Create app.py

app.py

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+import gradio as gr
+from fastai.vision.all import PILImage
+
+
+
+def predict(image):
+    # Transform the image
+    pil_image = PILImage.create(image)
+    
+    # Predict
+    preds, _, probs = learn.predict(pil_image)
+    
+    # Apply the threshold
+    threshold = 0.425
+    classes = [learn.dls.vocab[i] for i in range(len(probs)) if probs[i] > threshold]
+
+    pet_type = "dog" if "dog" in classes else "cat" if "cat" in classes else None
+    breeds = [breed for breed in classes if breed not in ["cat", "dog"]]
+
+    if pet_type:
+        breed_info = f"The breed is {'/'.join(breeds)}." if breeds else "The breed is not identified."
+        return f"This is a {pet_type}. {breed_info}"
+    else:
+        return "This is not a cat, nor a dog."
+
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(shape=(224, 224)),
+    outputs="text",
+    live=True,
+    title="Cat and Dog Image Classifier",
+    description="Upload an image of a cat or a dog, and the model will identify the type and breed.",
+    article="This model has been trained on the Oxford Pets dataset and might not recognize all types dog and cat breeds. For best results, use clear images."
+)
+
+
+# Launch the interface
+iface.launch(share=True)