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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
#define the model architecture
model_resnet = models.resnet18(weights='IMAGENET1K_V1')
for param in model_resnet.parameters():
param.requires_grad = False
# Parameters of newly constructed modules have requires_grad=True by default
num_ftrs = model_resnet.fc.in_features
model_resnet.fc = nn.Linear(num_ftrs, 15) #mengganti jumlah classifier sesuai output kelas
# Load the model
model = model_resnet
state_dict = torch.load('transfer_learning_resnet_15class.pth', map_location=torch.device('cpu'))
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 = ('Bean', 'Bitter_Gourd', 'Bottle_Gourd', 'Brinjal', 'Broccoli', 'Cabbage', 'Capsicum', 'Carrot', 'Cauliflower', 'Cucumber', 'Papaya', 'Potato', 'Pumpkin', 'Radish', 'Tomato')
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 for learning')
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}%') |