Update app.py

app.py

@@ -39,11 +39,11 @@ image_path = [['test_images/2a998cfb0901db5f8210.jpg','cham_diem_yolov8', 640, 0
 
 ###################################################
 def yolov8_img_inference(
-    image = None,
-    model_path =   None,
-    image_size =  640,
-    conf_threshold =  0.25,
-    iou_threshold =  0.45,
+    image: gr.inputs.Image = None,
+    model_path: gr.inputs.Dropdown = None,
+    image_size: gr.inputs.Slider = 640,
+    conf_threshold: gr.inputs.Slider = 0.25,
+    iou_threshold: gr.inputs.Slider = 0.45,
 ):
     # model = YOLO(model_path)
     model = YOLO(model_path)
@@ -78,82 +78,89 @@ def yolov8_img_inference(
     return render, {names[k]: v for k, v in present_objects.items()}
 
 
-def yolov8_vid_inference(video_path):
-    cap = cv2.VideoCapture(video_path)
-    while cap.isOpened():
-        success, frame = cap.read()
-
-        if success:
-            frame_copy = frame.copy()
-            outputs = model.predict(source=frame)
-            results = outputs[0].cpu().numpy()
-            for i, det in enumerate(results.boxes.xyxy):
-                cv2.rectangle(
-                    frame_copy,
-                    (int(det[0]), int(det[1])),
-                    (int(det[2]), int(det[3])),
-                    color=(0, 0, 255),
-                    thickness=2,
-                    lineType=cv2.LINE_AA
-                )
-            yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
+# def yolov8_vid_inference(video_path):
+#     cap = cv2.VideoCapture(video_path)
+#     while cap.isOpened():
+#         success, frame = cap.read()
+
+#         if success:
+#             frame_copy = frame.copy()
+#             outputs = model.predict(source=frame)
+#             results = outputs[0].cpu().numpy()
+#             for i, det in enumerate(results.boxes.xyxy):
+#                 cv2.rectangle(
+#                     frame_copy,
+#                     (int(det[0]), int(det[1])),
+#                     (int(det[2]), int(det[3])),
+#                     color=(0, 0, 255),
+#                     thickness=2,
+#                     lineType=cv2.LINE_AA
+#                 )
+#             yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
     
 
-inputs_vid = [
-    gr.components.Video(type="filepath", label="Input Video"),
-]
+# inputs_vid = [
+#     gr.components.Video(type="filepath", label="Input Video"),
+# ]
 
-outputs_vid = [
-    gr.components.Image(type="numpy", label="Output Image"),
-              ]
+# outputs_vid = [
+#     gr.components.Image(type="numpy", label="Output Image"),
+#               ]
 
-interface_vid = gr.Interface(
-    fn=yolov8_vid_inference,
-    inputs = inputs_vid,
-    outputs = outputs_vid,
-    title = "Detect Thiên Việt productions",
-    cache_examples = False,
+# interface_vid = gr.Interface(
+#     fn=yolov8_vid_inference,
+#     inputs = inputs_vid,
+#     outputs = outputs_vid,
+#     title = "Detect Thiên Việt productions",
+#     cache_examples = False,
     
-)
+# )
 
-# inputs_image = [
-#     # gr.inputs.Image(type="filepath", label="Input Image"),
-#     gr.Image(type="pil"),
-#     # gr.Dropdown(["linhcuem/linhcuem/chamdiemgianhang_yolov8_ver1"], 
-#                         default="linhcuem/chamdiemgianhang_yolov8_ver1", label="Model"),
-    
-#     gr.Slider(maximum=1280, step=32, value = 640, label="Image Size"),
-#     gr.Slider(maximum=1.0 , step=0.05, value = 0.25, label="Confidence Threshold"),
-#     gr.Slider(maximum=1.0, step=0.05, value = 0.45, label="IOU Threshold"),
-    
-    
-# ]
+inputs = [
+    gr.inputs.Image(type="filepath", label="Input Image"),
+    gr.inputs.Dropdown(["linhcuem/checker_TB_yolov8_ver1", "linhcuem/chamdiemgianhang_yolov8_ver21"], 
+                        default="linhcuem/checker_TB_yolov8_ver1", label="Model"),
+    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
+    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
+    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
+]
 
-# outputs_image =gr.outputs.Image(type="filepath", label="Output Image")
+outputs_image =gr.outputs.Image(type="filepath", label="Output Image")
 # count_obj = gr.Textbox(show_label=False)
 
 title = "Detect Thiên Việt productions"
 
-interface_image = gr.Interface(
+# interface_image = gr.Interface(
+#     fn=yolov8_img_inference,
+#     inputs=[
+#         gr.Image(type='pil'),
+#         gr.Dropdown(["linhcuem/checker_TB_yolov8_ver1", "linhcuem/chamdiemgianhang_yolov8_ver21"], 
+#                         default="linhcuem/checker_TB_yolov8_ver1", label="Model"),
+#         gr.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
+#         gr.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
+#         gr.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
+#     ],
+#     outputs=[gr.Image(type="pil"),gr.Textbox(show_label=False)],
+#     title=title,
+#     examples=image_path,
+#     cache_examples=True if image_path else False,
+    
+# )
+
+# gr.TabbedInterface(
+#     [interface_image, interface_vid],
+#     tab_names=['Image inference', 'Video inference']
+# ).queue().launch()
+
+demo_app = gr.Interface(
     fn=yolov8_img_inference,
-    inputs=[
-        gr.Image(type='pil'),
-        gr.Dropdown(["linhcuem/checker_TB_yolov8_ver1", "linhcuem/chamdiemgianhang_yolov8_ver21"], 
-                        default="linhcuem/checker_TB_yolov8_ver1", label="Model"),
-        gr.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
-        gr.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
-        gr.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
-    ],
-    outputs=[gr.Image(type="pil"),gr.Textbox(show_label=False)],
+    inputs=inputs,
+    outputs=outputs,
     title=title,
-    examples=image_path,
-    cache_examples=True if image_path else False,
-    
+    examples=examples,
+    cache_examples=True,
+    theme='huggingface',
 )
-
-gr.TabbedInterface(
-    [interface_image, interface_vid],
-    tab_names=['Image inference', 'Video inference']
-).queue().launch()
+demo_app.launch(debug=True, enable_queue=True)
 
 # interface_image.launch(debug=True, enable_queue=True)