🚀 LeetCode Question Helper - AI-Powered DSA Assistant

📌 Overview

The LeetCode Question Helper is an AI-powered teaching assistant that helps users understand and solve DSA (Data Structures & Algorithms) problems. The application enables users to:

• Submit a LeetCode problem link and ask doubts about it.
• Receive guided explanations with hints and approach breakdowns instead of direct answers.
• Engage in an interactive chat-based UI to explore concepts in an intuitive way.
• Get structured responses with headings, subheadings, code snippets, and optimizations.

Demo Link - Link

📚 Example AI Prompts for LeetCode Helper

These prompts ensure users develop their own problem-solving skills rather than just receiving direct solutions.

🔍 Overview of the Problem

"Before we dive into solving the problem, let's break it down step by step. What is the problem asking you to find? Can you rephrase it in your own words?"

💡 Encourages users to internalize the problem rather than copying the question.

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🧠 Building Intuition

"Let's relate this problem to a real-world scenario. Imagine you are at a store, and you need to find two products that add up to a certain price. How would you do it efficiently without checking every possible combination?"

💡 Uses analogies to simplify abstract DSA concepts.

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🚀 Suggested Approaches

"How would you approach this problem using brute force? Can you write a simple algorithm that checks every possible combination?"

"Now, think about optimizing it. Is there a way to avoid unnecessary comparisons? Can you use a data structure like a hash table or a sorting technique to speed up the process?"

💡 Encourages users to compare brute force vs optimized solutions.

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🛠️ Guidance for Implementation

"Let's structure the solution step by step before writing code. What would be the input and output of your function? Can you define it in plain English?"

"Now, let's write pseudocode before jumping into actual implementation. How would you translate your plan into simple logical steps?"

💡 Encourages structured thinking before implementation.

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⚡ Tips for Optimization

"Your current solution works, but can we improve it? What is the time complexity of your approach? Can you reduce it from O(n²) to O(n) or O(log n)?"

"Are there alternative ways to solve this problem using different algorithms or data structures? Could sorting, dynamic programming, or greedy techniques help?"

💡 Introduces big-O complexity analysis and alternative methods.

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⚠️ Common Pitfalls and Edge Cases

"Many developers make mistakes when solving this type of problem. Can you identify what might go wrong in an incorrect implementation?"

"What test cases would you use to verify your solution? Have you considered edge cases such as duplicate values, negative numbers, or large inputs?"

💡 Develops debugging skills and encourages self-checking.

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Run Locally

Clone the project

  git clone <repo_url>

Go to the project directory

  cd repo-name

Create a Virtual Environment

python -m venv venv  # Create a virtual environment
source venv/bin/activate  # Activate it (For Windows: venv\Scripts\activate)
pip install -r requirements.txt  # Install dependencies

Set Gemini API Key in config.py

  GEMINI_API_KEY = "your_api_key"

Start the app.py

  python3 app.py

Go to the frontend of the project directory

  cd frontend

Install dependencies

  npm install

Start the frontend server

  npm start

Project Architure

🖥️ Backend (Flask)

The backend is built using Flask and is responsible for handling user requests, processing AI responses, and interacting with Gemini AI.

File	Description
app.py	Main backend server that handles API requests.
gpt_handler.py	Communicates with Google Gemini AI to process and format responses.
config.py	Stores configurations like API keys securely.
requirements.txt	Lists Python dependencies needed for the backend.

💡 How It Works:

1. The user submits a LeetCode problem link and a question.
2. The backend processes the request and forwards it to Gemini AI.
3. Gemini AI returns a structured response with explanations, hints, and optimizations.
4. The backend sends the AI response to the React frontend.

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🌐 Frontend (React)

The frontend is built using React.js and provides an interactive chat interface for users to engage with the AI assistant.

File	Description
App.js	Main React application that connects frontend and backend.
index.js	Entry point for rendering the React app.
components/QuestionInput.js	Handles user input (LeetCode URL & question).
components/ResponseDisplay.js	Displays structured AI-generated responses.
components/ResponseDisplay.css	Styles the response section for better readability.
package.json	Manages frontend dependencies (React, Tailwind, etc.).
public/	Contains static assets for the frontend.

💡 How It Works:

1. The user enters a question and optionally provides a LeetCode link.
2. The frontend sends the request to the Flask backend.
3. The backend processes it using Gemini AI and returns a structured response.
4. The response is formatted and displayed in a chat-like UI.

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🚀 Why This Architecture?

✅ Separation of Concerns: The frontend and backend are independent, making it easier to maintain and scale.
✅ Scalability: Can integrate more AI features without major changes.
✅ Modular Components: The frontend is built with React components for reusability.
✅ AI-Powered Responses: Uses Google Gemini AI for intelligent DSA guidance.

This structure ensures that LeetCode-Helper is efficient, scalable, and user-friendly. 🚀🔥

🔥 How to Use the Application

1. Enter a LeetCode problem link in the input field.
2. Describe your doubt (e.g., "How do I optimize this problem?").
3. Click Submit to receive an AI-generated structured response.
4. The AI will guide you with hints instead of giving direct answers.
5. Modify your doubt if you need further clarifications.
6. Change the LeetCode problem link anytime to ask about another question.

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🤖 How the GPT Integration Works

• The Gemini AI model is used as the backend AI assistant.
• The AI is prompted with structured guidance instructions to:
  • ✅ Encourage conceptual understanding.
  • 🚀 Avoid direct solutions but offer hints & intuition building.
  • 💡 Provide approaches, edge cases, and optimization techniques.
• The AI parses & returns structured responses in a readable format.

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