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ExtraTreesClassifier.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b1ac22d924a196ada8f75ca42320f71b46828e9fbfdbdff7e989577d19d75551
+size 4211049

app.py

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+import gradio as gr
+import numpy as np
+import cv2
+import joblib
+import os
+
+# ================= LOAD MODEL =================
+MODEL_PATH = "ExtraTreesClassifier.pkl"
+
+if not os.path.exists(MODEL_PATH):
+    raise FileNotFoundError("Model not found! Train and save Model first.")
+
+model = joblib.load(MODEL_PATH)
+
+# ================= CATEGORY LABELS =================
+# ⚠️ IMPORTANT: Replace with your actual folder names in SAME ORDER
+categories = ["Defective", "Normal"]
+
+
+# ================= PREDICTION FUNCTION =================
+def predict_image(image):
+    try:
+        # Convert to numpy
+        img = np.array(image)
+
+        # Resize
+        img = cv2.resize(img, (64, 64))
+
+        # Normalize
+        img = img / 255.0
+
+        # Flatten
+        img = img.flatten().reshape(1, -1)
+
+        # Predict
+        pred = model.predict(img)[0]
+        label = categories[pred]
+
+        return f"Predicted Class: {label}"
+
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+# ================= GRADIO UI =================
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Cating Product Defect Classification From Images",
+    description="Upload an image to predict its category"
+)
+
+interface.launch()

requirements.txt

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+gradio
+numpy
+opencv-python
+joblib
+scikit-learn
+pillow