Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import torch
+from torchvision import models, transforms
+
+# Load the pre-trained model
+model = models.densenet121(pretrained=True)
+model.eval()
+
+# Define the image transformations
+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]),
+])
+
+# Define the class labels
+class_labels = ['Normal', 'Pneumonia']
+
+# Create a function to make predictions
+def predict(image):
+    # Preprocess the image
+    image = transform(image).unsqueeze(0)
+    
+    # Make the prediction
+    with torch.no_grad():
+        output = model(image)
+        _, predicted_idx = torch.max(output, 1)
+        predicted_label = class_labels[predicted_idx.item()]
+    
+    return predicted_label
+
+# Create the Streamlit app
+def main():
+    st.title("Pneumonia Detection")
+    st.write("Upload an image and the app will predict if it has pneumonia or not.")
+    
+    # Upload and display the image
+    uploaded_image = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
+    
+    if uploaded_image is not None:
+        image = Image.open(uploaded_image)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+        
+        # Make a prediction
+        predicted_label = predict(image)
+        st.write("Prediction:", predicted_label)
+
+# Run the app
+if __name__ == '__main__':
+    main()