CanineCare-AI-Vet-for-Dog-Breeds

CanineCare - AI Vet For Dog Breeds

This repository explores using computer vision and machine learning to improve dog care. It includes tools for identifying dog breeds from images, predicting diseases, and recommending the best breed for potential owners. By analyzing images and data, these tools help veterinarians provide better care and help people choose the right dog for their lifestyle, ensuring healthier and happier dogs and owners.

Canine Care and Management System

Overview

This project leverages computer vision and machine learning to improve dog care through three main functionalities: dog breed classification, disease prediction, and breed recommendation. The application is built using Flask and integrates TensorFlow and Scikit-learn models to deliver these functionalities.

Features

1. Dog Breed Classification
   • Identifies dog breeds from images using a pre-trained TensorFlow model.
2. Disease Prediction
   • Predicts potential diseases based on symptoms using a Scikit-learn model.
3. Breed Recommendation System
   • Recommends dog breeds suitable for different lifestyles and preferences.

Requirements

• Flask
• TensorFlow
• TensorFlow Hub
• Scikit-learn
• Joblib
• OpenCV
• NumPy
• Pandas

Installation

1. Clone the repository:

    git clone https://github.com/ssbaraar/CanineCare-AI-Vet-for-Dog-Breeds.git
    cd CanineCare-AI-Vet-for-Dog-Breeds
   

2. Create a virtual environment and activate it:

    python3 -m venv venv
    source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   

3. Install the dependencies:

    pip install -r requirements.txt
   

4. Ensure you have the required model files and place them in the model/ directory:
   • TensorFlow model: model/20220804-16551659632113-all-images-Adam.h5
   • Scikit-learn model: model/dogModel1.pkl
5. Ensure you have the required data file and place it in the data/ directory:
   • Data file: data/dog_data_09032022.csv

Running the Application

1. Start the Flask application:

    python app.py
   

2. Open your web browser and navigate to http://127.0.0.1:5000/.

Screenshots

Home Page

Dog Breed Classification Page

Dog Breed Recommendation Page

Predict Disease Page

Endpoints

Home

• URL: /
• Methods: GET, POST
• Description: Renders the home page where users can upload an image to predict the dog breed.

Predict Dog Breed

• URL: /predict_breed_route
• Methods: POST
• Description: Accepts an image file and returns the predicted dog breed.
• Request:
  • file: Image file of the dog.
• Response:

    {
        "predicted_breed": "Golden Retriever"
    }
  

Predict Disease

• URL: /predict_disease
• Methods: POST
• Description: Accepts symptoms data and returns the predicted disease.
• Request:

    {
        "symptoms": [0.1, 0.3, 0.5, ...]
    }
  

• Response:

    {
        "disease": "Tick fever"
    }
  

File Structure

│
├── app.py # Main Flask application
├── model/
│   ├── 20220804-16551659632113-all-images-Adam.h5 # TensorFlow model
│   └── dogModel1.pkl # Scikit-learn model
├── data/
│   └── dog_data_09032022.csv # Data file for preprocessing
├── templates/
│   └── Home.html # HTML template for the home page
├── static/
│   └── style.css # CSS file for styling
├── requirements.txt # List of Python dependencies
└── README.md # Project documentation