app.py

# 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("./Object-Detection/helper.py")  # Adjust the path as necessary to point to the directory where helper.py is located

#from Object-Detection.helper import ignore_warnings
#from helper import ignore_warnings
from helper import *
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 Object-Detection.helper import import load_image_from_url, render_results_in_image
#from 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

iface = 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")
)

iface.launch()