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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=3) | |
| # io = gr.Interface( | |
| # fn=predict, inputs=inputs, outputs=outputs, examples=["dog.jpg"] | |
| # ) | |
| # io.launch(inline=False, share=True) | |
| # 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() | |