Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# 1. Load the model
+# Ensure 'my_cifar_model.keras' is uploaded to the same Space directory
+model = tf.keras.models.load_model('my_cifar_model.keras')
+
+# 2. Define the class labels (Matches CIFAR-10 order)
+labels = [
+    'airplane', 'automobile', 'bird', 'cat', 'deer',
+    'dog', 'frog', 'horse', 'ship', 'truck'
+]
+
+def predict(img):
+    """
+    Takes an input image, processes it, and returns 
+    the top classification probabilities.
+    """
+    if img is None:
+        return None
+
+    # Preprocessing:
+    # Convert to PIL Image if it's a numpy array, then resize to 32x32
+    img = Image.fromarray(img).resize((32, 32))
+    
+    # Convert to array and normalize (0 to 1)
+    img_array = np.array(img).astype('float32') / 255.0
+    
+    # Add batch dimension: (32, 32, 3) -> (1, 32, 32, 3)
+    img_array = np.expand_dims(img_array, axis=0)
+
+    # Perform prediction
+    predictions = model.predict(img_array).flatten()
+
+    # Apply Softmax to get probabilities (if not already in the model output)
+    score = tf.nn.softmax(predictions).numpy()
+
+    # Create a dictionary of {Label: Probability}
+    return {labels[i]: float(score[i]) for i in range(10)}
+
+# 3. Create the Gradio Interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(),
+    outputs=gr.Label(num_top_classes=3),
+    title="CIFAR-10 Image Classifier",
+    description="Upload an image and the model will predict its category among 10 classes.",
+    examples=["airplane_example.jpg", "cat_example.jpg"] # Optional: if you upload these files too
+)
+
+# 4. Launch the app
+if __name__ == "__main__":
+    demo.launch()