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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}%') |