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app.py

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+import gradio as gr
+from fastai.vision.all import *
+from fastai.vision.all import PILImage
+
+# Load the trained model
+learn = load_learner('fog_classifier.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))}
+
+# Example images for demonstration
+examples = [
+    "dar3.jpg",
+    "dar2.jpg"
+]
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(),
+    outputs=gr.Label(num_top_classes=3),
+    examples=examples  # Move examples here
+)
+
+# Enable the queue to handle POST requests
+interface.queue(api_open=True)
+
+# Launch the interface
+interface.launch()

fog_classifier.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:261d104955b6d3efc06d3e3b25ab104b790c60a7ef78d79665cad81f35ffaff7
+size 348590778