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app.py

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+import gradio as gr
+from fastcoref import FCoref
+
+# 1. 初始化指代消解模型
+print("正在載入 AI 模型，請稍候...")
+model = FCoref()
+
+def coref_chat(user_input):
+    # 執行模型推論
+    preds = model.predict(texts=[user_input])
+    clusters = preds[0].get_clusters()
+    
+    if not clusters:
+        return user_input, "模型未偵測到明確的指代群組。"
+    
+    # 整理輸出結果
+    result_text = "🎯 AI 偵測到的連連看群組：\n"
+    for i, cluster in enumerate(clusters):
+        result_text += f"【群組 {i+1}】(指的是同一個主體)：{cluster}\n"
+    
+    return user_input, result_text
+
+# 2. 建立 Gradio 網頁介面
+with gr.Blocks(title="指代消解 AI 助理") as demo:
+    gr.Markdown("# 🤖 指代消解 (Coreference Resolution) 聊天機器人")
+    gr.Markdown("輸入一段包含多個角色的對話，看 AI 如何幫你「連連看」！")
+    
+    with gr.Row():
+        with gr.Column():
+            txt_input = gr.Textbox(
+                label="請輸入測試文本", 
+                placeholder="例如: Professor James and Mary are in the lab. She smiled at him.",
+                lines=5
+            )
+            submit_btn = gr.Button("開始分析")
+            
+        with gr.Column():
+            txt_original = gr.Textbox(label="你輸入的文字", interactive=False)
+            txt_output = gr.Textbox(label="AI 分析結果", lines=8, interactive=False)
+
+    submit_btn.click(
+        fn=coref_chat, 
+        inputs=txt_input, 
+        outputs=[txt_original, txt_output]
+    )
+    
+    gr.Examples(
+        examples=[
+            ["Professor James and his assistant Mary are in the lab. The woman told the man that the experiment was a success. She smiled at him."],
+            ["Ben brought some pizza for his friends, and they all enjoyed it."]
+        ],
+        inputs=txt_input
+    )
+
+# 3. 啟動機器人
+if __name__ == "__main__":
+    # share=True 會產生一個臨時網址，你可以傳給老師或組員看
+    demo.launch()

requirements.txt

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+fastcoref
+gradio
+transformers==4.40.0