Update

app.py

@@ -18,18 +18,34 @@ def yolov7_inference(
         
 
 inputs = [
-    gr.inputs.Image(type="pil", label="Input Image"),
+    gr.inputs.Image(shape=(640, 480), type="pil", label="Input Image"),
     gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.50, step=0.05, label="Confidence Threshold"),
 ]
 
+outputs = [
+    gr.outputs.Image(type="filepath").style(height=400, width=400),
+    
+]
+
+css = ".output_image {height: 40rem !important; width: 100% !important;}"
+
 demo_app = gr.Interface(
     fn=yolov7_inference,
     inputs=inputs,
-    outputs=gr.outputs.Image(type="filepath", label="Output Image"),
+    outputs=outputs,
     title="Detection of jar lid defects (Yolov7)",
-    description = "App detecting jar lids that are damaged versus intact. Type of damage is deformation, hole or scratch | Ruthger Righart ",
+    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>
+
+Contact: Ruthger Righart
+
+Email: rrighart@googlemail.com
+
+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', 0.50], ['example2.JPG', 0.50], ['example3.JPG', 0.50]],
+    css=css,
     cache_examples=True,
 )
 demo_app.launch(debug=False, enable_queue=True)