app.py

import gradio as gr
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.ui import Select
from selenium.webdriver.chrome.options import Options
from bs4 import BeautifulSoup
import pandas as pd
import time
import re
import os

def parse_credits(credits_str):
    """精準拆解國圖的作品職者字串"""
    c_str = re.sub(r'\s+', '', credits_str)
    singer_match = re.search(r'表演者:?([^作編混]*?)(?:作詞|作曲|編曲|$)', c_str)
    lyricist_match = re.search(r'作詞者:?([^作表編混]*?)(?:作曲|編曲|表演|$)', c_str)
    composer_match = re.search(r'作曲者:?([^作表編混]*?)(?:作詞|編曲|表演|$)', c_str)
    
    singer = singer_match.group(1).strip() if singer_match else ""
    lyricist = lyricist_match.group(1).strip() if lyricist_match else ""
    composer = composer_match.group(1).strip() if composer_match else ""
    
    return singer.replace(':', '').replace('：', ''), lyricist.replace(':', '').replace('：', ''), composer.replace(':', '').replace('：', '')

def process_single_song(driver, song_name, artist_name):
    """處理單一歌曲的爬取核心邏輯"""
    try:
        url = "https://isrc-web.ncl.edu.tw/C200/C200"
        driver.get(url)
        
        # 1. 切換分頁
        song_tab = WebDriverWait(driver, 10).until(EC.element_to_be_clickable((By.ID, "profile-tab")))
        song_tab.click()
        time.sleep(0.5)
        
        advanced_tab = WebDriverWait(driver, 10).until(EC.element_to_be_clickable((By.ID, "v-pills-settings-tab")))
        advanced_tab.click()
        time.sleep(1.2) 
        
        # 2. 輸入歌名
        field1_element = WebDriverWait(driver, 10).until(EC.visibility_of_element_located((By.ID, "songField1")))
        Select(field1_element).select_by_value("NAME")
        if song_name:
            keyword1_box = driver.find_element(By.ID, "songKeyword1")
            keyword1_box.clear()  
            keyword1_box.send_keys(str(song_name).strip())
            
        # 3. 輸入歌手
        field2_element = WebDriverWait(driver, 10).until(EC.visibility_of_element_located((By.ID, "songField2")))
        Select(field2_element).select_by_value("ACTION")
        if artist_name and not pd.isna(artist_name):
            keyword2_box = driver.find_element(By.ID, "songKeyword2")
            keyword2_box.clear()  
            keyword2_box.send_keys(str(artist_name).strip())
            
        # 4. 送出與等待渲染
        search_button = WebDriverWait(driver, 10).until(EC.element_to_be_clickable((By.XPATH, '//button[@data-action="C20004"]')))
        search_button.click()
        time.sleep(3.5) 
        
        # 5. 解析
        soup = BeautifulSoup(driver.page_source, 'html.parser')
        result_table = soup.find('table', id='dataTable2')
        
        data_list = []
        if result_table and result_table.find('tbody'):
            rows = result_table.find('tbody').find_all('tr')
            for row in rows:
                cols = row.find_all('td')
                if len(cols) >= 8: 
                    raw_credits = cols[4].text.strip()  
                    release_date = cols[7].text.strip()
                    extracted_singer, lyricist, composer = parse_credits(raw_credits)
                    data_list.append({
                        "主要歌手": extracted_singer, "作詞者": lyricist, "作曲者": composer, "發行日期": release_date
                    })
        
        if data_list:
            df = pd.DataFrame(data_list)
            df['datetime_parsed'] = pd.to_datetime(df['發行日期'], errors='coerce')
            df = df.sort_values(by='datetime_parsed', ascending=True).reset_index(drop=True)
            
            earliest_release_date = df.iloc[0]['發行日期'] if not df.empty else "未記載"
            
            final_lyricist, final_composer = "", ""
            target_artist = str(artist_name).strip() if (artist_name and not pd.isna(artist_name)) else ""
            
            df_artist_match = df[df['主要歌手'] == target_artist].reset_index(drop=True) if target_artist else pd.DataFrame()
            loop_df = df_artist_match if not df_artist_match.empty else df
            
            for _, row in loop_df.iterrows():
                if not final_lyricist and row['作詞者']: final_lyricist = row['作詞者']
                if not final_composer and row['作曲者']: final_composer = row['作曲者']
                if final_lyricist and final_composer: break
            
            return final_lyricist if final_lyricist else "未記載", final_composer if final_composer else "未記載", earliest_release_date
        else:
            return "查無資料", "查無資料", "查無資料"
            
    except Exception as e:
        print(f"處理歌曲 {song_name} 時發生問題: {str(e)}")
        return "錯誤", "錯誤", "錯誤"

def create_driver():
    """建立並初始化 Chrome 瀏覽器的捷徑函式"""
    chrome_options = Options()
    chrome_options.add_argument('--headless')
    chrome_options.add_argument('--no-sandbox')
    chrome_options.add_argument('--disable-dev-shm-usage')
    return webdriver.Chrome(options=chrome_options)

# 🎯 關鍵改動：加入 progress=gr.Progress() 參數來啟用進度條
def batch_process_file(file_obj, progress=gr.Progress()):
    if file_obj is None:
        return None, "請先上傳檔案！"
    
    file_path = file_obj.name
    
    if file_path.endswith('.csv'):
        input_df = pd.read_csv(file_path, header=None)
    else:
        input_df = pd.read_excel(file_path, header=None, engine='openpyxl')
        
    if input_df.shape[1] < 2:
        return None, "錯誤：表格格式不正確，至少需要有兩欄資料（左邊歌名、右邊歌手）！"
        
    song_col_idx = 0
    artist_col_idx = 1
    
    # 初始化第一個瀏覽器
    driver = create_driver()
    
    lyricists = []
    composers = []
    release_dates = []
    
    total_songs = len(input_df)
    
    # 🎯 核心進度條啟動：設定總步數
    progress(0, desc="🚀 正在準備啟動自動化系統...")
    
    for index, row in input_df.iterrows():
        song = row[song_col_idx]
        artist = row[artist_col_idx]
        
        # 🎯 即時更新網頁上的進度條狀態與文字提示
        progress((index / total_songs), desc=f"🎵 正在處理 ({index + 1}/{total_songs}): {song if not pd.isna(song) else ''}")
        
        # 🎯 記憶體防爆優化：每查 30 首歌，就關掉重開一次瀏覽器清空快取
        if index > 0 and index % 30 == 0:
            driver.quit()
            driver = create_driver()
        
        if index == 0 and any(keyword in str(song) for keyword in ["歌名", "歌曲", "曲目", "Title", "Name"]):
            lyricists.append("作詞者")
            composers.append("作曲者")
            release_dates.append("最早發行時間")
            continue
            
        if pd.isna(song):
            lyricists.append("歌名空白")
            composers.append("歌名空白")
            release_dates.append("歌名空白")
            continue
            
        lyr, comp, rel = process_single_song(driver, song, artist)
        
        lyricists.append(lyr)
        composers.append(comp)
        release_dates.append(rel)

        # 🎯 在這裡加入「溫柔限速機制」：每查完一首，強迫機器人休息 1.5 秒
        # 這樣查 100 首歌雖然會多花 150 秒，但可以確保 100% 穩當不被國圖封鎖！
        time.sleep(1.5)

    driver.quit()
    
    progress(0.95, desc="💾 正在將清洗後的數據打包成 Excel 檔案...")
    
    # 重新結合成全新表格
    output_df = pd.DataFrame()
    output_df['歌曲'] = input_df[song_col_idx]
    output_df['歌手'] = input_df[artist_col_idx]
    output_df['作詞者'] = lyricists
    output_df['作曲者'] = composers
    output_df['最早發行時間'] = release_dates
    
    if any(keyword in str(output_df.iloc[0, 0]) for keyword in ["歌名", "歌曲", "曲目", "Title"]):
        output_df.columns = ["歌曲", "歌手", "作詞者", "作曲者", "最早發行時間"]
        output_df = output_df.drop(output_df.index[0])
    
    output_file_path = "isrc_batch_results.xlsx"
    output_df.to_excel(output_file_path, index=False, engine='openpyxl')
    
    progress(1.0, desc="🎉 大功告成！")
    return output_file_path, f"🎉 成功處理完成！共處理 {len(output_df)} 筆歌曲資料。"

# Gradio 批量介面保持不變
with gr.Blocks(title="ISRC 歌曲資料批量洗滌器") as demo:
    gr.Markdown("# 🗂️ 臺灣 ISRC 歌曲詞曲資料【批量】查詢系統")
    gr.Markdown("請上傳一個 Excel 或 CSV 表格檔。規定：**第一欄為「歌名」，第二欄為「歌手」**（有無標題列皆相容）。")
    
    with gr.Row():
        file_input = gr.File(label="請上傳 Excel (.xlsx) 或 CSV (.csv) 檔案", file_types=[".xlsx", ".csv"])
    
    process_btn = gr.Button("開始批量轉換", variant="primary")
    
    with gr.Row():
        status_output = gr.Textbox(label="處理狀態", placeholder="尚未開始...")
        file_output = gr.File(label="下載轉換完成的 Excel 表格")
        
    process_btn.click(
        fn=batch_process_file, 
        inputs=file_input, 
        outputs=[file_output, status_output]
    )

demo.launch()