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import streamlit as st |
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import os |
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from PIL import Image |
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import torch |
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from torchvision import transforms |
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from models.cnn import CNNModel |
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from utils.transforms import get_transforms |
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os.environ["STREAMLIT_ROOT"] = "/tmp/.streamlit" |
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@st.cache_resource |
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def load_model(model_path='saved_models/cnn_model.pth'): |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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checkpoint = torch.load(model_path, map_location=device) |
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class_names = checkpoint['class_names'] |
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model = CNNModel(num_classes=len(class_names)) |
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model.load_state_dict(checkpoint['model_state_dict']) |
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model.to(device) |
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model.eval() |
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return model, class_names, device |
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st.title("📸 Intel Image Classification") |
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st.write("Upload an image to classify it into one of the image categories: buildings, forest, glacier, mountain, sea, or street.") |
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model, class_names, device = load_model() |
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uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) |
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if uploaded_file: |
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image = Image.open(uploaded_file).convert("RGB") |
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st.image(image, caption="Uploaded Image", use_container_width=True) |
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transform = get_transforms(train=False) |
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image_tensor = transform(image).unsqueeze(0).to(device) |
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with torch.no_grad(): |
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output = model(image_tensor) |
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predicted_idx = torch.argmax(output, 1).item() |
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predicted_class = class_names[predicted_idx] |
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st.success(f"Predicted class: {predicted_class}") |
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