app.py

import gradio as gr
import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing import image
from PIL import Image

# Load the trained model
MODEL_PATH = "setosys_dogs_model.h5"
model = tf.keras.models.load_model(MODEL_PATH)

# Image preprocessing function
def preprocess_image(img: Image.Image) -> np.ndarray:
    """Preprocess the image to match the model's input requirements."""
    img = img.resize((224, 224))  # Resize image to model input size
    img_array = image.img_to_array(img)
    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
    img_array /= 255.0  # Normalize pixel values
    return img_array

# Prediction function
def predict_dog_breed(img: Image.Image) -> dict:
    """Predict the breed of the dog in the uploaded image."""
    img_array = preprocess_image(img)
    predictions = model.predict(img_array)
    class_idx = np.argmax(predictions)  # Index of the highest prediction probability
    confidence = float(np.max(predictions))  # Confidence score

    # Get class labels from the model
    class_labels = model.classes_ if hasattr(model, 'classes_') else None
    
    # If class labels are available, return the predicted breed with confidence score
    if class_labels is not None:
        predicted_breed = class_labels[class_idx]
    else:
        predicted_breed = "Unknown"
    
    return {predicted_breed: confidence}

# Create Gradio interface
interface = gr.Interface(
    fn=predict_dog_breed,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(),
    title="Dog Breed Classifier",
    description="Upload an image of a dog to predict its breed."
)

# Launch the Gradio app
if __name__ == "__main__":
    interface.launch()