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| import gradio as gr | |
| from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor | |
| import requests | |
| from PIL import Image | |
| import torch | |
| torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/20294671002019.png', 'chart_example.png') | |
| torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/test/png/multi_col_1081.png', 'chart_example_2.png') | |
| torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/test/png/18143564004789.png', 'chart_example_3.png') | |
| torch.hub.download_url_to_file('https://sharkcoder.com/files/article/matplotlib-bar-plot.png', 'chart_example_4.png') | |
| model_name = "google/matcha-chartqa" | |
| model = Pix2StructForConditionalGeneration.from_pretrained(model_name) | |
| processor = Pix2StructProcessor.from_pretrained(model_name) | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| def filter_output(output): | |
| return output.replace("<0x0A>", "") | |
| def chart_qa(image, question): | |
| inputs = processor(images=image, text=question, return_tensors="pt").to(device) | |
| predictions = model.generate(**inputs, max_new_tokens=512) | |
| return filter_output(processor.decode(predictions[0], skip_special_tokens=True)) | |
| image = gr.inputs.Image(type="pil", label="Chart") | |
| question = gr.inputs.Textbox(label="Question") | |
| answer = gr.outputs.Textbox(label="Model Output") | |
| examples = [["chart_example.png", "Which country has the second highest death rate?"], | |
| ["chart_example_2.png", "What is the B2B sales in 2017?"], | |
| ["chart_example_3.png", "Which country has the lowest CPA received across all times?"], | |
| ["chart_example_4.png", "How much revenue did Furious 7 make?"]] | |
| title = "Interactive demo: Chart QA with MatCha🍵" | |
| description = "Gradio Demo for the [MatCha](https://arxiv.org/abs/2212.09662) model, fine-tuned on the [ChartQA](https://paperswithcode.com/dataset/chartqa) dataset. To use it, simply upload your image and click 'submit', or click one of the examples to load them. \n Quick links: [[paper]](https://arxiv.org/abs/2212.09662) [[google-ai blog]](https://ai.googleblog.com/2023/05/foundation-models-for-reasoning-on.html) [[code]](https://github.com/google-research/google-research/tree/master/deplot)" | |
| interface = gr.Interface(fn=chart_qa, | |
| inputs=[image, question], | |
| outputs=answer, | |
| examples=examples, | |
| title=title, | |
| description=description, | |
| theme='gradio/soft', | |
| enable_queue=True) | |
| interface.launch() |