Update app.py

app.py

@@ -41,7 +41,7 @@ transform = transforms.Compose([
 
 # 4. Prediction Function
 def predict(image):
-    """Takes a PIL image and returns a dictionary of top 5 predictions."""
+    """Takes a PIL image and returns a dictionary of top 3 predictions."""
     if model is None:
         return {"Error": "Model is not loaded. Please check the logs for errors."}
         
@@ -50,24 +50,24 @@ def predict(image):
         outputs = model(image)
         probabilities = torch.nn.functional.softmax(outputs, dim=1)[0]
         
-    # Get top 5 predictions
-    top5_prob, top5_indices = torch.topk(probabilities, 5)
+    # Get top 3 predictions
+    top3_prob, top3_indices = torch.topk(probabilities, 3)
     
-    confidences = {labels[i]: float(p) for i, p in zip(top5_indices, top5_prob)}
+    confidences = {labels[i]: float(p) for i, p in zip(top3_indices, top3_prob)}
     
     return confidences
 
 # 5. Gradio Interface
 title = "Bird Species Classifier"
-description = "Upload an image of a bird to classify it into one of 200 species. This model is a ConvNeXt V2 Large, fine-tuned on the Caltech-UCSD Birds 200 (CUB-200) dataset."
+description = "Upload an image of a bird to classify it into one of 200 species. This model is a ConvNeXt V2 Large, fine-tuned on a dataset of 200 bird species."
 
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil", label="Upload Bird Image"),
-    outputs=gr.Label(num_top_classes=5, label="Predictions"),
+    outputs=gr.Label(num_top_classes=3, label="Predictions"),
     title=title,
     description=description,
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()