app.py

import gradio as gr
import os
from llm_utils import parse_srt_file, analyze_meeting_transcript, generate_summary

# Gradio State variables to hold intermediate values
# transcript_state will store the raw text from the SRT file
# auto_template_state will store the template suggested by the LLM after analysis

def handle_file_upload(srt_file_obj):
    """
    Handles the SRT file upload.
    Parses the file, analyzes it for industry, purpose, and template suggestion.
    Returns values to update the UI and state variables.
    """
    if srt_file_obj is None:
        # Clear all fields if no file is present (e.g., file removed)
        return "", "", "", "", "", "", "请上传SRT文件后再试 (Please upload an SRT file)."

    srt_file_path = srt_file_obj.name
    transcript_text = parse_srt_file(srt_file_path)

    if transcript_text is None:
        return "", "", "", "SRT文件解析失败 (Error parsing SRT file).", "", "", "SRT文件解析失败，请检查文件格式 (SRT parsing failed, please check file format)."

    try:
        industry, meeting_purpose, auto_suggested_template_val = analyze_meeting_transcript(transcript_text)
        return (
            industry,
            meeting_purpose,
            auto_suggested_template_val, # This will populate the editable Textbox
            transcript_text,
            transcript_text, # For transcript_state
            auto_suggested_template_val, # For auto_template_state (original LLM suggestion)
            '上传SRT文件成功，点击"开始总结"生成总结 (SRT file uploaded successfully. Click \'Start Summarization\' to generate summary.)'
        )
    except ValueError as ve:  # API key issue
        error_msg = f"API配置错误 (API Configuration Error): {str(ve)}"
        return "", "", "", transcript_text, transcript_text, "", error_msg # Industry, Purpose, Template, Preview, TranscriptState, AutoTemplateState, Summary
    except Exception as e:
        error_msg = f"会议分析出错 (Error during meeting analysis): {e}"
        print(error_msg)
        return "", "", "", transcript_text, transcript_text, "", error_msg # Consistent error return for 7 outputs


def handle_summarization(current_transcript_text, edited_template_text, reference_summary_text, user_notes_text):
    """
    Handles the summarization process after the 'Start Summarization' button is clicked.
    Uses the transcript and either the (potentially edited) auto-suggested template 
    or a reference summary, along with user's manual notes.
    The edited_template_text comes directly from the output_suggested_template textbox.
    """
    if not current_transcript_text:
        return "无转写内容可供总结，请先上传SRT文件 (No transcript to summarize. Please upload an SRT file first)."

    summary_output = ""
    try:
        if reference_summary_text and reference_summary_text.strip():
            summary_output = generate_summary(current_transcript_text, reference_summary_text=reference_summary_text, user_notes=user_notes_text)
        elif edited_template_text and edited_template_text.strip(): # Prioritize edited template
            summary_output = generate_summary(current_transcript_text, auto_template_structure=edited_template_text, user_notes=user_notes_text)
        else:
            return "无可用模板，请输入或确保自动推荐的模板已生成且未被清空 (No template available. Please enter one or ensure the auto-suggested template was generated and not cleared)."
    except ValueError as ve: # API key issue
         return f"API配置错误 (API Configuration Error): {str(ve)}"
    except Exception as e:
        error_message = f"生成总结出错 (Error during summary generation): {e}"
        print(error_message)
        return error_message
        
    return summary_output

# Gradio Interface Definition
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("## 会议总结程序 V1.2") # Version updated
    gr.Markdown(
        "上传会议的SRT格式录音转写文件，系统将：\n" +
        "1. **自动分析** 会议内容，给出所属行业和推荐的总结模板。\n" +
        "2. （可选）您可以输入一个参考总结文本。\n" +
        "3. （新增）您可以输入手动记录的关键笔记，系统将重点关注和包含这些内容。\n" +
        "4. 点击 **开始总结** 按钮后，系统将根据推荐模板或您提供的参考总结格式，结合您的手动笔记，生成最终的会议纪要。"
    )

    # Hidden state variables
    transcript_state = gr.State(value="")
    auto_template_state = gr.State(value="") # Stores the original LLM template suggestion

    with gr.Row():
        with gr.Column(scale=1):
            srt_file_input = gr.File(label="上传SRT文件", file_types=[".srt"])
            reference_summary_input = gr.Textbox(
                label="参考总结 (可选，优先使用此模板)", 
                placeholder="如果希望按特定格式总结，请在此处粘贴参考总结文本...", 
                lines=8
            )
            user_notes_input = gr.Textbox(
                label="手动记录的关键笔记 (可选)", 
                placeholder="请在此处输入您在会议过程中记录的关键词、关键数字、重要观点等...", 
                lines=6
            )
            submit_button = gr.Button("开始总结", variant="primary")
            
        with gr.Column(scale=2):
            gr.Markdown("### 分析结果 (SRT上传后自动更新)")
            output_industry = gr.Textbox(label="推测行业", interactive=False)
            output_meeting_purpose = gr.Textbox(label="会议目的", interactive=False)
            # Changed to Textbox, made interactive, and given more lines for editing
            output_suggested_template = gr.Textbox(label="推荐总结模板 (可编辑)", interactive=True, lines=10) 
            
            gr.Markdown("### 会议总结 (点击按钮后生成)")
            output_summary = gr.Markdown(label="会议总结") # Output summary remains Markdown for rendering
    
    gr.Markdown("### 原始SRT转写内容 (上传SRT后自动更新)")
    output_transcript_preview = gr.Textbox(label="SRT文本预览", lines=8, interactive=False)
    
    # Event for file upload: triggers analysis
    srt_file_input.upload(
        fn=handle_file_upload,
        inputs=[srt_file_input],
        outputs=[
            output_industry, 
            output_meeting_purpose,
            output_suggested_template, # Now populates the Textbox
            output_transcript_preview,
            transcript_state, 
            auto_template_state,
            output_summary 
        ]
    )
    
    # Event for button click: triggers summarization
    submit_button.click(
        fn=handle_summarization,
        inputs=[
            transcript_state, 
            output_suggested_template, # Use the current content of the textbox
            reference_summary_input,
            user_notes_input
        ],
        outputs=[output_summary]
    )
    
    # Define a wrapper for the examples fn to match expected output structure
    def example_fn_wrapper(srt_file_obj, _reference_summary_text, _user_notes_text):
        analysis_results = handle_file_upload(srt_file_obj)
        if isinstance(analysis_results, tuple) and len(analysis_results) == 7:
            return analysis_results[:-1] + ("Example loaded. Edit template if needed, then click '开始总结'.",)
        else:
            return "Error", "Error", "Error loading example", "Please try manually.", "", "", "Error processing example."

    gr.Examples(
        examples=[
            [os.path.join(os.path.dirname(__file__), "example_meeting.srt"), "", ""],
            [
                os.path.join(os.path.dirname(__file__), "example_meeting.srt"), 
                "会议纪要\n\n日期：YYYY-MM-DD\n主题：示例\n\n讨论点：\n- 点一\n- 点二\n\n行动项：\n- 某人：某事 (截止日期)",
                "重要数字：预算 50万元\n关键决策：选择方案A\n重点关注：Q2季度目标"
            ]
        ],
        inputs=[srt_file_input, reference_summary_input, user_notes_input],
        outputs=[output_industry, output_meeting_purpose, output_suggested_template, output_transcript_preview, transcript_state, auto_template_state, output_summary],
        fn=example_fn_wrapper, 
        cache_examples=False 
    )

if __name__ == "__main__":
    if not os.getenv("OPENROUTER_API_KEY"):
        print("警告: OPENROUTER_API_KEY 环境变量未设置。")
        print("请在您的环境中设置它，或在项目根目录创建一个 .env 文件，内容如下:")
        print('OPENROUTER_API_KEY="sk-or-v1-your-key-here"')
        print('OPENROUTER_MODEL="your-chosen-model" (例如 mistralai/mistral-7b-instruct)')
        print("程序可能无法正常与大模型交互。")
    demo.launch()