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app.py

@@ -1,32 +1,31 @@
-# app.py
 import gradio as gr
 from fastai.learner import load_learner
 from fastai.vision.all import PILImage
-from PIL import Image
 
-# Load the model
+# Load the model directly (since it will be in the same repository)
 model = load_learner('model.pkl')
 
 def classify_image(image):
-    # Convert to FastAI PILImage
+    # Convert to FastAI format
     img = PILImage.create(image)
     
     # Get prediction
     pred, pred_idx, probs = model.predict(img)
     
-    # Return prediction and confidence
+    # Return prediction and probability
+    confidence = float(probs[pred_idx])
     return {
-        "cat": float(probs[pred_idx]) if str(pred) == "cat" else 1 - float(probs[pred_idx])
+        "Cat": confidence if str(pred).lower() == "cat" else 1 - confidence,
+        "Not Cat": confidence if str(pred).lower() != "cat" else 1 - confidence
     }
 
-# Create Gradio interface
-iface = gr.Interface(
+# Create the interface
+demo = gr.Interface(
     fn=classify_image,
-    inputs=gr.Image(),
+    inputs=gr.Image(type="pil"),
     outputs=gr.Label(num_top_classes=2),
-    title="Cat Classifier",
+    title="🐱 Cat Detector",
     description="Upload an image to check if it contains a cat!",
-    examples=["example1.jpg", "example2.jpg"]  # Optional: Add example images if you have them
 )
 
-iface.launch()
+demo.launch()