from transformers import AutoProcessor, Pix2StructForConditionalGeneration, Pix2StructProcessor import requests import json from PIL import Image model = Pix2StructForConditionalGeneration.from_pretrained("google/deplot") processor = AutoProcessor.from_pretrained("google/deplot") # processor = Pix2StructProcessor.from_pretrained('google/deplot') # url = "https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/5090.png" # image = Image.open(requests.get(url, stream=True).raw) image = Image.open('222.png') inputs = processor(images=image, text="Generate underlying data table of the figure below:", return_tensors="pt") predictions = model.generate(**inputs, max_new_tokens=512) print("prediction") print(processor.decode(predictions[0], skip_special_tokens=True)) raw_output = processor.decode(predictions[0], skip_special_tokens=True) split_by_newline = raw_output.split("<0x0A>") result_array = [] for item in split_by_newline:     result_array.append([x.strip() for x in item.split("|")]) print("result:") print(result_array) with open('test.log', mode='w') as file:     for row in result_array:         file.write(" | ".join(row) + "\n")