import os
import sys
import warnings
import logging

# 1. 抑制所有討厭的非同步與資源回收警告 (解決 ValueError: Invalid file descriptor: -1)
warnings.filterwarnings("ignore", category=RuntimeWarning)
warnings.filterwarnings("ignore", message="Exception ignored in")
logging.getLogger("asyncio").setLevel(logging.ERROR)

# 2. 強制下載 Spacy 英文模型
os.system(f"{sys.executable} -m spacy download en_core_web_sm")

# 3. 解決新舊版本 Hub 相容性的 Mock 補丁
try:
    import huggingface_hub
    if not hasattr(huggingface_hub, 'HfFolder'):
        class MockHfFolder:
            @staticmethod
            def get_token(): return os.getenv("HF_TOKEN")
            @staticmethod
            def save_token(token): pass
        huggingface_hub.HfFolder = MockHfFolder
except:
    pass

import gradio as gr
import spacy
from fastcoref import FCoref
from deep_translator import GoogleTranslator

# 4. 初始化 Spacy NLP 詞性解析器與指代模型
print("🚀 [System] 正在初始化 NLP 詞性解析器...")
nlp = spacy.load("en_core_web_sm")

print("🚀 [System] 正在初始化 F-Coref 核心大腦...")
try:
    model = FCoref(model_name_or_path='biu-nlp/f-coref', device='cpu')
    print("✅ [System] 所有模型加載成功！")
except:
    model = FCoref('biu-nlp/f-coref', device='cpu')
    print("✅ [System] 備用路徑加載成功！")

# 5. 核心運算邏輯
def coref_learning_pipeline(user_input):
    if not user_input.strip():
        return "等待輸入...", "等待輸入...", "等待輸入..."
    
    try:
        # A. 判斷語言並進行中翻英橋接
        has_chinese = any('\u4e00' <= char <= '\u9fff' for char in user_input)
        
        if has_chinese:
            working_text = GoogleTranslator(source='zh-CN', target='en').translate(user_input)
            mode_notice = "中文輸入模式（已啟動 AI 跨語言橋接）"
            
            # ✨ 關鍵修正：輸入中文時，完整文本翻譯欄位輸出「英文」
            translation_text = f"【英文對照】\n{working_text}"
        else:
            working_text = user_input
            mode_notice = "英文原語模式"
            
            # ✨ 關鍵修正：輸入英文時，完整文本翻譯欄位輸出「繁體中文」
            translated_zh = GoogleTranslator(source='en', target='zh-TW').translate(working_text)
            translation_text = f"【中文翻譯】\n{translated_zh}"

        # B. 執行 AI 指代消解運算
        preds = model.predict(texts=[working_text])
        clusters = preds[0].get_clusters()
        
        # C. 建立【AI 智慧單字本】
        vocab_output = ""
        doc = nlp(working_text)
        extracted_words = set()
        
        for token in doc:
            if token.pos_ == "NOUN" and len(token.text) > 2:
                extracted_words.add(token.lemma_.lower())
        
        if extracted_words:
            for word in sorted(extracted_words):
                try:
                    word_zh = GoogleTranslator(source='en', target='zh-TW').translate(word)
                    vocab_output += f"🔸 {word.capitalize()} ➔ {word_zh}\n"
                except:
                    pass
        else:
            vocab_output = "ℹ️ 未偵測到適合學習的核心英文單字。"

        # D. 生成「AI 語意共指報告」
        report_text = f"✨ 系統狀態：{mode_notice}\n"
        report_text += f"📝 英文運算空間: {working_text}\n"
        report_text += "-----------------------------------------\n"
        if not clusters:
            report_text += "🔍 分析結果：指代關係明確，無需額外消解。"
        else:
            report_text += "🎯【實體連連看鏈結 (Entity Chains)】:\n"
            for i, cluster in enumerate(clusters):
                cluster_str_en = ' ↔ '.join(cluster)
                try:
                    translated_items = [GoogleTranslator(source='en', target='zh-TW').translate(item) for item in cluster]
                    cluster_str_zh = ' ↔ '.join(translated_items)
                except:
                    cluster_str_zh = cluster_str_en
                report_text += f" 🔗 鏈結 {i+1} (中): {cluster_str_zh}\n"
                report_text += f"    └─ (英): {cluster_str_en}\n"

        # 💥 這裡非常重要！必須依序回傳給前端的三個框框
        return translation_text, vocab_output, report_text

    except Exception as e:
        return f"錯誤: {str(e)}", "無法整合單字", f"運行異常: {str(e)}"

# 6. 精美 UI 介面設計 (將 theme 移出 Blocks 構造函數)
with gr.Blocks() as demo:
    gr.Markdown("# 🤖 AI 跨語言智慧語意學習終端")
    gr.Markdown("### 🚀 專題亮點：結合核心指代消解 (Coreference Resolution) 與 NLP 智慧名詞提取技術")
    
    with gr.Row():
        with gr.Column(scale=1):
            txt_input = gr.Textbox(
                label="📥 請輸入中文或英文段落 (Input Text)", 
                placeholder="例如：Mary is a Farmer. Mary had a little lamb.", 
                lines=5
            )
            btn_submit = gr.Button("🔥 執行多維度 AI 語意解析", variant="primary")
            
        with gr.Column(scale=1):
            out_translation = gr.Textbox(label="📖 完整文本翻譯/對照 (Translation/Context)", lines=3)
            out_vocab = gr.Textbox(label="📚 AI 智慧單字本 (Vocabulary Booklet)", lines=5)
            out_report = gr.Textbox(label="🎯 AI 語意消解報告 (Coreference Report)", lines=5)
            
    btn_submit.click(
        fn=coref_learning_pipeline, 
        inputs=txt_input, 
        outputs=[out_translation, out_vocab, out_report]
    )

if __name__ == "__main__":
    # ✨ 關鍵修正：按照最新規範，將 theme 傳入 launch() 方法中
    demo.launch(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="indigo"))