# -*- coding: utf-8 -*-

from typing import Container
from config.config import PASSWORD
import gradio as gr
import os
import shutil
import tempfile
from google import genai
from google.genai import types

from initializer import initialize_clients, initialize_password

# 初始化 Google Cloud Storage 服務和 GENAI 客戶端
GCS_SERVICE, GENAI_CLIENT = initialize_clients()
GCS_CLIENT = GCS_SERVICE.client

PASSWORD = initialize_password()

def toggle_visibility(toggle_value):
    return gr.update(visible=toggle_value)

def mock_question_answer(question, history):
    # 假資料模擬回答
    answers = {
        "文件的核心觀點是什麼？": "這份文件的核心觀點是關於人工智慧如何提升工作效率。",
        "有哪些關鍵詞或數據？": "關鍵詞包括：人工智慧、工作效率、數據分析。",
        "文件的摘要是什麼？": "這份文件討論了如何利用人工智慧工具，提升企業的運營效率和決策速度。"
    }
    response = answers.get(question, "抱歉，我無法回答這個問題。請嘗試其他問題！")
    history.append({"role": "user", "content": question})
    history.append({"role": "assistant", "content": response})
    return history, ""

def mock_summary():
    # 假資料模擬摘要
    return "這份文件主要討論人工智慧在工作效率提升方面的應用，並提供了實際案例來說明其價值。"

def add_to_file_list(file, file_list):
    if file:
        temp_dir = tempfile.gettempdir()
        temp_path = os.path.join(temp_dir, os.path.basename(file.name))
        shutil.copy(file.name, temp_path)  # 將文件存儲到臨時目錄
        file_list.append(temp_path)
    display_list = [os.path.basename(path) if os.path.basename(path) else path for path in file_list]
    return gr.update(choices=display_list), None

def add_youtube_to_list(youtube_link, file_list):
    if youtube_link:
        file_list.append(youtube_link)
    display_list = [os.path.basename(path) if os.path.basename(path) else path for path in file_list]
    return gr.update(choices=display_list), ""

def generate_transcript(youtube_link):
    print(f"\n開始生成 YouTube 逐字稿: {youtube_link}")
    try:
        print("初始化 Gemini 模型設定...")
        video = types.Part.from_uri(
            file_uri=youtube_link,
            mime_type="video/*",
        )

        model = "gemini-2.0-flash-exp"
        contents = [
            types.Content(
                role="user",
                parts=[
                    video,
                    types.Part.from_text("""請給我帶時間軸的逐字稿，請統一用 zhTW語言""")
                ]
            )
        ]
        generate_content_config = types.GenerateContentConfig(
            temperature=1,
            top_p=0.95,
            max_output_tokens=8192,
            response_modalities=["TEXT"],
            safety_settings=[
                types.SafetySetting(category="HARM_CATEGORY_HATE_SPEECH", threshold="OFF"),
                types.SafetySetting(category="HARM_CATEGORY_DANGEROUS_CONTENT", threshold="OFF"),
                types.SafetySetting(category="HARM_CATEGORY_SEXUALLY_EXPLICIT", threshold="OFF"),
                types.SafetySetting(category="HARM_CATEGORY_HARASSMENT", threshold="OFF")
            ],
        )

        print("開始串流生成逐字稿...")
        transcript_text = ""
        for chunk in GENAI_CLIENT.models.generate_content_stream(
            model=model,
            contents=contents,
            config=generate_content_config,
        ):
            # Extract only text content from candidates
            if hasattr(chunk, 'candidates') and chunk.candidates:
                for candidate in chunk.candidates:
                    if (hasattr(candidate, 'content') and 
                        hasattr(candidate.content, 'parts')):
                        for part in candidate.content.parts:
                            if hasattr(part, 'text') and part.text:
                                transcript_text += part.text
            print(".", end="", flush=True)
        
        print("\n逐字稿生成完成！")
        return transcript_text
    except Exception as e:
        print(f"\n生成逐字稿時發生錯誤: {str(e)}")
        raise

def generate_summary(transcript):
    """Generate a summary from the transcript using Gemini."""
    try:
        print("\n開始生成摘要...")
        model = "gemini-2.0-flash-exp"
        contents = [
            types.Content(
                role="user",
                parts=[
                    types.Part.from_text(
                        f"""請根據以下逐字稿生成重點摘要，以條列方式呈現主要觀點：

{transcript}

請以下列格式輸出：
# 主要觀點：
1. [重點1]
2. [重點2]
...

# 結論：
[整體結論]
"""
                    )
                ]
            )
        ]
        
        response = GENAI_CLIENT.models.generate_content(
            model=model,
            contents=contents,
        )
        
        print("摘要生成完成！")
        return response.text
    except Exception as e:
        print(f"\n生成摘要時發生錯誤: {str(e)}")
        raise

def process_all_files(file_list):
    print("\n=== 開始處理檔案 ===")
    print(f"待處理檔案數量: {len(file_list)}")
    
    result_text = ""
    transcript_text = ""
    
    for index, file in enumerate(file_list, 1):
        print(f"\n處理第 {index}/{len(file_list)} 個檔案: {file}")
        
        if "youtube.com" in file or "youtu.be" in file:
            print(f"檢測到 YouTube 連結，開始生成逐字稿...")
            try:
                transcript = generate_transcript(file)
                print("✓ YouTube 逐字稿生成成功")
                result_text += f"🟢 YouTube 影片處理完成: {file}\n"
                transcript_text += f"\n=== {file} 的逐字稿 ===\n{transcript}\n"
            except Exception as e:
                print(f"✗ YouTube 逐字稿生成失敗: {str(e)}")
                result_text += f"🔴 YouTube 影片處理失敗: {file}\n"
        else:
            print(f"處理一般檔案: {file}")
            try:
                # 這裡可以加入其他檔案的處理邏輯
                print("✓ 檔案處理成功")
                result_text += f"🟢 檔案處理完成: {file}\n"
            except Exception as e:
                print(f"✗ 檔案處理失敗: {str(e)}")
                result_text += f"🔴 檔案處理失敗: {file}\n"
    
    print("\n=== 檔案處理完成 ===")
    return result_text, transcript_text

def process_with_auth(password, file_list):
    """包含密碼驗證的處理函數"""
    if not password or password != PASSWORD:
        return "請輸入正確的密碼", "", gr.update(visible=False)
    
    result_text, transcript_text = process_all_files(file_list)
    return result_text, transcript_text

def on_summary_click(transcript):
    if not transcript:
        return "請先上傳文件或輸入 YouTube 連結並處理完成後再生成摘要。"
    
    summary = generate_summary(transcript)
    return summary

with gr.Blocks() as demo:

    with gr.Row():
        gr.Markdown("# AI Notes Assistant")
        password_input = gr.Textbox(label="password")

    with gr.Row():
        source_toggle = gr.Checkbox(label="顯示來源選單", value=True)
        chat_toggle = gr.Checkbox(label="顯示對話區域", value=True)
        feature_toggle = gr.Checkbox(label="顯示功能卡片", value=True)

    with gr.Row():
        with gr.Column(visible=True) as source_column:
            gr.Markdown("### 來源選單")
            
            file_list = gr.State([])
            
            with gr.Tab("YouTube 連結"):
                youtube_link = gr.Textbox(label="輸入 YouTube 連結")
                add_youtube_button = gr.Button("添加到來源列表")
                add_youtube_button.click(add_youtube_to_list, inputs=[youtube_link, file_list], outputs=[file_list, youtube_link])

            with gr.Tab("上傳檔案"):
                upload_file = gr.File(label="從電腦添加文件", file_types=[".txt", ".pdf", ".docx"])
                add_file_button = gr.Button("添加到來源列表")
                add_file_button.click(add_to_file_list, inputs=[upload_file, file_list], outputs=[file_list, upload_file])        
            
            file_display = gr.CheckboxGroup(label="已上傳的文件", interactive=True)

            process_files_button = gr.Button("處理檔案")
            rag_result = gr.Textbox(label="處理狀態", interactive=False)
            
            file_list.change(lambda x: gr.update(choices = [os.path.basename(path) if os.path.basename(path) else path for path in x]), inputs=file_list, outputs=file_display)

        with gr.Column(visible=True) as chat_column:
            gr.Markdown("### 對話區域")
            chatbot = gr.Chatbot(label="聊天記錄", type="messages")
            question = gr.Textbox(label="輸入問題，例如：文件的核心觀點是什麼？")
            ask_button = gr.Button("提問")

        with gr.Column(visible=True) as feature_column:
            gr.Markdown("### 功能卡片")
            with gr.Tab("摘要生成"):
                summary_button = gr.Button("生成摘要", visible=False)
                summary_output = gr.Markdown(
                    label="摘要",
                    show_label=True,
                    show_copy_button=True,
                    container=True
                )
            with gr.Tab("逐字稿"):
                transcript_display = gr.Textbox(
                    label="YouTube 逐字稿", 
                    interactive=False, 
                    lines=10,
                    show_copy_button=True,
                    placeholder="處理 YouTube 影片後，逐字稿將顯示在這裡..."
                )
            with gr.Tab("其他功能"):
                gr.Markdown("此處可以添加更多功能卡片")

    source_toggle.change(toggle_visibility, inputs=source_toggle, outputs=source_column)
    chat_toggle.change(toggle_visibility, inputs=chat_toggle, outputs=chat_column)
    feature_toggle.change(toggle_visibility, inputs=feature_toggle, outputs=feature_column)

    # 更新處理檔案按鈕的事件處理
    process_files_button.click(
        fn=process_with_auth,
        inputs=[password_input, file_list],
        outputs=[
            rag_result, 
            transcript_display
        ]
    ).then(
        fn=on_summary_click,
        inputs=[transcript_display],
        outputs=[summary_output]
    )

    history = gr.State([])
    ask_button.click(mock_question_answer, inputs=[question, history], outputs=[chatbot, question])
    summary_button.click(
        fn=on_summary_click, 
        inputs=[transcript_display], 
        outputs=[summary_output]
    )

    

demo.launch(share=True)