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app.py

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+import gradio as gr
+import torch
+import torch.nn.functional as F
+from torchvision import transforms
+from PIL import Image
+from model import FineTunedResNet
+import time
+
+# Define the transform for the input image
+transform = transforms.Compose([
+    transforms.Resize((150, 150)),
+    transforms.ToTensor(),
+    transforms.Normalize((0.5,), (0.5,))
+])
+
+# Load the trained ResNet50 model
+model = FineTunedResNet(num_classes=3)
+model.load_state_dict(torch.load('/content/lung_disease_detection/models/final_fine_tuned_resnet50.pth',
+                                 map_location=torch.device('cpu')))
+model.eval()
+
+
+# Define a function to make predictions
+def predict(image):
+    start_time = time.time()  # Start the timer
+    image = transform(image).unsqueeze(0)  # Transform and add batch dimension
+
+    with torch.no_grad():
+        output = model(image)
+        probabilities = F.softmax(output, dim=1)[0]
+        top_prob, top_class = torch.topk(probabilities, 3)
+        classes = ['🦠 COVID', '🫁 Normal', '🦠 Pneumonia']  # Adjust based on the classes in your model
+
+    end_time = time.time()  # End the timer
+    prediction_time = end_time - start_time  # Calculate the prediction time
+
+    # Format the result string
+    result = f"Top Predictions:\\n"
+    for i in range(top_prob.size(0)):
+        result += f"{classes[top_class[i]]}: {top_prob[i].item() * 100:.2f}%\\n"
+    result += f"Prediction Time: {prediction_time:.2f} seconds"
+
+    return result
+
+
+# Example images with labels
+examples = [
+    ['examples/Pneumonia/02009view1_frontal.jpg', '🦠 Pneumonia'],
+    ['examples/Pneumonia/02055view1_frontal.jpg', '🦠 Pneumonia'],
+    ['examples/Pneumonia/03152view1_frontal.jpg', '🦠 Pneumonia'],
+    ['examples/COVID/11547_2020_1200_Fig3_HTML-a.png', '🦠 COVID'],
+    ['examples/COVID/11547_2020_1200_Fig3_HTML-b.png', '🦠 COVID'],
+    ['examples/COVID/11547_2020_1203_Fig1_HTML-b.png', '🦠 COVID'],
+    ['examples/Normal/06bc1cfe-23a0-43a4-a01b-dfa10314bbb0.jpg', '🫁 Normal'],
+    ['examples/Normal/08ae6c0b-d044-4de2-a410-b3cf8dc65868.jpg', '🫁 Normal'],
+    ['examples/Normal/IM-0178-0001.jpeg', '🫁 Normal']
+]
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload Chest X-ray Image"),
+    outputs=gr.Label(label="Predicted Disease"),
+    examples=examples,
+    title="Lung Disease Detection XVI",
+    description="Upload a chest X-ray image to detect lung diseases such as 🦠 COVID-19, 🦠 Pneumonia, or 🫁 Normal. Use the example images to see how the model works."
+)
+
+# Launch the interface
+interface.launch()

model.py

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+import torch
+import torchvision
+from torch import nn
+
+class FineTunedResNet(nn.Module):
+    def __init__(self, num_classes=3):
+        super(FineTunedResNet, self).__init__()
+        self.resnet = models.resnet50(pretrained=True)
+        self.resnet.fc = nn.Sequential(
+            nn.Linear(self.resnet.fc.in_features, 512),
+            nn.ReLU(),
+            nn.Dropout(0.5),
+            nn.Linear(512, num_classes)
+        )
+
+    def forward(self, x):
+        return self.resnet(x)

requirements.txt

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+gradio
+torch
+torchvision
+Pillow