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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() |