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| import gradio as gr | |
| import requests | |
| from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor | |
| import os | |
| ##Bloom | |
| API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom" | |
| HF_TOKEN = os.environ["HF_TOKEN"] | |
| headers = {"Authorization": f"Bearer {HF_TOKEN}"} | |
| def _add_markup(table): | |
| parts = [p.strip() for p in table.splitlines(keepends=False)] | |
| if parts[0].startswith('TITLE'): | |
| result = f"Title: {parts[0].split(' | ')[1].strip()}\n" | |
| rows = parts[1:] | |
| else: | |
| result = '' | |
| rows = parts | |
| prefixes = ['Header: '] + [f'Row {i+1}: ' for i in range(len(rows) - 1)] | |
| return result + '\n'.join(prefix + row for prefix, row in zip(prefixes, rows)) | |
| _TABLE = """Year | Democrats | Republicans | Independents | |
| 2004 | 68.1% | 45.0% | 53.0% | |
| 2006 | 58.0% | 42.0% | 53.0% | |
| 2007 | 59.0% | 38.0% | 45.0% | |
| 2009 | 72.0% | 49.0% | 60.0% | |
| 2011 | 71.0% | 51.2% | 58.0% | |
| 2012 | 70.0% | 48.0% | 53.0% | |
| 2013 | 72.0% | 41.0% | 60.0%""" | |
| _INSTRUCTION = 'Read the table below to answer the following questions.' | |
| _TEMPLATE = f"""{_INSTRUCTION} | |
| {_add_markup(_TABLE)} | |
| Q: In which year republicans have the lowest favor rate? | |
| A: Let's find the column of republicans. Then let's extract the favor rates, they [45.0, 42.0, 38.0, 49.0, 51.2, 48.0, 41.0]. The smallest number is 38.0, that's Row 3. Row 3 is year 2007. The answer is 2007. | |
| Q: What is the sum of Democrats' favor rates of 2004, 2012, and 2013? | |
| A: Let's find the rows of years 2004, 2012, and 2013. We find Row 1, 6, 7. The favor dates of Demoncrats on that 3 rows are 68.1, 70.0, and 72.0. 68.1+70.0+72=210.1. The answer is 210.1. | |
| Q: By how many points do Independents surpass Republicans in the year of 2011? | |
| A: Let's find the row with year = 2011. We find Row 5. We extract Independents and Republicans' numbers. They are 58.0 and 51.2. 58.0-51.2=6.8. The answer is 6.8. | |
| Q: Which group has the overall worst performance? | |
| A: Let's sample a couple of years. In Row 1, year 2004, we find Republicans having the lowest favor rate 45.0 (since 45.0<68.1, 45.0<53.0). In year 2006, Row 2, we find Republicans having the lowest favor rate 42.0 (42.0<58.0, 42.0<53.0). The trend continues to other years. The answer is Republicans. | |
| Q: Which party has the second highest favor rates in 2007? | |
| A: Let's find the row of year 2007, that's Row 3. Let's extract the numbers on Row 3: [59.0, 38.0, 45.0]. 45.0 is the second highest. 45.0 is the number of Independents. The answer is Independents. | |
| {_INSTRUCTION}""" | |
| def text_generate(prompt, table, problem): | |
| p = prompt + "\n" + table + "\n" + "Q: " + problem | |
| # print(f"Final prompt is : {p}") | |
| json_ = {"inputs": p, | |
| "parameters": | |
| { | |
| "top_p": 0.9, | |
| "temperature": 1.1, | |
| "max_new_tokens": 64, | |
| "return_full_text": True | |
| }, "options": | |
| { | |
| "use_cache": True, | |
| "wait_for_model":True | |
| },} | |
| response = requests.post(API_URL, headers=headers, json=json_) | |
| print(f"Response is : {response}") | |
| output = response.json() | |
| print(f"output is : {output}") #{output}") | |
| output_tmp = output[0]['generated_text'] | |
| print(f"output_tmp is: {output_tmp}") | |
| #solution = output_tmp.split("\nQ:")[0] #output[0]['generated_text'].split("Q:")[0] # +"." | |
| #print(f"Final response after splits is: {solution}") | |
| #return solution | |
| return output_tmp | |
| model_deplot = Pix2StructForConditionalGeneration.from_pretrained("belkada/deplot") | |
| processor_deplot = Pix2StructProcessor.from_pretrained("belkada/deplot") | |
| def process_document(image, question): | |
| # image = Image.open(image) | |
| inputs = processor_deplot(images=image, text="Generate the underlying data table for the figure below:", return_tensors="pt") | |
| predictions = model_deplot.generate(**inputs) | |
| table = processor_deplot.decode(predictions[0], skip_special_tokens=True) | |
| # send prompt+table to LLM | |
| res = text_generate(_TEMPLATE, table, question) | |
| print (res) | |
| description = "Demo for pix2struct fine-tuned on DocVQA (document visual question answering). To use it, simply upload your image and type a question and click 'submit', or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/pdf/2210.03347.pdf' target='_blank'>PIX2STRUCT: SCREENSHOT PARSING AS PRETRAINING FOR VISUAL LANGUAGE UNDERSTANDING</a></p>" | |
| demo = gr.Interface( | |
| fn=process_document, | |
| inputs=["image", "text"], | |
| outputs="text", | |
| title="Demo: deplot+llm test", | |
| description=description, | |
| article=article, | |
| enable_queue=True, | |
| examples=[["example_1.png", "When is the coffee break?"], ["example_2.jpeg", "What's the population of Stoddard?"]], | |
| cache_examples=False) | |
| demo.launch() |