Upload app.py

app.py

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+
+# import gradio as gr
+
+# # 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
+
+# # 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
+
+# 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.")
+    
+#     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)
+        
+    # 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=2)
+
+# io = gr.Interface(
+#     fn=predict, inputs=inputs, outputs=outputs, examples=["dog.jpg"]
+# )
+# io.launch(inline=False, share=True)
+
+
+
+
+
+
+
+# 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()
+
+    
+
+# 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(input_img):
+    predictions = pipeline(input_img)
+    return input_img, {p["label"]: p["score"] for p in predictions} 
+
+gradio_app = gr.Interface(
+    predict,
+    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
+    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
+    title="Hot Dog? Or Not?",
+)
+
+if __name__ == "__main__":
+    gradio_app.launch()