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README.md

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 ---
 title: AI Generated Art Classifier
-emoji: 👀
-colorFrom: blue
+emoji: 🌖
+colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 3.50.2
+sdk_version: 3.36.1
 app_file: app.py
 pinned: false
 ---

app.py

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+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)

gitattributes.txt

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requirements.txt

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+torch
+transformers
+pillow
+pip>=23.2
+gradio_client==0.2.7

resnet18_binary_prediction.pth

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resnet34_binary_prediction.pth

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