Create app.py

app.py

@@ -0,0 +1,57 @@
+# Install and update the necessary libraries
+import logging
+import sys
+from PIL import Image
+from transformers import pipeline
+import os
+import gradio as gr
+
+# Suppress non-critical log messages
+from transformers.utils import logging
+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
+
+from Open_Source_Models_with_Hugging_Face.Object_Detection.helper import ignore_warnings
+ignore_warnings()
+
+# Import the pipeline function from the transformers library
+#from transformers import pipeline
+
+# Set up the object detection pipeline.
+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
+
+# Load the image from a file
+#raw_image = Image.open('./Open_Source_Models_with_Hugging_Face/Object_Detection/kittens.jpeg')
+# Resize the image 
+#raw_image.resize((569, 491))
+
+#pipeline_output = pipe(raw_image)
+#processed_image = render_results_in_image(raw_image, pipeline_output)
+#processed_image
+
+# gradio interface 
+def get_pipeline_prediction(pil_image):
+    pipeline_output = pipe(pil_image)
+    processed_image = render_results_in_image(pil_image, pipeline_output)
+    return processed_image
+
+demo = gr.Interface(
+  fn=get_pipeline_prediction,
+  inputs=gr.Image(label="Input image", 
+                  type="pil"),
+  outputs=gr.Image(label="Output image with predicted instances",
+                   type="pil")
+)
+
+
+demo.launch(share=True)
+
+