initial app.py

app.py

@@ -0,0 +1,56 @@
+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)
+