Perfect Pathway

An AI-Assisted Decision-Making and Path Optimization System

Overview

Intelligent simulation system that uses AI algorithms to find optimal paths in real-world environments, considering risk, danger zones, and role-specific strategies.

Features

• AI Pathfinding - A* Search with risk-weighted costs
• Risk Prediction - ML-based (Logistic Regression) danger assessment
• Role-Based Logic - Army (safe), Rescuer (balanced), Volunteer (efficient)
• Danger Zones - Configurable enemy camps that Army role avoids
• Mission Briefings - AI-generated (Gemini) tactical narratives
• Real Map Data - OpenStreetMap integration via OSMnx

Quick Start

# 1. Activate virtual environment
.\.venv\Scripts\Activate.ps1

# 2. Install dependencies
pip install -r requirements.txt

# 3. (Optional) Set Gemini API key for AI briefings
# Create .env file with: GEMINI_API_KEY=your_key_here

# 4. Run the app
streamlit run app.py

Project Structure

A Perfect Pathway/
├── src/
│   ├── ai/           # Pathfinding, Risk Model, Mission Narrator
│   ├── roles/        # Army, Rescuer, Volunteer role classes
│   ├── environment/  # Map downloader, Graph enricher
│   └── utils/        # Visualizer
├── docs/             # Phase documentation
├── config.py         # Settings (map center, danger zones, API keys)
└── app.py            # Streamlit UI

Usage

1. Select a Role (Army/Rescuer/Volunteer)
2. Choose Source and Destination streets
3. Click Plan Mission or Random
4. View the path on the map with role-specific colors
5. Read the auto-generated Mission Briefing

Configuration

Edit config.py to:

• Change map center coordinates
• Add/remove danger zones
• Set Gemini API key

Technologies

NetworkX | scikit-learn | OSMnx | Folium | Streamlit | Google Gemini

License

Academic project for demonstration purposes.