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Gradio interface.py

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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()

alexnet_cifar10.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:219ddb0d39b2c2e362f34fa41205b2c2ff5e7de15dadd6cd212e3a60c50ed695
+size 259542304