Gradio interface.py

import gradio as gr
import tensorflow as tf
import cv2

# Load the model
model = tf.keras.models.load_model(r"C:/Users/Irfan Arshad/Downloads/alexnet_cifar10.h5")

# Define a function to preprocess and predict using the loaded model
def predict(image):
    # Resize image to (32, 32)
    image = cv2.resize(image, (32, 32))
    print("Resized image shape:", image.shape)  # Print the shape of the resized image
    # Convert image to float32 and normalize
    image = image.astype('float32') / 255.0
    # Add batch dimension
    image = tf.expand_dims(image, 0)
    # Predict using the model
    prediction = model.predict(image)
    class_index = tf.argmax(prediction, axis=1)[0].numpy()
    class_label = class_names[class_index]  # Get the class label
    return class_label

# Define the class names for CIFAR-10
class_names = [
    "airplane", "automobile", "bird", "cat", "deer",
    "dog", "frog", "horse", "ship", "truck"
]

gr.Interface(fn=predict, inputs='image', outputs='text').launch()