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Update app.py
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app.py
CHANGED
@@ -31,7 +31,8 @@ for package, version in packages.items():
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print(f"Installing {package}...")
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install(f"{package}=={version}")
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import numpy as np
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import torch
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import torch.nn as nn
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@@ -40,7 +41,7 @@ import torchvision.transforms as transforms
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from PIL import Image
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import gradio as gr
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# Define the model
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class ModifiedLargeNet(nn.Module):
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def __init__(self):
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super(ModifiedLargeNet, self).__init__()
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@@ -59,26 +60,47 @@ class ModifiedLargeNet(nn.Module):
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x = self.fc2(x)
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return x
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# Load the trained model
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model
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# Define image transformation pipeline
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transform = transforms.Compose([
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transforms.Resize((128, 128)),
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transforms.ToTensor(),
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])
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def process_image(image):
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if image is None:
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return None
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image
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def predict(image):
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if image is None:
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@@ -86,30 +108,37 @@ def predict(image):
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try:
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# Process the image
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processed_image = process_image(
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if processed_image is None:
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return {cls: 0.0 for cls in ["Rope", "Hammer", "Other"]}
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# Transform for model
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tensor_image = transform(processed_image).unsqueeze(0)
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# Make prediction
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with torch.no_grad():
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outputs = model(tensor_image)
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probabilities = F.softmax(outputs, dim=1)[0].cpu().numpy()
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# Return results
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classes = ["Rope", "Hammer", "Other"]
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except Exception as e:
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print(f"Prediction error: {str(e)}")
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return {cls: 0.0 for cls in ["Rope", "Hammer", "Other"]}
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# Gradio interface
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interface = gr.Interface(
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fn=predict,
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inputs=gr.Image(
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outputs=gr.Label(num_top_classes=3),
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title="Mechanical Tools Classifier",
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description="Upload an image of a tool to classify it as 'Rope', 'Hammer', or 'Other'.",
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print(f"Installing {package}...")
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install(f"{package}=={version}")
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import numpy as np
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import torch
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import torch.nn as nn
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from PIL import Image
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import gradio as gr
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# Define the model exactly as in training
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class ModifiedLargeNet(nn.Module):
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def __init__(self):
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super(ModifiedLargeNet, self).__init__()
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x = self.fc2(x)
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return x
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# Load the trained model with error handling
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try:
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model = ModifiedLargeNet()
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state_dict = torch.load("modified_large_net.pt", map_location=torch.device("cpu"))
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model.load_state_dict(state_dict)
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print("Model loaded successfully")
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model.eval()
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except Exception as e:
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print(f"Error loading model: {str(e)}")
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traceback.print_exc()
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# Define image transformation pipeline to match training
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transform = transforms.Compose([
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transforms.Resize((128, 128)),
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transforms.ToTensor(),
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# Using standard normalization as in training
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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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def process_image(image):
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if image is None:
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return None
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try:
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# Convert numpy array to PIL Image
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if isinstance(image, np.ndarray):
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image = Image.fromarray(image)
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# Convert to RGB if necessary
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if image.mode != 'RGB':
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image = image.convert('RGB')
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# Print debug information
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print(f"Processed image size: {image.size}")
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print(f"Processed image mode: {image.mode}")
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return image
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except Exception as e:
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print(f"Error in process_image: {str(e)}")
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traceback.print_exc()
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return None
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def predict(image):
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if image is None:
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try:
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# Process the image
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processed_image = process_image(image)
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if processed_image is None:
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return {cls: 0.0 for cls in ["Rope", "Hammer", "Other"]}
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# Transform for model
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tensor_image = transform(processed_image).unsqueeze(0)
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print(f"Input tensor shape: {tensor_image.shape}")
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# Make prediction
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with torch.no_grad():
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outputs = model(tensor_image)
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print(f"Raw outputs: {outputs}")
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probabilities = F.softmax(outputs, dim=1)[0].cpu().numpy()
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print(f"Probabilities: {probabilities}")
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# Return results
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classes = ["Rope", "Hammer", "Other"]
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results = {cls: float(prob) for cls, prob in zip(classes, probabilities)}
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print(f"Final results: {results}")
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return results
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except Exception as e:
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print(f"Prediction error: {str(e)}")
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traceback.print_exc()
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return {cls: 0.0 for cls in ["Rope", "Hammer", "Other"]}
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# Gradio interface
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interface = gr.Interface(
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fn=predict,
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inputs=gr.Image(), # Accept any image format
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outputs=gr.Label(num_top_classes=3),
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title="Mechanical Tools Classifier",
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description="Upload an image of a tool to classify it as 'Rope', 'Hammer', or 'Other'.",
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