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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() |