Create app.py

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+from ultralytics import YOLO
+import cv2
+
+examples=[["photo/a.png","Image1"],["photo/b.png","Image2"],
+          ["photo/c.png","Image3"],["photo/d.png","Image4"],
+          ["photo/e.png","Image5"],["photo/f.png","Image6"],
+          ["photo/g.png","Image7"],["photo/h.png","Image8"]]
+
+
+def detect_objects_on_image(image_path):
+    image = cv2.imread(image_path)
+    model = YOLO("best.pt")
+    results = model.predict(image_path)
+    result = results[0]
+    output = []
+    for box in result.boxes:
+        x1, y1, x2, y2 = [
+            round(x) for x in box.xyxy[0].tolist()
+            
+        ]
+        class_id = box.cls[0].item()
+        prob = round(box.conf[0].item(), 2)
+        output.append([
+            x1, y1, x2, y2, result.names[class_id], prob
+        ])
+        cv2.rectangle(
+            image,
+            (x1, y1),
+            (x2, y2),
+            color=(0, 0, 255),
+            thickness=2,
+            lineType=cv2.LINE_AA
+        )
+        
+        cv2.putText(image,result.names[class_id],  (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36,255,12), 2)
+    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+
+inputs_image = [
+    gr.components.Image(type="filepath", label="Input Image"),
+]
+outputs_image = [
+    gr.components.Image(type="numpy", label="Output Image"),
+]
+demo = gr.Interface(
+    fn=detect_objects_on_image,
+    inputs=inputs_image,
+    outputs=outputs_image,
+    title="Biker Helmet and Number Plate Detection",
+    examples=examples,
+    cache_examples=False,
+)
+
+if __name__ == "__main__":
+    demo.launch()