Update app.py

app.py

@@ -4,7 +4,11 @@ from PIL import Image
 from io import BytesIO
 from transformers import pipeline
 
-# Our label mapping:
+# Adjust these if your model's order is actually different.
+# For example, if your dataset folders are named (alphabetically): 
+#   bumblebee, honeybee, vespidae,
+# then 0 => bumblebee, 1 => honeybee, 2 => vespidae (the default PyTorch order).
+# Verify your label indices by printing `test_dataset.classes` in your training script.
 label_map = {
     "LABEL_0": "honeybee",
     "LABEL_1": "bumblebee",
@@ -27,12 +31,16 @@ def classify_image_from_url(image_url: str):
         
         # Run inference
         results = classifier(image)
-        
-        # Post-process to replace "LABEL_0" etc. with "honeybee", "bumblebee", "vespidae"
+
+        # 1) Post-process labels
+        # 2) Format scores to remove scientific notation
         for r in results:
+            # Map from "LABEL_x" to your real class name
             if r["label"] in label_map:
                 r["label"] = label_map[r["label"]]
-        
+            # Format score with, e.g., 8 decimal places to avoid scientific notation
+            r["score"] = float(f"{r['score']:.8f}") 
+
         return results
     
     except Exception as e:
@@ -43,7 +51,7 @@ demo = gr.Interface(
     inputs=gr.Textbox(lines=1, label="Image URL"),
     outputs="json",
     title="ResNet-50 Image Classifier",
-    description="Enter a public image URL to get top predictions."
+    description="Enter a public image URL to get top predictions with custom labels."
 )
 
 if __name__ == "__main__":