Update app.py

app.py

@@ -28,15 +28,15 @@ class_labels = [
     "Vest", "Underwear"
 ]
 
-# ✅ **Predefined Default Images**
+# **Predefined Default Images**
 default_images = {
     "T-Shirt": "tshirt.jpg",
-    "Jacket": "default_images/jacket.jpg",
-    "Sweater": "default_images/sweater.jpg",
-    "Dress": "default_images/dress.jpg"
+    "Jacket": "jacket.jpg",
+    "Sweater": "sweater.webp",
+    "Dress": "dress.jpg"
 }
 
-# ✅ **Image Preprocessing Function**
+# **Image Preprocessing Function**
 def preprocess_image(image):
     """Applies necessary transformations to the input image."""
     transform = transforms.Compose([
@@ -47,7 +47,7 @@ def preprocess_image(image):
     ])
     return transform(image).unsqueeze(0).to(device)
 
-# ✅ **Classification Function**
+# **Classification Function**
 def classify_image(selected_default, uploaded_image):
     """Processes either a default or uploaded image and returns the predicted clothing category."""
 
@@ -69,7 +69,7 @@ def classify_image(selected_default, uploaded_image):
         with torch.no_grad():
             output = model(image)
 
-            # ✅ Ensure output is a tensor (handle tuple case)
+            # Ensure output is a tensor (handle tuple case)
             if isinstance(output, tuple):
                 output = output[1]  # Extract the actual output tensor
 
@@ -99,7 +99,7 @@ def classify_image(selected_default, uploaded_image):
         print(f"[ERROR] Exception during classification: {e}")
         return "Error in classification. Check console for details."
 
-# ✅ **Gradio Interface**
+# **Gradio Interface**
 with gr.Blocks() as interface:
     gr.Markdown("# Clothing1M Image Classifier")
     gr.Markdown("Upload a clothing image or select from the predefined images below.")
@@ -127,7 +127,7 @@ with gr.Blocks() as interface:
         outputs=output_text
     )
 
-# ✅ **Run the Interface**
+# **Run the Interface**
 if __name__ == "__main__":
     print("[INFO] Launching Gradio interface...")
     interface.launch()