pages/25_Deployment.py

import streamlit as st
from PIL import Image
import torch
import torchvision.transforms as transforms
import torchvision.models as models

# Save the model (this should be run only once, so it is placed here for completeness)
def save_model():
    model = models.resnet18(pretrained=True)
    torch.save(model.state_dict(), 'resnet18.pth')

# Call save_model to save the model
save_model()

# Load the model
def load_model():
    model = models.resnet18()
    model.load_state_dict(torch.load('resnet18.pth'))
    model.eval()
    return model

def main():
    st.title("Image Classification with ResNet18")
    
    # Upload an image
    uploaded_file = st.file_uploader("Choose an image...", type="jpg")
    if uploaded_file is not None:
        image = Image.open(uploaded_file)
        st.image(image, caption='Uploaded Image.', use_column_width=True)
        st.write("")
        st.write("Classifying...")
        
        # Load the model
        model = load_model()
        
        # Preprocess the image
        preprocess = 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]),
        ])
        
        input_tensor = preprocess(image)
        input_batch = input_tensor.unsqueeze(0)
        
        # Ensure the input is on the same device as the model
        if torch.cuda.is_available():
            input_batch = input_batch.to('cuda')
            model.to('cuda')
        
        with torch.no_grad():
            output = model(input_batch)
        
        # The output has unnormalized scores. To get probabilities, you can run a softmax on it.
        probabilities = torch.nn.functional.softmax(output[0], dim=0)
        
        # Print top 5 categories
        with open("imagenet_classes.txt") as f:
            categories = [line.strip() for line in f.readlines()]
        
        top5_prob, top5_catid = torch.topk(probabilities, 5)
        for i in range(top5_prob.size(0)):
            st.write(categories[top5_catid[i]], top5_prob[i].item())

if __name__ == "__main__":
    main()