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Duplicate from fl399/matcha_chartqa
Browse filesCo-authored-by: Fangyu Liu <fl399@users.noreply.huggingface.co>
- .DS_Store +0 -0
- .gitattributes +34 -0
- README.md +13 -0
- app.py +48 -0
- requirements.txt +3 -0
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README.md
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---
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title: MatCha ChartQA
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emoji: 📊
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colorFrom: red
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colorTo: pink
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sdk: gradio
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sdk_version: 3.24.1
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app_file: app.py
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pinned: false
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duplicated_from: fl399/matcha_chartqa
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import gradio as gr
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from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
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import requests
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from PIL import Image
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import torch
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torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/20294671002019.png', 'chart_example.png')
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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')
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torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/test/png/18143564004789.png', 'chart_example_3.png')
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torch.hub.download_url_to_file('https://sharkcoder.com/files/article/matplotlib-bar-plot.png', 'chart_example_4.png')
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model_name = "google/matcha-chartqa"
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model = Pix2StructForConditionalGeneration.from_pretrained(model_name)
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processor = Pix2StructProcessor.from_pretrained(model_name)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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def filter_output(output):
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return output.replace("<0x0A>", "")
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def chart_qa(image, question):
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inputs = processor(images=image, text=question, return_tensors="pt").to(device)
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predictions = model.generate(**inputs, max_new_tokens=512)
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return filter_output(processor.decode(predictions[0], skip_special_tokens=True))
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image = gr.inputs.Image(type="pil", label="Chart")
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question = gr.inputs.Textbox(label="Question")
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answer = gr.outputs.Textbox(label="Model Output")
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examples = [["chart_example.png", "Which country has the second highest death rate?"],
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["chart_example_2.png", "What is the B2B sales in 2017?"],
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["chart_example_3.png", "Which country has the lowest CPA received across all times?"],
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["chart_example_4.png", "How much revenue did Furious 7 make?"]]
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title = "Interactive demo: Chart QA with MatCha🍵"
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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)"
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interface = gr.Interface(fn=chart_qa,
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inputs=[image, question],
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outputs=answer,
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examples=examples,
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title=title,
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description=description,
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theme='gradio/soft',
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enable_queue=True)
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interface.launch()
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requirements.txt
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gradio==3.26.0
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torch
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git+https://github.com/huggingface/transformers
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