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