Create app.py

app.py

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