import gradio as gr import tempfile import os import json import shutil from pathlib import Path import base64 import logging from main import generate_paper_poster logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) def process_paper_to_poster( pdf_file, model_choice, figure_service_url, openai_api_key, openai_base_url ): """ 处理上传的PDF文件并生成海报 - 支持实时状态更新 """ if pdf_file is None: yield None, None, None, "❌ Please upload a PDF file first!" return if not openai_api_key.strip(): yield None, None, None, "❌ Please enter your OpenAI API Key!" return if not figure_service_url.strip(): yield None, None, None, "❌ Please enter the figure detection service URL!" return try: # 初始状态 yield None, None, None, "🚀 Starting poster generation process..." # 配置OpenAI设置 yield None, None, None, "⚙️ Configuring OpenAI API settings..." os.environ['OPENAI_API_KEY'] = openai_api_key.strip() if openai_base_url.strip(): os.environ['OPENAI_BASE_URL'] = openai_base_url.strip() # 创建临时目录 yield None, None, None, "📁 Creating temporary workspace..." temp_dir = tempfile.mkdtemp() # 保存上传的PDF文件 yield None, None, None, "📄 Processing uploaded PDF file..." pdf_path = os.path.join(temp_dir, "paper.pdf") shutil.copy(pdf_file.name, pdf_path) # 开始调用生成函数 yield None, None, None, "🔍 Extracting content from PDF and detecting figures..." # 调用原始生成函数 poster, html = generate_paper_poster( url=figure_service_url, pdf=pdf_path, vendor="openai", model=model_choice, text_prompt="", # 使用默认提示 figures_prompt="", # 使用默认提示 output="" # 不再使用 ) # 检查返回值是否为None if poster is None and html is None: yield None, None, None, "❌ Failed to generate poster! The paper processing returned no results. Please check:\n- PDF file format and content\n- Figure detection service availability\n- API key validity\n- Model configuration\n- Network connectivity" return # 图片处理完毕，开始生成JSON yield None, None, None, "🖼️ Image processing completed! Generating JSON structure..." # 将海报转换为JSON以便预览 json_content = json.dumps(poster.model_dump(), indent=2, ensure_ascii=False) # JSON生成完毕，开始生成HTML yield None, None, None, "📋 JSON file generated successfully! Creating HTML poster..." # 创建持久化的临时文件用于下载 json_file = tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False, encoding='utf-8') html_file = tempfile.NamedTemporaryFile(mode='w', suffix='.html', delete=False, encoding='utf-8') # 写入内容到文件 json_file.write(json_content) json_file.close() html_file.write(html) html_file.close() # 清理我们创建的临时目录 yield None, None, None, "🧹 Cleaning up temporary files..." shutil.rmtree(temp_dir) # 最终完成 yield ( [json_file.name, html_file.name], json_content, html, "✅ Poster generated successfully! 🎉\n📥 Files are ready for download\n🎨 HTML preview is displayed below\n💡 Download the HTML file for best viewing experience" ) except Exception as e: error_msg = f"❌ Error occurred during processing: {str(e)}" yield None, None, None, error_msg # 创建Gradio界面 def create_interface(): # JavaScript代码强制启用Light模式 js_func = """ function refresh() { const url = new URL(window.location); if (url.searchParams.get('__theme') !== 'light') { url.searchParams.set('__theme', 'light'); window.location.href = url.href; } } """ with gr.Blocks( title="P2P: Paper-to-Poster Generator", theme=gr.themes.Default(), # 使用Light主题 js=js_func, # 添加JavaScript强制Light模式 css=""" .title { text-align: center; margin-bottom: 1rem; } .preview-container { min-height: 1000px; max-height: 1500px; overflow-y: auto; border: 1px solid #e0e0e0; border-radius: 8px; padding: 15px; background-color: #fafafa; } .preview-container iframe { width: 100% !important; min-height: 1000px !important; } .config-section { margin-bottom: 2rem; } .status-updating { color: #2563eb; font-weight: 500; } """ ) as demo: gr.HTML("""

🎓 P2P: Paper-to-Poster Generator

Automatically convert academic papers into professional conference posters ✨

📄 View Research Paper

""") # 配置区域 - 水平布局 with gr.Row(elem_classes=["config-section"]): with gr.Column(scale=1): gr.Markdown("### 📥 Input Configuration") # 文件上传 pdf_input = gr.File( label="Upload PDF Paper File", file_types=[".pdf"], file_count="single" ) # OpenAI API配置 gr.Markdown("#### 🔑 OpenAI API Configuration") openai_api_key = gr.Textbox( label="OpenAI API Key", placeholder="sk-...", type="password", info="Enter your OpenAI API key" ) openai_base_url = gr.Textbox( label="OpenAI Base URL (Optional)", placeholder="https://api.openai.com/v1", value="https://api.openai.com/v1", info="Modify this URL if using proxy or other OpenAI-compatible services" ) with gr.Column(scale=1): gr.Markdown("### ⚙️ Model Configuration") # 模型选择 model_choice = gr.Textbox( label="AI Model Name", value="gpt-4o-mini", placeholder="e.g., gpt-4o-mini, gpt-4o, gpt-3.5-turbo, claude-3-sonnet", info="Enter the AI model name you want to use" ) # 图片检测服务URL figure_url = gr.Textbox( label="Figure Detection Service URL", placeholder="Enter the URL of figure detection service", info="Used to extract images and tables from PDF" ) # 生成按钮 generate_btn = gr.Button( "🚀 Generate Poster", variant="primary", size="lg" ) with gr.Column(scale=1): gr.Markdown("### 📤 Results & Downloads") # 状态消息 status_msg = gr.Textbox( label="Status Information", interactive=False, lines=4, show_copy_button=True ) # 文件下载 output_files = gr.File( label="📥 Download Generated Files (JSON & HTML)", file_count="multiple", interactive=False, show_label=True ) # JSON预览 - 压缩到侧边栏 with gr.Accordion("📋 JSON Structure", open=False): json_preview = gr.Code( label="", language="json", lines=10, show_label=False ) # 预览区域 - 跨栏全宽显示 gr.Markdown("### 🎨 HTML Poster Preview") gr.Markdown("**💡 Recommended: Download the HTML file from above and open it in your browser for optimal viewing experience**") # HTML预览 - 全宽显示 html_preview = gr.HTML( label="", show_label=False, elem_classes=["preview-container"] ) # 使用说明 gr.Markdown(""" ### 📖 Usage Instructions 1. **Upload PDF File**: Select the academic paper PDF you want to convert 2. **Configure OpenAI API**: Enter your API Key and Base URL (if needed) 3. **Select Model**: Enter model name manually, such as gpt-4o-mini, gpt-4o, claude-3-sonnet, etc. 4. **Set Figure Service**: Enter the URL of the figure detection service 5. **Generate Poster**: Click the generate button and wait for processing 6. **Download Results**: Download the generated JSON and HTML files from the download section 7. **Full Preview**: Download the HTML file and open it in your browser for the best viewing experience ⚠️ **Important Notes**: - Generated Poster is recommended to be viewed in fullscreen mode or download the HTML file to view in browser - Requires a valid OpenAI API key with sufficient balance - Figure detection service URL is required for extracting images from PDFs - Processing time depends on paper length and complexity (usually 3-6 minutes) - Ensure the model name is correct and supported by your API - Download the HTML file and open it in your browser for the best viewing experience 💡 **Tips**: - Recommended to use gpt-4o-mini model for cost-effective testing - Recommended to use Claude model for better performance - Modify Base URL if using domestic proxy services - Supports any OpenAI-compatible model names - Can use Claude, Gemini and other models (requires corresponding API configuration) - The HTML preview below shows how your poster will look with maximum width for better viewing - Download the HTML file from the "Download Generated Files" section for standalone viewing """) # 绑定事件 generate_btn.click( fn=process_paper_to_poster, inputs=[ pdf_input, model_choice, figure_url, openai_api_key, openai_base_url ], outputs=[ output_files, json_preview, html_preview, status_msg ] ) return demo if __name__ == "__main__": demo = create_interface() demo.launch( server_name="0.0.0.0", server_port=7860, share=False )