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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 preprocess the image
def preprocess_image(image):
# Convert the image to RGB
image = image.convert('RGB')
# Resize the image to match the model's input shape
image = image.resize((224, 224))
# Convert the image to a tensor
image_tensor = transform(image)
# Add a batch dimension
image_tensor = image_tensor.unsqueeze(0)
return image_tensor
# Create a function to make predictions
def predict(image):
# Preprocess the image
preprocessed_image = preprocess_image(image)
# Make the prediction
with torch.no_grad():
output = model(preprocessed_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()
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