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import argparse |
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import gradio as gr |
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import os |
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import torch |
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from donut import DonutModel |
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from PIL import Image |
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def demo_process_vqa(input_img, question): |
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global pretrained_model, task_prompt, task_name |
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input_img = Image.fromarray(input_img) |
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user_prompt = task_prompt.replace("{user_input}", question) |
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return pretrained_model.inference(input_img, prompt=user_prompt)["predictions"][0] |
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def demo_process(input_img): |
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global pretrained_model, task_prompt, task_name |
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input_img = Image.fromarray(input_img) |
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best_output = pretrained_model.inference(image=input_img, prompt=task_prompt)["predictions"][0] |
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return best_output["text_sequence"].split(" </s_MachineReadableZone>")[0] |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--task", type=str, default="s_passport") |
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parser.add_argument("--pretrained_path", type=str, default=os.getcwd()) |
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parser.add_argument("--port", type=int, default=12345) |
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parser.add_argument("--url", type=str, default="0.0.0.0") |
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parser.add_argument("--sample_img_path", type=str) |
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args, left_argv = parser.parse_known_args() |
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task_name = args.task |
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if "docvqa" == task_name: |
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task_prompt = "<s_docvqa><s_question>{user_input}</s_question><s_answer>" |
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else: |
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task_prompt = f"<s_{task_name}>" |
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example_sample = [os.path.join("images", image) for image in os.listdir("images")] |
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if args.sample_img_path: |
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example_sample.append(args.sample_img_path) |
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pretrained_model = DonutModel.from_pretrained(args.pretrained_path) |
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if torch.cuda.is_available(): |
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pretrained_model.half() |
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device = torch.device("cuda") |
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pretrained_model.to(device) |
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pretrained_model.eval() |
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gr.Interface( |
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fn=demo_process_vqa if task_name == "docvqa" else demo_process, |
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inputs=["image", "text"] if task_name == "docvqa" else "image", |
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outputs="text", |
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title="Demo of MRZ Extraction model based on 🍩 architecture", |
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examples=example_sample if example_sample else None |
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).launch() |
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