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import gradio as gr |
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from transformers import AutoProcessor, PaliGemmaForConditionalGeneration |
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import requests |
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from PIL import Image |
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import torch, os, re, json |
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import spaces |
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torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/test/png/74801584018932.png', 'chart_example_1.png') |
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torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/multi_col_1229.png', 'chart_example_2.png') |
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model = PaliGemmaForConditionalGeneration.from_pretrained("ahmed-masry/chartgemma") |
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processor = AutoProcessor.from_pretrained("ahmed-masry/chartgemma") |
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@spaces.GPU |
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def predict(image, input_text): |
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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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image = image.convert("RGB") |
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inputs = processor(text=input_text, images=image, return_tensors="pt") |
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inputs = {k: v.to(device) for k, v in inputs.items()} |
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prompt_length = inputs['input_ids'].shape[1] |
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generate_ids = model.generate(**inputs, max_new_tokens=512) |
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output_text = processor.batch_decode(generate_ids[:, prompt_length:], skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] |
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return output_text |
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image = gr.components.Image(type="pil", label="Chart Image") |
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input_prompt = gr.components.Textbox(label="Input Prompt") |
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model_output = gr.components.Textbox(label="Model Output") |
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examples = [["chart_example_1.png", "Describe the trend of the mortality rates for children before age 5"], |
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["chart_example_2.png", "What is the share of respondants who prefer Facebook Messenger in the 30-59 age group?"]] |
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title = "Interactive Gradio Demo for ChartGemma model" |
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interface = gr.Interface(fn=predict, |
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inputs=[image, input_prompt], |
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outputs=model_output, |
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examples=examples, |
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title=title, |
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theme='gradio/soft') |
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interface.launch() |
