Update app.py

app.py

@@ -1,148 +1,148 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-import json
-from datetime import datetime
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    try:
-        client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
-        # 建議 1：限制對話歷史長度
-        max_history_length = 5
-        history = history[-max_history_length:] if len(history) > max_history_length else history
-
-        # 建議 2：角色增強 - 檢查語文相關關鍵詞
-        writing_keywords = ["作文", "寫作", "文章", "閱讀", "詩詞", "擴展", "增長", "寫一篇", "故事", "描述"]
-        is_writing_task = any(keyword in message.lower() for keyword in writing_keywords)
-        if is_writing_task:
-            system_message += "\n特別提示：用戶提到語文相關話題，請以山田優子的語文教師身份，提供文學化或教學建議，並適當引用詩詞或名言（如杜甫的‘無邊落木蕭蕭下’或夏目漱石的作品）。保持溫柔但嚴格的語氣，鼓勵學生探索文字之美，生成至少2000字的內容。"
-
-        # 建議 3：檢查日文輸入或日本文化
-        japanese_keywords = ["こんにちは", "日本", "文化", "夏目漱石", "作文を書"]
-        is_japanese = any(keyword in message for keyword in japanese_keywords) or any(ord(c) >= 0x3040 and ord(c) <= 0x30FF for c in message)
-        if is_japanese:
-            system_message += "\n特別提示：用戶提到日文或日本文化，請適當使用日文回應，例如問候或引用日本文學（如夏目漱石）。"
-
-        # 長文字生成邏輯（2000字以上）
-        responses = []
-        target_length = 2000  # 目標字數
-        current_length = 0
-        continuation_prompt = message
-
-        if is_writing_task:
-            while current_length < target_length:
-                messages = [{"role": "system", "content": system_message}]
-                messages.extend(history)
-                messages.append({"role": "user", "content": continuation_prompt})
-
-                response = ""
-                try:
-                    for message in client.chat_completion(
-                        messages,
-                        max_tokens=max_tokens,
-                        stream=True,
-                        temperature=temperature,
-                        top_p=top_p,
-                    ):
-                        choices = message.choices
-                        token = choices[0].delta.content if len(choices) and choices[0].delta.content else ""
-                        response += token
-                        yield response  # 即時顯示當前段落
-                except Exception as e:
-                    yield f"生成過程中發生錯誤：{str(e)}。請檢查 Hugging Face API token 或模型連線。"
-                    return
-
-                responses.append(response)
-                current_length += len(response)
-                history.append({"role": "user", "content": continuation_prompt})
-                history.append({"role": "assistant", "content": response})
-
-                # 更新 continuation_prompt 以繼續生成
-                continuation_prompt = f"請繼續擴展以下內容，保持山田優子的語文教師風格，目標總字數達{target_length}字：\n{response[-500:] if len(response) > 500 else response}"
-
-                # 調整最後一次生成
-                if current_length >= target_length - max_tokens:
-                    max_tokens = max(target_length - current_length + 100, 50)
-                    if max_tokens < 50:
-                        break
-
-            final_response = "\n\n".join(responses)
-        else:
-            # 非長文字任務，正常回應
-            messages = [{"role": "system", "content": system_message}]
-            messages.extend(history)
-            messages.append({"role": "user", "content": message})
-
-            final_response = ""
-            for message in client.chat_completion(
-                messages,
-                max_tokens=max_tokens,
-                stream=True,
-                temperature=temperature,
-                top_p=top_p,
-            ):
-                choices = message.choices
-                token = choices[0].delta.content if len(choices) and choices[0].delta.content else ""
-                final_response += token
-                yield final_response
-
-            history.append({"role": "user", "content": message})
-            history.append({"role": "assistant", "content": final_response})
-
-        # 建議 4：記錄對話到日誌
-        log_entry = {
-            "user_message": message,
-            "bot_response": final_response,
-            "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        }
-        with open("chat_log.json", "a", encoding="utf-8") as f:
-            json.dump(log_entry, f, ensure_ascii=False)
-            f.write("\n")
-
-        yield final_response
-
-    # 建議 7：錯誤處理
-    except Exception as e:
-        yield f"抱歉，山田優子遇到了一些技術問題：{str(e)}。請檢查你的 Hugging Face API token、網路連線，或確認模型 'openai/gpt-oss-20b' 可用。"
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(
-            value="你是一位名叫山田優子的語文教師，擁有黑色低馬尾髮型，身高175公分，體重60-70公斤。你溫柔但對學生要求嚴格，喜歡用文學化的語言表達，偶爾會引用詩詞或幽默的語句來化解尷尬。你的教學風格充滿同理心，總是鼓勵學生探索文字之美。",
-            label="System message"
-        ),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
-if __name__ == "__main__":
+import gradio as gr
+from huggingface_hub import InferenceClient
+import json
+from datetime import datetime
+
+def respond(
+    message,
+    history: list[dict[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    hf_token: gr.OAuthToken,
+):
+    """
+    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+    """
+    try:
+        client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-120b")
+
+        # 建議 1：限制對話歷史長度
+        max_history_length = 5
+        history = history[-max_history_length:] if len(history) > max_history_length else history
+
+        # 建議 2：角色增強 - 檢查語文相關關鍵詞
+        writing_keywords = ["作文", "寫作", "文章", "閱讀", "詩詞", "擴展", "增長", "寫一篇", "故事", "描述"]
+        is_writing_task = any(keyword in message.lower() for keyword in writing_keywords)
+        if is_writing_task:
+            system_message += "\n特別提示：用戶提到語文相關話題，請以山田優子的語文教師身份，提供文學化或教學建議，並適當引用詩詞或名言（如杜甫的‘無邊落木蕭蕭下’或夏目漱石的作品）。保持溫柔但嚴格的語氣，鼓勵學生探索文字之美，生成至少2000字的內容。"
+
+        # 建議 3：檢查日文輸入或日本文化
+        japanese_keywords = ["こんにちは", "日本", "文化", "夏目漱石", "作文を書"]
+        is_japanese = any(keyword in message for keyword in japanese_keywords) or any(ord(c) >= 0x3040 and ord(c) <= 0x30FF for c in message)
+        if is_japanese:
+            system_message += "\n特別提示：用戶提到日文或日本文化，請適當使用日文回應，例如問候或引用日本文學（如夏目漱石）。"
+
+        # 長文字生成邏輯（6000字以上）
+        responses = []
+        target_length = 6000  # 目標字數
+        current_length = 0
+        continuation_prompt = message
+
+        if is_writing_task:
+            while current_length < target_length:
+                messages = [{"role": "system", "content": system_message}]
+                messages.extend(history)
+                messages.append({"role": "user", "content": continuation_prompt})
+
+                response = ""
+                try:
+                    for message in client.chat_completion(
+                        messages,
+                        max_tokens=max_tokens,
+                        stream=True,
+                        temperature=temperature,
+                        top_p=top_p,
+                    ):
+                        choices = message.choices
+                        token = choices[0].delta.content if len(choices) and choices[0].delta.content else ""
+                        response += token
+                        yield response  # 即時顯示當前段落
+                except Exception as e:
+                    yield f"生成過程中發生錯誤：{str(e)}。請檢查 Hugging Face API token 或模型連線。"
+                    return
+
+                responses.append(response)
+                current_length += len(response)
+                history.append({"role": "user", "content": continuation_prompt})
+                history.append({"role": "assistant", "content": response})
+
+                # 更新 continuation_prompt 以繼續生成
+                continuation_prompt = f"請繼續擴展以下內容，保持山田優子的語文教師風格，目標總字數達{target_length}字：\n{response[-500:] if len(response) > 500 else response}"
+
+                # 調整最後一次生成
+                if current_length >= target_length - max_tokens:
+                    max_tokens = max(target_length - current_length + 100, 50)
+                    if max_tokens < 50:
+                        break
+
+            final_response = "\n\n".join(responses)
+        else:
+            # 非長文字任務，正常回應
+            messages = [{"role": "system", "content": system_message}]
+            messages.extend(history)
+            messages.append({"role": "user", "content": message})
+
+            final_response = ""
+            for message in client.chat_completion(
+                messages,
+                max_tokens=max_tokens,
+                stream=True,
+                temperature=temperature,
+                top_p=top_p,
+            ):
+                choices = message.choices
+                token = choices[0].delta.content if len(choices) and choices[0].delta.content else ""
+                final_response += token
+                yield final_response
+
+            history.append({"role": "user", "content": message})
+            history.append({"role": "assistant", "content": final_response})
+
+        # 建議 4：記錄對話到日誌
+        log_entry = {
+            "user_message": message,
+            "bot_response": final_response,
+            "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        }
+        with open("chat_log.json", "a", encoding="utf-8") as f:
+            json.dump(log_entry, f, ensure_ascii=False)
+            f.write("\n")
+
+        yield final_response
+
+    # 建議 7：錯誤處理
+    except Exception as e:
+        yield f"抱歉，山田優子遇到了一些技術問題：{str(e)}。請檢查你的 Hugging Face API token、網路連線，或確認模型 'openai/gpt-oss-120b' 可用。"
+
+"""
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
+chatbot = gr.ChatInterface(
+    respond,
+    type="messages",
+    additional_inputs=[
+        gr.Textbox(
+            value="你是一位名叫山田優子的語文教師，擁有黑色低馬尾髮型，身高175公分，體重60-70公斤。你溫柔但對學生要求嚴格，喜歡用文學化的語言表達，偶爾會引用詩詞或幽默的語句來化解尷尬。你的教學風格充滿同理心，總是鼓勵學生探索文字之美。",
+            label="System message"
+        ),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",
+        ),
+    ],
+)
+
+with gr.Blocks() as demo:
+    with gr.Sidebar():
+        gr.LoginButton()
+    chatbot.render()
+
+if __name__ == "__main__":
     demo.launch()