Create app.py

app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+from PIL import Image
+from ResNet_for_CC import CC_model  # Import the model
+
+# Set device (CPU/GPU)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load the trained CC_model
+model_path = "CC_net.pt"
+model = CC_model(num_classes1=14)
+
+# Load model weights
+state_dict = torch.load(model_path, map_location=device)
+model.load_state_dict(state_dict, strict=False)
+model.to(device)
+model.eval()
+
+# Clothing1M Class Labels
+class_labels = [
+    "T-Shirt", "Shirt", "Knitwear", "Chiffon", "Sweater", "Hoodie",
+    "Windbreaker", "Jacket", "Downcoat", "Suit", "Shawl", "Dress",
+    "Vest", "Underwear"
+]
+
+# ✅ **Updated Image Preprocessing Function**
+def preprocess_image(image):
+    """Applies necessary transformations to the input image."""
+    transform = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ])
+    return transform(image).unsqueeze(0).to(device)
+
+# ✅ **Classification Function**
+def classify_image(image):
+    """Processes the input image and returns the predicted clothing category."""
+    print("\n[INFO] Received image for classification.")
+
+    try:
+        image = Image.fromarray(image)  # Ensure conversion to PIL format
+        image = preprocess_image(image)  # Apply transformations
+        print("[INFO] Image transformed and moved to device.")
+
+        with torch.no_grad():
+            output = model(image)
+            
+            # ✅ Ensure output is a tensor (handle tuple case)
+            if isinstance(output, tuple):
+                output = output[1]  # Extract the actual output tensor
+
+            print(f"[DEBUG] Model output shape: {output.shape}")
+            print(f"[DEBUG] Model output values: {output}")
+
+            if output.shape[1] != 14:
+                return f"[ERROR] Model output mismatch! Expected 14 but got {output.shape[1]}."
+
+            # Convert logits to probabilities
+            probabilities = F.softmax(output, dim=1)
+            print(f"[DEBUG] Softmax probabilities: {probabilities}")
+
+            # Get predicted class index
+            predicted_class = torch.argmax(probabilities, dim=1).item()
+            print(f"[INFO] Predicted class index: {predicted_class} (Class: {class_labels[predicted_class]})")
+
+            # Validate and return the prediction
+            if 0 <= predicted_class < len(class_labels):
+                predicted_label = class_labels[predicted_class]
+                confidence = probabilities[0][predicted_class].item() * 100
+                return f"Predicted Class: {predicted_label} (Confidence: {confidence:.2f}%)"
+            else:
+                return "[ERROR] Model returned an invalid class index."
+
+    except Exception as e:
+        print(f"[ERROR] Exception during classification: {e}")
+        return "Error in classification. Check console for details."
+
+# ✅ **Gradio Interface**
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="numpy"),
+    outputs="text",
+    title="Clothing1M Image Classifier",
+    description="Upload a clothing image, and the model will classify it into one of the 14 categories."
+)
+
+# ✅ **Run the Interface**
+if __name__ == "__main__":
+    print("[INFO] Launching Gradio interface...")
+    interface.launch()