Update pages/25_Deployment.py

pages/25_Deployment.py

@@ -1,67 +1,7 @@
-import streamlit as st
-from PIL import Image
-import torch
-import torchvision.transforms as transforms
-import torchvision.models as models
+import webbrowser
 
-# 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')
+# URL of the YouTube video
+youtube_url = "https://www.youtube.com/watch?v=Dk88zv1KYMI&list=PL_lsbAsL_o2CTlGHgMxNrKhzP97BaG9ZN"
 
-# 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()
+# Open the YouTube video in the default web browser
+webbrowser.open(youtube_url)