🧙‍♂️ Connect 4 — Harry Potter Edition (AI Course Project)

This repository is an AI course project: a Harry-Potter-themed Connect Four web game with an intelligent opponent.

---

✨ Features

• 🎮 Web game (Flask) with an interactive 6×7 Connect Four board.
• 🧠 Multiple AI options:
  • Minimax
  • Minimax v2 (alternative evaluation)
  • Minimax + Alpha–Beta pruning
  • Minimax v2 + Alpha–Beta pruning
  • Heuristic / Heuristic v2 (fast greedy)
• 🎚️ Difficulty levels mapped to search depth:
  • Easy → depth 2
  • Medium → depth 4
  • Hard → depth 7
• ⚡ Harry Potter vibe:
  • Intro video + music
  • HP font and themed assets

---

🧩 Tech Stack

• Python 3
• Flask (backend / routes)
• NumPy (board representation & computation)
• HTML/CSS/JS (frontend)

---

🚀 Getting Started

1) Clone the project

git clone <YOUR_REPO_URL>
cd An-Intelligent-Connect-Four-Player-main

2) Create a virtual environment (recommended)

python -m venv .venv
# Windows:
.venv\Scripts\activate
# macOS / Linux:
source .venv/bin/activate

3) Install dependencies

If you have a requirements.txt:

pip install -r requirements.txt

Otherwise install directly:

pip install flask numpy

4) Run the app

python app.py

Then open:

• http://127.0.0.1:5000/

---

✅ Important Note (Quick Fix)

If the app fails immediately with an import error, ensure app.py uses render_template (Flask’s correct function name):

Replace:

from flask import Flask, render_templates, request, jsonify
...
return render_templates("index.html")

With:

from flask import Flask, render_template, request, jsonify
...
return render_template("index.html")

---

🎯 How to Play

1. Start from the landing page / then click Start Game.
2. Choose:
   • Algorithm
   • Difficulty
   • Your character & AI opponent
3. Click any column to drop your piece.
4. The AI responds using the selected algorithm.

Board encoding:

• 0 → empty
• 1 → human player
• 2 → AI

---

🤖 AI Details (High Level)

The AI lives in connect4.py and supports:

• Minimax: explores the game tree up to a chosen depth and picks the move that maximizes the AI score.
• Alpha–Beta Pruning: speeds up minimax by skipping branches that cannot affect the final decision.
• Two evaluation strategies:
  • score_position() (classic window scoring + center preference)
  • future_chance_score() (counts future chances / open windows)

---

🔌 API Endpoint (for the AI move)

The frontend calls:

• POST /move

Payload:

{
  "board": [[0,0,0,0,0,0,0], ...],
  "difficulty": "MED",
  "algorithm": "alpha_beta"
}

Response:

{ "column": 3 }

Supported algorithm values:

• minimax
• minimax2
• alpha_beta
• alpha_beta2
• hurestic
• hurestic2

---

📁 Project Structure

.
├─ app.py                 # Flask app (routes + AI selection)
├─ connect4.py            # Game logic + AI algorithms
├─ templates/
│  ├─ index.html          # Intro page (video + start)
│  └─ game.html           # Game UI & settings
└─ static/
   ├─ css/                # Styles (Harry Potter theme)
   ├─ js/                 # Frontend logic
   ├─ images/             # Characters, icons, backgrounds
   ├─ sounds/             # Music & effects
   └─ video/              # Intro video

---

📌 Credits / Academic Use

This project is made for educational purposes as part of an AI course.