to temporarily set the bucket to public during testing

3. File path issues:

   • If complex paths fail, try uploading directly to the root of the bucket
   • Use the "upsert" option for replacing existing files

4. Check environment setup:

   • For local development: Make sure .env file exists with correct credentials
   • For Streamlit Cloud: Ensure secrets are properly configured

5. Debugging:

   • Add debug print statements to view Supabase responses
   • Check the app logs for specific error messages

Other Issues

If you experience other problems:

1. Enable debug mode:

   streamlit run app.py --logger.level=debug
   

2. Check API rate limits:

   • OpenAI and Pinecone have rate limits on free/starter tiers
   • Space requests out if hitting limits

3. Large PDF files:

   • PDF files over 10MB may cause memory issues
   • Consider splitting large transcripts

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. # Coursera Study Buddy