Create app.py

app.py

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+from fastai.vision.all import *
+
+# Load the pre-trained model
+learn = load_learner('model.pkl')
+
+# Define categories and map them to indices
+searches = ['formal', 'casual', 'athletic']
+searches = sorted(searches)  # Ensure the categories are in sorted order
+values = [i for i in range(0, len(searches))]
+class_dict = dict(zip(searches, values))
+
+def classify_image(image_path):
+    # Load the image from the provided path
+    img = PILImage.create(image_path)
+    
+    # Make the prediction
+    classification, _, probs = learn.predict(img)
+    
+    # Convert the prediction to a confidence dictionary
+    confidences = {label: float(probs[i]) for i, label in enumerate(class_dict)}
+    
+    # If classification is not formal, return 'informal'
+    if classification != 'formal':
+        informal_confidence = sum(confidences[label] for label in class_dict if label != 'formal')
+        return {'informal': informal_confidence}
+    else:
+        return {'formal': confidences['formal']}
+
+# Example usage with an image path
+image_path = 'path_to_image.jpg'  # Replace with the actual image path
+result = classify_image(image_path)
+print(result)