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