TerpOracle analyzes a University of Maryland (UMD) student's class schedule to provide comprehensive, AI-powered feedback and scores across several key categories, helping students make more informed decisions about their semester.

• Dual Input: Analyze schedules by uploading an image or entering course/section details manually.
• AI Analysis: Leverages Google Gemini (2.0 Flash) to generate insights on:
  • Overall Workload
  • Professor Quality (using PlanetTerp data)
  • Schedule Balance
  • Subject Synergy
  • Difficulty Management
  • Overall Schedule Quality & Grade
• Data Integration: Combines information scraped from UMD Testudo and fetched from the PlanetTerp API.
• Concurrent Processing: Uses asyncio to generate individual course analyses in parallel for faster results.
• Web Interface: Simple, clean UI built with Flask and vanilla HTML/CSS/JS.

1. Input: User provides a schedule image or enters courses manually, selects the term, and enters their Google Gemini API Key via the web UI.
2. Backend (Flask): Receives the request, handles input (image processing or JSON parsing).
3. Core Script (enhanced_schedule_analyzer.py):
   • Extracts courses (if image provided) using Gemini Vision.
   • Scrapes Testudo for official course details (professor, time).
   • Fetches professor reviews/ratings from PlanetTerp API.
   • Sends data to Gemini 2.0 Flash for concurrent individual course analysis.
   • Sends individual summaries to Gemini 2.0 Flash for overall schedule analysis and grade.
   • Outputs results to a JSON file.
4. Backend (Flask): Reads the resulting JSON.
5. Frontend: Receives the JSON data and displays the formatted analysis, overall grade, and individual course breakdowns.

1. Clone the Repository (or ensure you have the files):

   # If you haven't already:
   # git clone https://github.com/andrewxie04/terporacle.git
   # cd terporacle

2. Install Dependencies:

   • Python: Ensure you have Python 3 installed.
   • Flask & Requests:

     pip install Flask Werkzeug requests beautifulsoup4 Pillow google-generativeai
     # Or potentially: pip3 install ...

3. Get Gemini API Key:

   • Obtain an API key from Google AI Studio (click "Create API key").

4. Run the Flask Server:

   • Navigate to the schedule-frontend directory:

     cd schedule-frontend

   • Run the server:

     python3 app.py
     # Or: python app.py

5. Access the App:

   • Open your web browser and go to http://localhost:5001 (or the address shown in the terminal).

6. Use the App:

   • Enter your Gemini API key.
   • Select the desired term.
   • Choose either "Upload Image" or "Enter Manually".
   • Provide the schedule input.
   • Click "Analyze Schedule".

• Python: Core logic, backend server.
• Flask: Web framework for the backend API.
• Google Gemini API: For vision (image extraction) and text generation (analysis).
• Requests: For making HTTP requests to Testudo and PlanetTerp.
• BeautifulSoup4: For parsing HTML scraped from Testudo.
• Pillow: For image handling (if using image upload).
• Asyncio: For concurrent Gemini API calls.
• HTML, CSS, JavaScript: For the frontend web interface.
• Git & GitHub: For version control and hosting.