Update app.py

app.py

@@ -1,19 +1,32 @@
 import gradio as gr
 from fastai.vision.all import *
-import skimage
 from fastai.vision.all import PILImage
 
+# Load the trained model
 learn = load_learner('export.pkl')
 
+# Get the labels from the data loaders
 labels = learn.dls.vocab
 
+# Define the prediction function
 def predict(img):
- img = PILImage.create(img)
- img = img.resize((512, 512))
- pred,pred_idx,probs = learn.predict(img)
- return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    img = PILImage.create(img)
+    img = img.resize((512, 512))
+    pred, pred_idx, probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
 
+# Example images for demonstration
 examples = ['image.jpg']
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.components.Image(shape=(512, 512)),
+    outputs=gr.components.Label(num_top_classes=3),
+)
+
+# Enable the queue to handle POST requests
 interface.queue(api_open=True)
-#gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()
-gr.Interface(fn=predict,inputs=gr.components.Image(),outputs=gr.components.Label(num_top_classes=3)).launch()
+
+# Launch the interface
+interface.launch()