Update requirements.txt

requirements.txt

@@ -1,52 +1,4 @@
-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()
+streamlit
+Pillow
+torch
+torchvision