Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| from transformers import AutoProcessor, PaliGemmaForConditionalGeneration | |
| import requests | |
| from PIL import Image | |
| import torch | |
| # 下载示例图片 | |
| torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/test/png/74801584018932.png', 'chart_example_1.png') | |
| 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') | |
| # 加载模型和处理器 | |
| model = PaliGemmaForConditionalGeneration.from_pretrained("ahmed-masry/chartgemma") | |
| processor = AutoProcessor.from_pretrained("ahmed-masry/chartgemma") | |
| def predict(image, input_text): | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| image = image.convert("RGB") | |
| inputs = processor(text=input_text, images=image, return_tensors="pt") | |
| inputs = {k: v.to(device) for k, v in inputs.items()} | |
| prompt_length = inputs['input_ids'].shape[1] | |
| # 生成文本 | |
| generate_ids = model.generate(**inputs, max_new_tokens=512) | |
| output_text = processor.batch_decode(generate_ids[:, prompt_length:], skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] | |
| return output_text | |
| examples = [ | |
| ["chart_example_1.png", "Describe the trend of the mortality rates for children before age 5"], | |
| ["chart_example_2.png", "What is the share of respondents who prefer Facebook Messenger in the 30-59 age group?"] | |
| ] | |
| title = "ChartGemma 模型的互动式 Gradio 演示" | |
| with gr.Blocks(css="theme.css") as demo: | |
| gr.Markdown(f"# {title}") | |
| with gr.Row(): | |
| with gr.Column(): | |
| image = gr.Image(type="pil", label="图表图像") | |
| input_prompt = gr.Textbox(label="输入") | |
| with gr.Column(): | |
| model_output = gr.Textbox(label="输出") | |
| gr.Examples(examples=examples, inputs=[image, input_prompt]) | |
| submit_button = gr.Button("运行") | |
| submit_button.click(predict, inputs=[image, input_prompt], outputs=model_output) | |
| demo.launch(server_name="0.0.0.0", server_port=7860, share=True) | |