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import tensorflow as tf
import numpy as np
import gradio as gr
from PIL import Image
# Load the saved model
model = tf.keras.models.load_model('cifar10_cnn_model.keras')
# Define the CIFAR-10 class names
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
# Define a function to preprocess the input image
def preprocess_image(image):
image = image.resize((32, 32)) # Resize image to 32x32
image = np.array(image) / 255.0 # Normalize pixel values
image = np.expand_dims(image, axis=0) # Add batch dimension
return image
# Define the prediction function
def classify_image(image):
preprocessed_image = preprocess_image(image)
predictions = model.predict(preprocessed_image)
predicted_class = class_names[np.argmax(predictions)]
confidence = np.max(predictions)
return f"Prediction: {predicted_class} (Confidence: {confidence:.2f})"
# Create the Gradio interface
interface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="pil"),
outputs="text",
title="CIFAR-10 Image Classifier",
description="Upload an image of a CIFAR-10 category (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck), and the model will classify it."
)
# Launch the app
if __name__ == "__main__":
interface.launch()
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