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| import os | |
| import torch | |
| import requests | |
| import gradio as gr | |
| import transformers | |
| from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor | |
| from peft import PeftModel | |
| ## CoT prompts | |
| 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"""First read an example then the complete question for the second table. | |
| ------------ | |
| {_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}""" | |
| ## alpaca-lora | |
| assert ( | |
| "LlamaTokenizer" in transformers._import_structure["models.llama"] | |
| ), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git" | |
| from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig | |
| tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf") | |
| BASE_MODEL = "decapoda-research/llama-7b-hf" | |
| LORA_WEIGHTS = "tloen/alpaca-lora-7b" | |
| if torch.cuda.is_available(): | |
| device = "cuda" | |
| else: | |
| device = "cpu" | |
| try: | |
| if torch.backends.mps.is_available(): | |
| device = "mps" | |
| except: | |
| pass | |
| if device == "cuda": | |
| model = LlamaForCausalLM.from_pretrained( | |
| BASE_MODEL, | |
| load_in_8bit=False, | |
| torch_dtype=torch.float16, | |
| device_map="auto", | |
| ) | |
| model = PeftModel.from_pretrained( | |
| model, LORA_WEIGHTS, torch_dtype=torch.float16, force_download=True | |
| ) | |
| elif device == "mps": | |
| model = LlamaForCausalLM.from_pretrained( | |
| BASE_MODEL, | |
| device_map={"": device}, | |
| torch_dtype=torch.float16, | |
| ) | |
| model = PeftModel.from_pretrained( | |
| model, | |
| LORA_WEIGHTS, | |
| device_map={"": device}, | |
| torch_dtype=torch.float16, | |
| ) | |
| else: | |
| model = LlamaForCausalLM.from_pretrained( | |
| BASE_MODEL, device_map={"": device}, low_cpu_mem_usage=True | |
| ) | |
| model = PeftModel.from_pretrained( | |
| model, | |
| LORA_WEIGHTS, | |
| device_map={"": device}, | |
| ) | |
| if device != "cpu": | |
| model.half() | |
| model.eval() | |
| if torch.__version__ >= "2": | |
| model = torch.compile(model) | |
| def evaluate( | |
| table, | |
| question, | |
| input=None, | |
| temperature=0.1, | |
| top_p=0.75, | |
| top_k=40, | |
| num_beams=4, | |
| max_new_tokens=128, | |
| **kwargs, | |
| ): | |
| prompt = _TEMPLATE + "\n" + _add_markup(table) + "\n" + "Q: " + question + "\n" + "A:" | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| input_ids = inputs["input_ids"].to(device) | |
| generation_config = GenerationConfig( | |
| temperature=temperature, | |
| top_p=top_p, | |
| top_k=top_k, | |
| num_beams=num_beams, | |
| **kwargs, | |
| ) | |
| with torch.no_grad(): | |
| generation_output = model.generate( | |
| input_ids=input_ids, | |
| generation_config=generation_config, | |
| return_dict_in_generate=True, | |
| output_scores=True, | |
| max_new_tokens=max_new_tokens, | |
| ) | |
| s = generation_output.sequences[0] | |
| output = tokenizer.decode(s) | |
| #return output.split("A:")[-1].strip() | |
| return output | |
| ## deplot models | |
| model_deplot = Pix2StructForConditionalGeneration.from_pretrained("google/deplot", torch_dtype=torch.bfloat16).to(0) | |
| processor_deplot = Pix2StructProcessor.from_pretrained("google/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").to(0, torch.bfloat16) | |
| predictions = model_deplot.generate(**inputs, max_new_tokens=512) | |
| table = processor_deplot.decode(predictions[0], skip_special_tokens=True).replace("<0x0A>", "\n") | |
| # send prompt+table to LLM | |
| res = evaluate(table, question) | |
| #return res + "\n\n" + res.split("A:")[-1] | |
| return [table, res.split("A:")[-1]] | |
| description = "Demo for DePlot+LLM for QA and summarisation. [DePlot](https://arxiv.org/abs/2212.10505) is an image-to-text model that converts plots and charts into a textual sequence. The sequence then is used to prompt LLM for chain-of-thought reasoning. The current underlying LLM is [alpaca-lora](https://huggingface.co/spaces/tloen/alpaca-lora). To use it, simply upload your image and type a question or instruction 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/abs/2212.10505' target='_blank'>DePlot: One-shot visual language reasoning by plot-to-table translation</a></p>" | |
| demo = gr.Interface( | |
| fn=process_document, | |
| inputs=["image", "text"], | |
| outputs=[ | |
| gr.inputs.Textbox( | |
| lines=8, | |
| label="Intermediate Table", | |
| ), | |
| gr.inputs.Textbox( | |
| lines=5, | |
| label="Output", | |
| ) | |
| ], | |
| title="DePlot+LLM (Multimodal chain-of-thought reasoning on plots)", | |
| description=description, | |
| article=article, | |
| enable_queue=True, | |
| examples=[["deplot_case_study_m1.png", "What is the sum of numbers of Indonesia and Ireland? Remember to think step by step."], | |
| ["deplot_case_study_m1.png", "Summarise the chart for me please."], | |
| ["deplot_case_study_3.png", "By how much did China's growth rate drop? Think step by step."], | |
| ["deplot_case_study_4.png", "How many papers are submitted in 2020?"], | |
| ["deplot_case_study_x2.png", "Summarise the chart for me please."]], | |
| cache_examples=True) | |
| demo.launch(debug=True) |