Update app.py

app.py

@@ -12,10 +12,10 @@ logging.set_verbosity_error()
 
 #from helper import ignore_warnings
 #ignore_warnings()
-#import sys
-#sys.path.append("./Open_Source_Models_with_Hugging_Face/Object_Detection/helper.py")  # Adjust the path as necessary to point to the directory where helper.py is located
+import sys
+sys.path.append("./Object-Detection/helper.py")  # Adjust the path as necessary to point to the directory where helper.py is located
 
-from Open_Source_Models_with_Hugging_Face.Object_Detection.helper import ignore_warnings
+from Object-Detection.helper import ignore_warnings
 ignore_warnings()
 
 # Import the pipeline function from the transformers library
@@ -26,7 +26,7 @@ pipe = pipeline("object-detection", model="facebook/detr-resnet-50")
 
 #sys.path.append("./Open_Source_Models_with_Hugging_Face/Object_Detection/helper.py")  # Adjust the path as necessary to point to the directory where helper.py is located
 
-#from Open_Source_Models_with_Hugging_Face.Object_Detection.helper import load_image_from_url, render_results_in_image
+from Object-Detection.helper import import load_image_from_url, render_results_in_image
 
 # Load the image from a file
 #raw_image = Image.open('./Open_Source_Models_with_Hugging_Face/Object_Detection/kittens.jpeg')
@@ -43,7 +43,7 @@ def get_pipeline_prediction(pil_image):
     processed_image = render_results_in_image(pil_image, pipeline_output)
     return processed_image
 
-demo = gr.Interface(
+iface = gr.Interface(
   fn=get_pipeline_prediction,
   inputs=gr.Image(label="Input image", 
                   type="pil"),
@@ -51,7 +51,15 @@ demo = gr.Interface(
                    type="pil")
 )
 
+iface.launch()
+
+
+
+
+
+
+
+
 
-demo.launch(share=True)