Update app.py

app.py

@@ -1,7 +1,10 @@
 import gradio as gr
 import torch
-from sahi.prediction import ObjectPrediction
-from sahi.utils.cv import visualize_object_predictions, read_image
+# from sahi.prediction import ObjectPrediction
+# from sahi.utils.cv import visualize_object_predictions, read_image
+import os 
+import requests
+
 from ultralyticsplus import YOLO, render_result
 
 # from ultralyticsplus import render_result
@@ -19,7 +22,7 @@ image_path = [['test_images/2a998cfb0901db5f8210.jpg','linhcuem/chamdiem_yolov8_
              ['test_images/ee106392e56837366e79.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/f88d2214a4ee76b02fff.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45]]
 
 # Load YOLO model
-model = YOLO('linhcuem/cham_diem_yolov8')
+model = YOLO('linhcuem/chamdiem_yolov8_ver10')
 
 ###################################################
 def yolov8_img_inference(
@@ -36,10 +39,11 @@ def yolov8_img_inference(
     model.overrides['iou'] = iou_threshold
     model.overrides['agnostic_nms'] = False
     model.overrides['max_det'] = 1000
-    image = read_image
+    # image = read_image
     results = model.predict(image)
-    results = render_result(model=model, image=image, result=results[0])
-    
+    render = render_result(model=model, image=image, result=results[0])
+
+    return render 
     
     # results = model.predict(image, imgsz=image_size, return_outputs=True)
     # results = model.predict(image)