AI Code Debugger, Explainer, Quizzer & Converter by CodeSage

Overview

This is a Flask + Gemini (Google Generative AI) application that:

1. Analyzes your code and lists mistakes (syntax, logic, redundancy), with explanations.
2. Stores short labels of detected issues into bug_log.csv.
3. Generates a 15-question quiz from your recent issues and shows correct answers.
4. Converts code between Python, C, C++, and Java.

(Includes a login prompt UI for demo only — no backend connected.)

Screenshots:

Home Page with a python code as example

Quiz Page (Loading State)

Quiz Page

Converter Page (Loading State)

Converter Page

Project Structure

ai-debugger/
├── app.py                 - Flask backend (Gemini integration)
├── bug_log.csv            - CSV log of short issue labels (auto-updated)
├── index.html             - Chat-style code analysis view
├── index-script.js        - Chat page logic
├── quiz.html              - Quiz page
├── quiz-script.js         - Quiz logic
├── conversion.html        - Code converter page
└── con-script.js          - Converter logic

Prerequisites

• Python 3.9+ recommended
• A Google AI Studio API key for Gemini

1) Set your API key

The backend reads an environment variable named GEMINI_API_KEY. (if you have set it up if not just follow option A) Alternatively, app.py has a fallback "Your_API_Key" (change it if you prefer hardcoding although env vars are safer).

Option A —

Edit app.py (Recommended for development purpose):

• In app.py,

• FIND:

  GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY", "Your_API_Key")

• Replace--

  "Your_API_Key" with your actual key generated from Google Ai studio.

Option B —

Environment variable (recommended for deployment, not recommended for development stage):

• Windows (PowerShell):

  • setx GEMINI_API_KEY "YOUR_KEY_HERE"
  • Close and reopen your terminal after setx; or for current session:
  • $env:GEMINI_API_KEY="YOUR_KEY_HERE"

• macOS/Linux (bash/zsh):

  • export GEMINI_API_KEY="YOUR_KEY_HERE"

2) Install dependencies

First, ensure you are in the project directory that contains "requirements.txt" (In terminal).

Then run:

pip install -r requirements.txt

3) Run the backend server

From the folder that has app.py, run: (in Terminal)

python app.py

By default this starts Flask on http://127.0.0.1:5000 (development server).

4) Open the frontend

• Open the HTML files directly (double-click).
  Note: It is recommended that you keep all files in same folder

Usage Notes

• The login prompt is only a placeholder UI. It has no backend functionality since the project is not deployed.
• The chat page sends your code to /explain-code and lists bullet-pointed issues. It also logs short "bug summaries" to bug_log.csv for quiz creation.
• The quiz page reads the latest 15 summaries from bug_log.csv and requests a quiz from the backend.
• The converter page calls /convert-code; supports Python, C, C++, and Java.
• If you ever delete bug_log.csv, it will be re-created automatically once you analyze new code.

Environment / Paths

• The backend writes to "bug_log.csv" in the working directory. Make sure the process has write permissions.
• If deploying to stateless platforms, consider storing logs in a database or mounted volume.

Endpoints (for reference)

• POST /explain-code

  json: {"code": ""}
  resp: {"explanation": "...", "bug_summaries": ["...", "..."]}

• GET /bug-log

  resp: {"log": ["...", "..."]}

• GET /generate-quiz

  resp: {"quizzes": [ { "question": "Q1 ...", "options": ["A","B","C","D"], "answer": "A" }, ... ] }

• POST /convert-code

  json: {"code": "...", "from_lang": "python|c|cpp|java", "to_lang": "python|c|cpp|java"} resp: {"converted_code": "..."}

Troubleshooting

• 401/403 errors: check GEMINI_API_KEY and billing/quotas in Google AI Studio.
• CORS errors: ensure Flask is running and you used a local static server (http://localhost:5500).
• Empty quiz: analyze some code first so bug_log.csv has entries.

License

BSD 3-Clause (see LICENSE file).

Credits

Built by TEAM CodeSage. See Contributors for full credits.