Update app.py

app.py

@@ -1,80 +1,64 @@
 import os
 import sys
-
-# 強制下載模型與補強環境
-os.system(f"{sys.executable} -m pip install spacy")
-os.system(f"{sys.executable} -m spacy download en_core_web_sm")
-
 import gradio as gr
-from fastcoref import FCoref
 from deep_translator import GoogleTranslator
 
-# 初始化模型
-model = FCoref()
+# 補強環境套件
+os.system(f"{sys.executable} -m pip install torch transformers")
+
+from fastcoref import LingmessCoref
+
+# 1. 預先下載並加載 Lingmess 模型 (這在雲端更穩定)
+print("正在初始化深度語意模型...")
+model = LingmessCoref(device='cpu') 
 
 def coref_chat(user_input):
     if not user_input.strip():
         return "請輸入內容", "等待輸入..."
 
-    # 1. 執行翻譯
     try:
+        # 2. 翻譯橋接
         translated_text = GoogleTranslator(source='auto', target='en').translate(user_input)
-    except Exception as e:
-        return user_input, f"翻譯失敗: {str(e)}"
-
-    # 2. 執行指代消解
-    preds = model.predict(texts=[translated_text])
-    clusters = preds[0].get_clusters()
-    
-    # 3. 處理「沒抓到群組」的情況
-    if not clusters:
-        error_msg = (
-            f"📝 翻譯後的英文：{translated_text}\n\n"
-            "❌ 狀態：AI 未能建立實體連結。\n"
-            "💡 建議：嘗試輸入更長的段落，並明確提到『人名』或『職業』。例如：『教授和助理在聊天，他很開心，而她很忙。』"
-        )
-        return user_input, error_msg
-
-    # 4. 成功抓到群組，整理輸出 (方案三的對照展示)
-    result_text = f"📝 英文語意空間 (English space):\n{translated_text}\n\n"
-    result_text += "🎯 【實體追蹤結果 Entity Tracking】:\n"
-    
-    for i, cluster in enumerate(clusters):
-        result_text += f" ● 群組 {i+1}: {' ↔ '.join(cluster)}\n"
+        
+        # 3. 執行預測
+        preds = model.predict(texts=[translated_text])
+        clusters = preds[0].get_clusters()
+        
+        # 4. 如果還是沒抓到，列出除錯資訊
+        if not clusters:
+            return user_input, f"📝 翻譯文本：{translated_text}\n\n❌ 狀態：模型已運作但未偵測到明確指代。\n💡 提示：請嘗試輸入更明確的關係，例如：'The doctor met the nurse. She gave him a book.'"
 
-    # 自動偵測是否有潛在性別偏見 (方案三核心邏輯)
-    bias_check = ""
-    if "she" in translated_text.lower() or "her" in translated_text.lower():
-        bias_check = "\n✨ [技術亮點] 偵測到女性代名詞，系統已成功鎖定實體對象，避免翻譯偏見。"
+        # 5. 格式化結果
+        result_text = f"📝 英文語意路徑：{translated_text}\n\n"
+        result_text += "🎯 【指代消解群組 Coref Clusters】:\n"
+        for i, cluster in enumerate(clusters):
+            result_text += f" ● 群組 {i+1}: {' ↔ '.join(cluster)}\n"
+        
+        # 方案三應用：偵測性別歧義修正
+        if "she" in translated_text.lower() and "he" in translated_text.lower():
+            result_text += "\n✨ [技術應用] 已成功解析跨性別主體關聯，可應用於消除翻譯軟體之偏見。"
+            
+        return user_input, result_text
 
-    return user_input, result_text + bias_check
+    except Exception as e:
+        return user_input, f"系統錯誤: {str(e)}"
 
-# 建立介面
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🤖 Janice's AI Coreference Assistant")
-    gr.Markdown("### 專題應用：跨語言實體追蹤與翻譯去偏見測試層")
+# Gradio 介面設定
+with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
+    gr.Markdown("# 🤖 Janice's AI Coreference Engine")
+    gr.Markdown("### 應用：跨語言實體追蹤與翻譯去偏見測試")
     
     with gr.Row():
-        with gr.Column():
-            txt_input = gr.Textbox(
-                label="請輸入中文或英文段落", 
-                placeholder="例如：醫生走進病房，她拿起了聽診器，他在旁邊紀錄。", 
-                lines=5
-            )
-            submit_btn = gr.Button("執行深度語意分析", variant="primary")
-        
-        with gr.Column():
-            txt_output = gr.Textbox(label="系統分析報告", lines=10)
-            
+        txt_input = gr.Textbox(label="輸入待分析文本 (中/英)", lines=4)
+        txt_output = gr.Textbox(label="深度語意分析報告", lines=10)
+    
+    btn = gr.Button("執行分析", variant="primary")
+    btn.click(fn=coref_chat, inputs=txt_input, outputs=[txt_input, txt_output])
+
     gr.Examples(
-        examples=[
-            ["教授走進實驗室，他在看燒瓶。助教過來了，她對他笑了笑。"],
-            ["The surgeon performed the operation. She was very careful because her patient was weak."]
-        ],
+        examples=["The doctor asked the nurse to help her. He was busy.", "教授走進教室，學生們對他打招呼。"],
         inputs=txt_input
     )
-    
-    submit_btn.click(fn=coref_chat, inputs=txt_input, outputs=[txt_input, txt_output])
 
 if __name__ == "__main__":
     demo.launch()