blocks

app.py

@@ -1,8 +1,9 @@
-
 import gradio as gr
+import os
 import torch
 
-#############
+def update_value(val):
+  return f'Value is set to {val}'
 
 def yolov7_inference(
     image: gr.Image = None,
@@ -16,39 +17,27 @@ def yolov7_inference(
     results = model([image], size=640)
     return results.render()[0]
         
-inputs = [
-    gr.Image(type="filepath", label="Input"),
-    gr.Slider(minimum=0.0, maximum=1.0, value=0.2, step=0.05, label="Confidence Threshold", interactive=True),
-]
-
-outputs = [
-    gr.Image(type="filepath"),
-    
-]
-
-css = ".output_image {height: 40rem !important; width: 100% !important;}"
-
-demo = gr.Interface(
-    fn=yolov7_inference,
-    inputs=inputs,
-    outputs=outputs,
-    title="The detection of jar lid defects using Yolov7",
-    description = """
-This application is detecting damaged jar lids. Type of damages include deformations, holes or scratches. The object detection notebook can be found at <a href="https://www.kaggle.com/rrighart">Kaggle</a>
+demo = gr.Blocks()
 
-Contact: Ruthger Righart
+with demo:
 
-Email: rrighart@googlemail.com
+  dd = gr.Interface(
+    yolov7_inference,
+    gr.Image(type="pil"),
+    "image",
+    title="The detection of jar lid defects using Yolov7",
 
-Web: <a href="https://www.rrighart.com">www.rrighart.com</a> 
-""",
-    article = "<p style='text-align: center'><a href='https://www.rrighart.com' target='_blank'>Webpage</a></p> <p style='text-align: center'><a href='https://www.kaggle.com/code/rrighart/detection-of-product-defects-using-yolov7' target='_blank'>Kaggle</a></p>",
-    examples = [['example1.JPG'], ['example2.JPG'], ['example3.JPG']],
-    #examples = [['example1.JPG', 0.50], ['example2.JPG', 0.50], ['example3.JPG', 0.50]],
-    css=css,
-    cache_examples=True,
+    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"),
+    ],
 )
 
+  md = gr.Markdown("Confidence Threshold")
+  conf_threshold = gr.Slider(minimum=0, maximum=1, step=0.1, label='Value')
+  #inp = gr.Slider(minimum=0.0, maximum=1.0, value=0.2, step=0.05, label="Value"),
+  #inp.change(fn=yolov7_inference, inputs=inp, outputs=md)
+  conf_threshold.change(fn=update_value, inputs=conf_threshold, outputs=md)
 
-demo.queue().launch(server_name="0.0.0.0", server_port=7860, debug=False, inline=True)
-
+demo.launch()

slider.py

@@ -0,0 +1,14 @@
+import gradio as gr
+
+def update_value(val):
+  return f'Value is set to {val}'
+
+demo = gr.Blocks()
+
+with demo:
+  inp = gr.Slider(0, 100, label='Value')
+  md = gr.Markdown('Select a value')
+
+  inp.change(fn=update_value, inputs=inp, outputs=md)
+
+demo.launch()