app.py

import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image

# Load the custom classification models 
transfer_learning_model = tf.keras.models.load_model('model_vgg16.keras')

# Class names
class_names = ['butterfly', 'cat', 'elephant', 'horse', 'squirrel']

def classify_image(image, model):
    # Convert the Gradio input image to a PIL image
    if isinstance(image, np.ndarray):
        image = Image.fromarray(image.astype('uint8'), 'RGB')
    
    # Resize the image using np.resize
    image = np.resize(image, (300, 300, 3))  # Add the channel dimension
    
    image = image / 255.0  # Normalize the image
    image = np.expand_dims(image, axis=0)  # Add batch dimension
    
    # Predict the class of the image
    predictions = model.predict(image)
    
    # Get the indices of the top 3 predictions
    top_indices = np.argsort(predictions[0])[::-1][:3]
    
    # Get the corresponding class names and confidences
    top_classes = [class_names[i] for i in top_indices]
    confidences = [predictions[0][i] for i in top_indices]
    
    return {class_name: float(confidence) for class_name, confidence in zip(top_classes, confidences)}

image_input = gr.Image()
label = gr.Label(num_top_classes=3)

transfer_learning_interface = gr.Interface(
    fn=lambda image: classify_image(image, transfer_learning_model), 
    inputs=image_input, 
    outputs=label,
    title='Animal Classifier',
    description='Upload an image of a butterfly, a cat, an elephant, a horse or a squirrel, and the classifier will tell you which animal it is, along with the confidence level of the prediction.'
)

transfer_learning_interface.launch()