Create README.md

README.md

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+---
+language:
+- zh
+library_name: transformers
+tags:
+- donut
+- donut-python
+---
+
+### Installtion
+```bash
+pip install torch 
+pip install transformers==4.11.3
+pip install opencv-python==4.6.0.66
+pip install donut-python
+```
+
+### Usage 
+```python
+import sys
+import os
+import pandas as pd
+import numpy as np
+import shutil
+
+from tqdm import tqdm
+import re
+
+from donut import DonutModel
+import torch
+from PIL import Image
+
+zh_model_path = "question_generator_by_zh_on_pic"
+
+task_prompt = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
+zh_pretrained_model = DonutModel.from_pretrained(zh_model_path)
+
+if torch.cuda.is_available():
+    zh_pretrained_model.half()
+    device = torch.device("cuda")
+    zh_pretrained_model.to(device)
+
+zh_pretrained_model.eval()
+print("have load !")
+
+def demo_process_vqa(input_img, question):
+    #input_img = Image.fromarray(input_img)
+    global zh_pretrained_model, task_prompt
+    user_prompt = task_prompt.replace("{user_input}", question)
+    output = zh_pretrained_model.inference(input_img, prompt=user_prompt)["predictions"][0]
+    req = {
+        "question": output["answer"],
+        "answer": output["question"]
+    }
+    return req
+
+
+img_path = "zh_img.png"
+demo_process_vqa(Image.open(img_path), "零钱通", )
+
+'''
+{
+  "question": "支付方式是什么?",
+  "answer": "零钱通"
+}
+'''
+
+```
+
+### Sample Image 
+<img src="https://raw.githubusercontent.com/svjack/docvqa-gen/main/imgs/zh_img.png" width = "500px" height = "500px"/>