Upload app.py

app.py

@@ -1,76 +1,117 @@
 
-import gradio as gr
+# import gradio as gr
 
-# Use a pipeline as a high-level helper
-from transformers import pipeline
+# # Use a pipeline as a high-level helper
+# from transformers import pipeline
 
-# Use a pipeline as a high-level helper
-# Load model directly
-from transformers import AutoImageProcessor, AutoModelForImageClassification
+# # Use a pipeline as a high-level helper
+# # Load model directly
+# from transformers import AutoImageProcessor, AutoModelForImageClassification
 
-# processor = AutoImageProcessor.from_pretrained("AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat")
-# model = AutoModelForImageClassification.from_pretrained("AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat")
-pipe = pipeline("image-classification", model="AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat")
+# # processor = AutoImageProcessor.from_pretrained("AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat")
+# # model = AutoModelForImageClassification.from_pretrained("AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat")
+# pipe = pipeline("image-classification", model="AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat")
 
 
-# $ pip install gradio_client fastapi uvicorn
+# # $ pip install gradio_client fastapi uvicorn
 
-import requests
-from PIL import Image
-from transformers import pipeline
-import io
-import base64
+# import requests
+# from PIL import Image
+# from transformers import pipeline
+# import io
+# import base64
 
 # Initialize the pipeline
 # pipe = pipeline('image-classification')
 
-def load_image_from_path(image_path):
-    return Image.open(image_path)
-
-def load_image_from_url(image_url):
-    response = requests.get(image_url)
-    return Image.open(io.BytesIO(response.content))
-
-def load_image_from_base64(base64_string):
-    image_data = base64.b64decode(base64_string)
-    return Image.open(io.BytesIO(image_data))
-
-def predict(image_input):
-    if isinstance(image_input, str):
-        if image_input.startswith('http'):
-            image = load_image_from_url(image_input)
-        elif image_input.startswith('/'):
-            image = load_image_from_path(image_input)
-        else:
-            image = load_image_from_base64(image_input)
-    elif isinstance(image_input, Image.Image):
-        image = image_input
-    else:
-        raise ValueError("Incorrect format used for image. Should be an URL linking to an image, a base64 string, a local path, or a PIL image.")
+# def load_image_from_path(image_path):
+#     return Image.open(image_path)
+
+# def load_image_from_url(image_url):
+#     response = requests.get(image_url)
+#     return Image.open(io.BytesIO(response.content))
+
+# def load_image_from_base64(base64_string):
+#     image_data = base64.b64decode(base64_string)
+#     return Image.open(io.BytesIO(image_data))
+
+# def predict(image_input):
+#     if isinstance(image_input, str):
+#         if image_input.startswith('http'):
+#             image = load_image_from_url(image_input)
+#         elif image_input.startswith('/'):
+#             image = load_image_from_path(image_input)
+#         else:
+#             image = load_image_from_base64(image_input)
+#     elif isinstance(image_input, Image.Image):
+#         image = image_input
+#     else:
+#         raise ValueError("Incorrect format used for image. Should be an URL linking to an image, a base64 string, a local path, or a PIL image.")
     
-    return pipe(image)
+#     return pipe(image)
 
 
 # def predict(image):
 #   return pipe(image)
 
-def main():
-    # image_input = 'path_or_url_or_base64'  # Update with actual input
-    # output = predict(image_input)
-    # print(output)
+# def main():
+#     # image_input = 'path_or_url_or_base64'  # Update with actual input
+#     # output = predict(image_input)
+#     # print(output)
         
-    demo = gr.Interface(
-      fn=predict,
-      inputs='image',
-      outputs='text',
-    )
-
-    demo.launch()
+    # demo = gr.Interface(
+    #   fn=predict,
+    #   inputs='image',
+    #   outputs='text',
+    # )
+
+    # demo.launch()
+# import requests
+# import torch
+# from PIL import Image
+# from torchvision import transforms
+
+# def predict(inp):
+#     inp = Image.fromarray(inp.astype("uint8"), "RGB")
+#     inp = transforms.ToTensor()(inp).unsqueeze(0)
+#     with torch.no_grad():
+#         prediction = torch.nn.functional.softmax(model(inp.to(device))[0], dim=0)
+#     return {labels[i]: float(prediction[i]) for i in range(1000)}
+
+
+# inputs = gr.Image()
+# outputs = gr.Label(num_top_classes=3)
+
+# io = gr.Interface(
+#     fn=predict, inputs=inputs, outputs=outputs, examples=["dog.jpg"]
+# )
+# io.launch(inline=False, share=True)
 
     
 
-if __name__ == "__main__":
-    main()
+# if __name__ == "__main__":
+#     main()
+
+
+
+
+
+
+import gradio as gr
+from transformers import pipeline
+
+pipeline = pipeline("image-classification", model="AZIIIIIIIIZ/vit-base-patch16-224-finetuned-eurosat")
+
+def predict(image):
+  predictions = pipeline(image)
+  return {p["label"]: p["score"] for p in predictions}
 
+gr.Interface(
+    predict,
+    inputs=gr.inputs.Image(label="Upload Image", type="filepath"),
+    outputs=gr.outputs.Label(num_top_classes=2),
+    title="AI Generated? Or Not?",
+    allow_flagging="manual"
+).launch()