Update app.py

app.py

@@ -2,14 +2,10 @@ import supervision as sv
 import gradio as gr
 from ultralytics import YOLO
 import sahi
-
+import numpy as np
 
 
 # Images
-sahi.utils.file.download_from_url(
-    "https://transform.roboflow.com/zD7y6XOoQnh7WC160Ae7/4d51f997137c0dca78fa2c9154e0b51a/thumb.jpg",
-    "f1.jpg",
-)
 sahi.utils.file.download_from_url(
     "https://transform.roboflow.com/zD7y6XOoQnh7WC160Ae7/48174c7c26c2cbca52b084ebbb03d215/thumb.jpg",
     "f2.jpg",
@@ -28,19 +24,17 @@ annotatormask=sv.MaskAnnotator()
 
 def yolov8_inference(
     image: gr.inputs.Image = None,
-    model_name: gr.inputs.Dropdown = None,
-    image_size: gr.inputs.Slider = 360,
-    conf_threshold: gr.inputs.Slider = 0.25,
+    conf_threshold: gr.inputs.Slider = 0.5,
     iou_threshold: gr.inputs.Slider = 0.45,
 ):
 
-
-    model = YOLO("https://huggingface.co/spaces/devisionx/Second_demo/blob/main/best.pt")
-
-    results = model(image,conf=conf_threshold,iou=iou_threshold ,imgsz=360)[0]
+    image=image[:, :, ::-1].astype(np.uint8)
+    model = YOLO("/content/segment/train/weights/best.pt")
+    results = model(image,imgsz=320)[0]
+    image=image[:, :, ::-1].astype(np.uint8)
     detections = sv.Detections.from_yolov8(results)
-    annotated_image = annotatorbbox.annotate(scene=image, detections=detections)
-    annotated_image = annotatormask.annotate(scene=annotated_image, detections=detections)
+    annotated_image = annotatormask.annotate(scene=image, detections=detections)
+    annotated_image = annotatorbbox.annotate(scene=annotated_image , detections=detections)
 
 
 
@@ -61,7 +55,6 @@ outputs = gr.Image(type="filepath", label="Output Image")
 title = "Ultralytics YOLOv8 Segmentation Demo"
 import os
 examples = [
-    ["f1.jpg", 0.6, 0.45],
     ["f2.jpg", 0.25, 0.45],
     ["f3.jpg", 0.25, 0.45],
 ]
@@ -73,4 +66,4 @@ demo_app = gr.Interface(examples=examples,
     cache_examples=True,
     theme="default",
 )
-demo_app.launch(debug=True, enable_queue=True)
+demo_app.launch(debug=False, enable_queue=True)