import gradio as gr # from PIL import Image from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor 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" + _INSTRUCTION + "\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 = "

PIX2STRUCT: SCREENSHOT PARSING AS PRETRAINING FOR VISUAL LANGUAGE UNDERSTANDING

" 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()