Downloads · 30 days
15
2% of all-time downloads
svjack/question_generator_by_zh_on_pic
question_generator_by_zh_on_pic is a machine learning model from svjack. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
<img src="https://raw.githubusercontent.com/svjack/docvqa-gen/main/imgs/zhimg.png" width = "500px" height = "500px"/
Downloads · 30 days
15
2% of all-time downloads
All-time downloads
896
Public
Repo size
4.1 GB
Likes
1
Public
Click a slice to open those files.
.ckpt3.2 GB · 79%
From the Hugging Face model README
pip install torch
pip install transformers==4.11.3
pip install opencv-python==4.6.0.66
pip install donut-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"
}
'''