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LICENSE

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+Copyright (c) 2025 SMARTFALCON-AI.com
+All Rights Reserved.
+
+This model is proprietary. Any usage, distribution, or reproduction requires explicit permission from the copyright holder.

app.py

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+import gradio as gr
+import onnxruntime as ort
+import numpy as np
+import cv2
+from huggingface_hub import hf_hub_download
+
+# Private models from your account
+MODELS = {
+    "Dental Implant": "smartfalcon-ai/Dental-Implant-Defect-Detection",
+    "Data Matrix": "smartfalcon-ai/Data-Matrix-Defect-Detection",
+    "Ball Pen": "smartfalcon-ai/Ball-Pen-Defect-Detection",
+    "Knit Up": "smartfalcon-ai/Knit-Up-Defect-Detection",
+    "Knit Back": "smartfalcon-ai/Knit-Back-Defect-Detection",
+    "Jean Back": "smartfalcon-ai/Jean-Back-Defect-Detection",
+    "Jean Up": "smartfalcon-ai/Jean-Up-Defect-Detection",
+    "Tire Cord": "smartfalcon-ai/Tire-Cord-Defect-Detection"
+}
+
+# Keep loaded sessions in memory
+sessions = {}
+
+def get_session(model_name):
+    if model_name not in sessions:
+        model_path = hf_hub_download(repo_id=MODELS[model_name], filename="best.onnx", use_auth_token=True)
+        sessions[model_name] = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
+    return sessions[model_name]
+
+IMG_SIZE = 640
+IOU_THRESHOLD = 0.45  # Fixed NMS
+
+# Preprocessing
+def preprocess(img):
+    h, w = img.shape[:2]
+    img_resized = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
+    img_resized = img_resized.astype(np.float32) / 255.0
+    img_resized = img_resized.transpose(2, 0, 1)  # CHW
+    img_resized = np.expand_dims(img_resized, 0)
+    return img_resized, w, h
+
+# Convert [cx,cy,w,h] -> [x1,y1,x2,y2]
+def xywh2xyxy(x):
+    y = np.copy(x)
+    y[:,0] = x[:,0] - x[:,2]/2
+    y[:,1] = x[:,1] - x[:,3]/2
+    y[:,2] = x[:,0] + x[:,2]/2
+    y[:,3] = x[:,1] + x[:,3]/2
+    return y
+
+# Non-max suppression
+def non_max_suppression(preds, conf_thres=0.25, iou_thres=0.45):
+    preds = preds[0]
+    preds = preds[preds[:,4] > conf_thres]
+    if preds.shape[0]==0:
+        return []
+
+    boxes = xywh2xyxy(preds[:, :4])
+    scores = preds[:,4]
+    class_scores = preds[:,5:]
+    cls_ids = np.argmax(class_scores, axis=1)
+    cls_conf = class_scores.max(axis=1)
+    final_scores = scores * cls_conf
+
+    indices = cv2.dnn.NMSBoxes(
+        bboxes=boxes.tolist(),
+        scores=final_scores.tolist(),
+        score_threshold=conf_thres,
+        nms_threshold=iou_thres
+    )
+    if len(indices)==0:
+        return []
+
+    indices = indices.flatten()
+    output=[]
+    for idx in indices:
+        x1, y1, x2, y2 = boxes[idx]
+        output.append([x1, y1, x2, y2, final_scores[idx], cls_ids[idx]])
+    return output
+
+# Inference
+def infer(image, model_name, conf_threshold):
+    session = get_session(model_name)
+    img_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    blob, orig_w, orig_h = preprocess(img_bgr)
+    preds = session.run(None, {"images": blob})[0]
+    detections = non_max_suppression(preds, conf_threshold, IOU_THRESHOLD)
+
+    for det in detections:
+        x1, y1, x2, y2, score, cls_id = det
+        x1 = int(x1 / IMG_SIZE * orig_w)
+        y1 = int(y1 / IMG_SIZE * orig_h)
+        x2 = int(x2 / IMG_SIZE * orig_w)
+        y2 = int(y2 / IMG_SIZE * orig_h)
+
+        label = f"{int(cls_id)}:{score:.2f}"
+        cv2.rectangle(img_bgr, (x1, y1), (x2, y2), (0,255,0), 2)
+        cv2.putText(img_bgr, label, (x1, y1-5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,255,0), 2)
+
+    return cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
+
+# Gradio UI
+demo = gr.Interface(
+    fn=infer,
+    inputs=[
+        gr.Image(type="numpy"),
+        gr.Dropdown(list(MODELS.keys()), label="Select Model"),
+        gr.Slider(minimum=0.0, maximum=1.0, value=0.25, step=0.01, label="Confidence Threshold")
+    ],
+    outputs=gr.Image(type="numpy"),
+    title="Industrial Defect Detection",
+    description="Upload an image, select the defect model, and adjust the confidence threshold."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+onnxruntime
+numpy
+opencv-python
+huggingface_hub