app.py

import gradio as gr

from transformers import ImageClassificationPipeline, AutoImageProcessor, AutoModelForImageClassification, ResNetForImageClassification
# 
# 
import torch

from transformers import pipeline

feature_extractor = AutoImageProcessor.from_pretrained("artfan123/resnet-18-finetuned-ai-art")
model = AutoModelForImageClassification.from_pretrained("artfan123/resnet-18-finetuned-ai-art")

image_pipe = ImageClassificationPipeline(model=model, feature_extractor=feature_extractor)

def classify_image(image):
  results = image_pipe(image)
  # convert to format Gradio expects
  output = {}
  for prediction in results:
    predicted_label = prediction['label']
    score = prediction['score']
    output[predicted_label] = score
  return output

image = gr.inputs.Image(type="pil")
label = gr.outputs.Label(num_top_classes=2)
examples = [['50.jpg'], ['344.jpg'],['24.jpg'], ['339.jpg'], ['105.jpg']]
title = "AI Art Detector"
description = "A deep learning model that detects whether an image is AI generated or human made. Upload image or use the example images below."
gr.Interface(fn=classify_image, inputs=image, outputs=label, title=title, description=description, examples=examples, enable_queue=True).launch(debug=True)

# if __name__ == "__main__":
#     with gr.Blocks() as demo:

#         with gr.Row():
#             with gr.Column(scale=4.5):
#                 with gr.Group():
#                     image_prompt = gr.Image(type='pil', shape=[512,512],label="Input Image")
#                     gr.Examples(inputs=image_prompt,examples=[['50.jpg'], ['344.jpg'],['24.jpg'], ['339.jpg'], ['105.jpg']])
#                     with gr.Row():
#                         clear_button = gr.Button('Clear')
#                         run_button = gr.Button('Predict')

#             with gr.Column(scale=5.5):
#                 image_output = gr.Image(type='pil', shape=[512,512], label="Prediction")
#                 clear_button.click(lambda: None, None, image_prompt, queue=False)
#         clear_button.click(lambda: None, None, image_output, queue=False)

#         run_button.click(fn=segment,inputs=[image_prompt],
#                          outputs=[image_output])
#     demo.queue().launch(share=True)