app.py

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

# Load the pre-trained model
model = tf.keras.models.load_model('model.h5')  # Replace with the path to your saved model

# Define a Gradio interface for image classification
def classify_image(image):
    # Preprocess the input image
    image = Image.fromarray(image)
    image = image.resize((128, 128))
    image = np.array(image)
    image = image / 255.0  # Normalize the pixel values

    # Make a prediction
    prediction = model.predict(np.expand_dims(image, axis=0))

    # Get the class label
    class_label = "Dog" if prediction[0][0] < 0.5 else "Cat"

    return class_label

# Create a Gradio interface
iface = gr.Interface(fn=classify_image, 
                     inputs="image",
                     outputs="text",
                     capture_session=True)

# Launch the Gradio interface


iface.launch(server_name="0.0.0.0", server_port=7860)