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app-com_sentenca.py

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+import cv2
+import gradio as gr
+from ultralytics import YOLO
+
+# Load the model once globally
+MODEL_PATH = "best.pt"  
+model = YOLO(MODEL_PATH)
+
+def detect_and_visualize(image):
+    # image is a NumPy array from Gradio
+    # Perform inference directly on this array
+    results = model(image)
+    
+    # Ensure image is in the correct color space (most likely already RGB)
+    annotated_image = image.copy()
+
+    detections = []
+    for result in results:
+        boxes = result.boxes.xyxy.cpu().numpy()  
+        confidences = result.boxes.conf.cpu().numpy()  
+        class_ids = result.boxes.cls.cpu().numpy().astype(int)  
+
+        for box, confidence, class_id in zip(boxes, confidences, class_ids):
+            x_min, y_min, x_max, y_max = map(int, box)
+            class_name = model.names[class_id]
+
+            # Pick a color or use a fixed color, no need for random if not desired
+            color = (0, 255, 0)
+            cv2.rectangle(annotated_image, (x_min, y_min), (x_max, y_max), color, 2)
+            label = f"{class_name} {confidence:.2f}"
+            cv2.putText(annotated_image, label, (x_min, y_min - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+
+            detections.append({
+                "label": class_name,
+                "confidence": float(confidence),
+                "bounding_box": {
+                    "x1": x_min,
+                    "y1": y_min,
+                    "x2": x_max,
+                    "y2": y_max
+                }
+            })
+
+    return annotated_image, detections
+
+def gradio_interface(image):
+    annotated_image, detections = detect_and_visualize(image)
+    return annotated_image, detections
+
+interface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=[
+        gr.Image(type="numpy", label="Annotated Image"),
+        gr.JSON(label="Detection Details")
+    ],
+    title="YOLO Object Detection",
+    description="Upload an image to detect objects and view annotated results along with detailed detection data."
+)
+
+if __name__ == "__main__":
+    interface.launch()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d0020456ffaedfacdeafd3caf1d89b96a58844110c4caaa425f9e0a3152ef8b2
+size 118666514