Create app.py

app.py

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+import tensorflow as tf
+from tensorflow.keras.models import load_model
+import gradio as gr
+
+# Class names
+class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
+               'dog', 'frog', 'horse', 'ship', 'truck']
+
+# Load trained model
+model = load_model("ResNet50_cifar10_best_fr.h5")
+
+# Define the preprocessing function
+def preprocess_image(img):
+    img = tf.image.resize(img, (32, 32))
+    img = img / 255.0
+    img = tf.expand_dims(img, axis=0)
+    return img
+
+
+# Define the postprocessing function
+def process_prediction(prediction):
+    predicted_class_index = int(prediction.argmax())
+    predicted_class_name = class_names[predicted_class_index]
+    return predicted_class_name
+
+
+# Define the prediction function
+def predict_cifar10(img):
+    preprocessed_img = preprocess_image(img)
+    prediction = model.predict(preprocessed_img)
+    return process_prediction(prediction)
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_cifar10,
+    inputs=[gr.Image(label="Input Image")],
+    outputs=[gr.Label(label="Predicted Class")],
+    title="CIFAR-10 Image Classifier",
+    description="Upload an image to classify it using a CIFAR-10 model."
+)
+
+# Launch the interface
+iface.launch()