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import streamlit as st |
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import torch |
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import open_clip |
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from PIL import Image |
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from classifier import few_shot_fault_classification |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model, _, preprocess = open_clip.create_model_and_transforms('RN50', pretrained='openai') |
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model = model.to(device) |
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model.eval() |
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st.title("🛠️ Few-Shot Fault Detection (Industrial Quality Control)") |
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st.markdown("Upload **10 Nominal Images**, **10 Defective Images**, and one or more **Test Images** to classify.") |
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col1, col2 = st.columns(2) |
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with col1: |
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nominal_files = st.file_uploader("Upload Nominal Images", type=["png", "jpg", "jpeg"], accept_multiple_files=True) |
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with col2: |
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defective_files = st.file_uploader("Upload Defective Images", type=["png", "jpg", "jpeg"], accept_multiple_files=True) |
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test_files = st.file_uploader("Upload Test Images", type=["png", "jpg", "jpeg"], accept_multiple_files=True) |
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if st.button("Classify Test Images"): |
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if len(nominal_files) < 1 or len(defective_files) < 1 or len(test_files) < 1: |
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st.warning("Please upload at least 1 image in each category.") |
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else: |
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st.info("Running classification...") |
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nominal_imgs = [preprocess(Image.open(f).convert("RGB")).unsqueeze(0) for f in nominal_files] |
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defective_imgs = [preprocess(Image.open(f).convert("RGB")).unsqueeze(0) for f in defective_files] |
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test_imgs = [preprocess(Image.open(f).convert("RGB")).unsqueeze(0) for f in test_files] |
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results = few_shot_fault_classification( |
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model=model, |
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test_images=[img.squeeze(0) for img in test_imgs], |
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test_image_filenames=[f.name for f in test_files], |
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nominal_images=[img.squeeze(0) for img in nominal_imgs], |
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nominal_descriptions=[f.name for f in nominal_files], |
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defective_images=[img.squeeze(0) for img in defective_imgs], |
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defective_descriptions=[f.name for f in defective_files], |
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num_few_shot_nominal_imgs=len(nominal_files), |
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device=device |
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) |
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for res in results: |
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st.write(f"**{res['image_path']}** ➜ {res['classification_result']} " |
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f"(Nominal: {res['non_defect_prob']}, Defective: {res['defect_prob']})") |
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