Delete app (1).py

app (1).py

@@ -1,95 +0,0 @@
-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()