changes app

app.py

@@ -1,20 +1,38 @@
 import gradio as gr
 import os
+import torch
 
+def update_value(val):
+  return f'Value is set to {val}'
 
-def image_mod(image):
-    return image.rotate(45)
+def yolov7_inference(
+    image: gr.Image = None,
+    conf_threshold: gr.Slider = 0.20,
+):
 
-demo = gr.Interface(
-    image_mod,
-    gr.Image(type="pil"),
-    "image",
-    flagging_options=["blurry", "incorrect", "other"],
-    examples=[
-        os.path.join(os.path.dirname(__file__), "example1.JPG"),
-        os.path.join(os.path.dirname(__file__), "example2.JPG"),
-        os.path.join(os.path.dirname(__file__), "example3.JPG"),
-    ],
-)
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    path = 'y7-prdef.pt'
+    model = torch.hub.load("WongKinYiu/yolov7","custom",f"{path}")
+    model.conf = conf_threshold
+    results = model([image], size=640)
+    return results.render()[0]
 
-demo.launch()
+inputs = [
+    gr.Image(label="input image"),
+    gr.Slider(minimum=0, maximum=1, step=0.1, label='Value'),
+]
+
+outputs = [
+   gr.Image(label="output image"),
+]
+
+gr.Interface(
+    fn = yolov7_inference,
+    inputs = inputs,
+    outputs = outputs, 
+    title = "- The detection of jar lid defects using Yolov7 -",
+    description = "contact: rrighart@googlemail.com", 
+
+    examples = [["example1.JPG"], ["example2.JPG"], ["example3.JPG"]],
+
+).launch(debug=True)