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Update app.py
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app.py
CHANGED
@@ -1,14 +1,15 @@
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import numpy as np
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import streamlit as st
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from PIL import Image
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import
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from utils import preprocess_image
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# Initialize labels and model
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labels = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
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model =
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# Customized Streamlit layout
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st.set_page_config(
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@@ -86,7 +87,7 @@ st.title("EcoIdentify by EcoClim Solutions")
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st.header("Upload a waste image to find its category")
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# Note
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st.markdown("* Please note that our dataset is trained primarily with images that contain a white background.
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# Image upload section
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opt = st.selectbox("How do you want to upload the image for classification?", ("Please Select", "Upload image from device"))
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@@ -102,8 +103,14 @@ try:
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if image is not None:
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st.image(image, width=256, caption='Uploaded Image')
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if st.button('Predict'):
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except Exception as e:
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st.error(f"An error occurred: {e}.
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import numpy as np
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import streamlit as st
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from PIL import Image
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import torch
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import torch.nn as nn
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import torchvision.transforms as transforms
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from utils import preprocess_image
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# Initialize labels and model
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labels = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
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model = torch.load('classify_model.pth')
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model.eval()
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# Customized Streamlit layout
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st.set_page_config(
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st.header("Upload a waste image to find its category")
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# Note
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st.markdown("* Please note that our dataset is trained primarily with images that contain a white background. Therefore, images with white background would produce maximum accuracy *")
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# Image upload section
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opt = st.selectbox("How do you want to upload the image for classification?", ("Please Select", "Upload image from device"))
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if image is not None:
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st.image(image, width=256, caption='Uploaded Image')
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if st.button('Predict'):
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transform = transforms.Compose([
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transforms.Resize((256, 256)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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])
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image = transform(image).unsqueeze(0)
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with torch.no_grad():
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prediction = model(image)
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st.success(f'Prediction: {labels[torch.argmax(prediction, dim=1).item()]}')
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except Exception as e:
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st.error(f"An error occurred: {e}. Please contact us EcoClim Solutions at EcoClimSolutions.wordpress.com.")
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