🐶 Woof: Stray Dog Reporting & Adoption System

🏗️ Technical Architecture

Woof is built using a modern Microservices Architecture with Spring Boot, ensuring scalability and separation of concerns.

• woof-gateway: API Gateway built with Spring Cloud Gateway routing frontend requests (http://localhost:8000) to the respective backend microservices.
• service-reg: Netflix Eureka Server (http://localhost:8761) for central service discovery.
• woof-analysis-service: Handles image AI condition analysis (via Groq API), serves the frontend UI (http://localhost:8080), and calculates DBSCAN hotspot clusters.
• woof-adoption-service: Manages the end-to-end lifecycle of reported dogs (State machine: REPORTED -> NEEDS_TREATMENT -> IN_SHELTER -> AVAILABLE_FOR_ADOPTION -> ADOPTED). Exposes the Shelter Manager Dashboard and Adoption Gallery APIs.
• woof-payment-service: Handles user donations and blockchain simulation.
• woof-user-service: Handles user accounts and authentication profiles.

🚀 How to Run Locally

Ensure Docker Desktop is running on your machine.

1. Clone the repository and navigate to the project root.

2. Build and start all microservices:

   docker-compose up -d --build

3. Open your browser and navigate to:

   • Main Portal: http://localhost:8080
   • Shelter Dashboard: http://localhost:8080/static/shelter_dashboard.html
   • Adoption Gallery: http://localhost:8080/static/adoption_gallery.html

4. To stop the application:

   docker-compose down

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📝 1. User Reporting System

📌 Report Stray Dogs with Ease

Users can report stray dogs by providing the following details:

• Location: Auto-detect via GPS or manual pin selection.
• Photo Upload: Verify the dog's condition with an image.
• Vision AI Model for Condition Analysis: AI-based analysis of the dog's health, classifying the condition into:
  • 🟢 Healthy
  • 🟡 Mildly Injured/Sick
  • 🟠 Moderately Injured/Sick
  • 🔴 Critical Condition

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🌍 2. AI-Based Hotspot Detection

🔍 AI-Based Hotspot Detection Using DBSCAN

📌 Approach Overview

Our enhanced DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm prioritizes severity while also considering older reports. This ensures detected hotspots accurately reflect real-world conditions.

🚀 Key Features

1. Density-Based Clustering (DBSCAN) Categorizes reports into Core Points, Border Points, and Noise Points based on radius ε and MinPts.
2. Severity Weighting Reports are weighted based on severity, giving higher priority to more critical cases.
3. Time Decay Factor Older reports gradually lose their influence using an exponential decay function.
4. Custom Weighted Distance Function A modified distance function incorporates severity-based weighting, ensuring high-severity points cluster closely.

📊 Visualization with Folium

Clusters are plotted on an interactive Folium map:

• 🔴 Red = Emergency (High Priority)
• 🟠 Orange = Injured (Medium Priority)
• 🟢 Green = Healthy (Low Priority)

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🏡 3. Shelter Management & Adoption System

🏥 Shelter Dashboard

• Lifecycle Management: Shelter workers track reported dogs from street to adoption via a managed state machine.
• Medical Updates: Transition statuses from NEEDS_TREATMENT (clinic) to IN_SHELTER (recovering) and finally to AVAILABLE_FOR_ADOPTION.

🌟 Adoption Gallery

• Choose Your Perfect Companion: Browse a dynamic gallery of rehabilitated dogs ready for a forever home.
• Detailed Profiles: View images, medical history, age, breed, and temperament.
• Submit Adoption Requests: Contact shelters directly regarding specific dogs seamlessly through the integrated platform UI.

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🎉 Start your journey to finding a loving furry friend and making a difference today!