Update app.py

app.py

@@ -1,24 +1,24 @@
-import gradio as gr
-import torch
-from PIL import Image
-from ultralytics import YOLO
-
-model = YOLO(r'pcb-best.pt')
-
-def predict(img, conf, iou):
-    results = model.predict(img, conf=conf, iou=iou)
-    for i, r in enumerate(results):
-        # Plot results image
-        im_bgr = r.plot()  # BGR-order numpy array
-        im_rgb = Image.fromarray(im_bgr[..., ::-1])  # RGB-order PIL image
-
-        # Show results to screen (in supported environments)
-        return im_rgb
-
-
-base_conf, base_iou = 0.25, 0.45
-title = "基于YOLO-V8的PCB电路板缺陷检测"
-des = "鼠标点击上传图片即可检测缺陷，可通过鼠标调整预测置信度，还可点击网页最下方示例图片进行预测"
-gr.Interface(inputs=['image',gr.Slider(maximum=1, minimum=0, value=base_conf), gr.Slider(maximum=1, minimum=0, value=base_iou)],
-             outputs=["image"], examples='example1.jpg', fn=predict, title=title, description=des).launch()
-
+import gradio as gr
+import torch
+from PIL import Image
+from ultralytics import YOLO
+
+model = YOLO(r'pcb-best.pt')
+
+def predict(img, conf, iou):
+    results = model.predict(img, conf=conf, iou=iou)
+    for i, r in enumerate(results):
+        # Plot results image
+        im_bgr = r.plot()  # BGR-order numpy array
+        im_rgb = Image.fromarray(im_bgr[..., ::-1])  # RGB-order PIL image
+
+        # Show results to screen (in supported environments)
+        return im_rgb
+
+
+base_conf, base_iou = 0.25, 0.45
+title = "基于YOLO-V8的PCB电路板缺陷检测"
+des = "鼠标点击上传图片即可检测缺陷，可通过鼠标调整预测置信度，还可点击网页最下方示例图片进行预测"
+gr.Interface(inputs=['image',gr.Slider(maximum=1, minimum=0, value=base_conf), gr.Slider(maximum=1, minimum=0, value=base_iou)],
+             outputs=["image"], fn=predict, title=title, description=des).launch()
+