add model and requirements

app.py

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+import streamlit as st
+from PIL import Image
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from torch.utils.data import DataLoader
+from torchvision.datasets import ImageFolder
+
+vgg16 = models.vgg16(pretrained=True)
+
+# Freeze the convolutional base to prevent updating weights during training
+for param in vgg16.features.parameters():
+    param.requires_grad = False
+
+num_features = vgg16.classifier[6].in_features
+num_classes = 3  
+vgg16.classifier[6] = torch.nn.Linear(num_features, num_classes)
+
+# Load the model
+model = vgg16
+state_dict = torch.load('vgg16_transfer_learning.pth')
+model.load_state_dict(state_dict)
+model.eval()
+
+# Define the same transforms that were used during the model training
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),  
+    transforms.ToTensor(),  
+    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))        
+])
+
+classes = ('broccoli', 'cabbage', 'cauliflower')
+
+def predict(image):
+    input_tensor = transform(image)
+    input_batch = input_tensor.unsqueeze(0)
+
+    with torch.no_grad():
+        output = model(input_batch)
+
+    probabilities = torch.nn.functional.softmax(output[0], dim=0)
+    max_value, predicted_class = torch.max(probabilities, 0)
+    return classes[predicted_class.item()], max_value.item() * 100
+
+st.title('Vegetable Classification')
+st.write('you can upload your image of veggies below')
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert('RGB')
+    st.image(image, caption='Uploaded Image')
+    label, confidence = predict(image)
+    st.write(f'Predicted label: {label}, confidence: {confidence:.2f}%')

requirements.txt

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+torch==2.2.1
+torchvision==0.17.1
+torchaudio==2.2.1
+torchsummary==1.5.1
+torcheval==0.0.7
+ipykernel==6.29.3
+ipython==8.22.2
+scikit-learn
+streamlit==1.32.2
+toml==0.10.2
+pillow==10.2.0