Update app.py

app.py

@@ -1,70 +1,160 @@
+import os
+from typing import List, Dict, Tuple
+
 import gradio as gr
-from huggingface_hub import InferenceClient
+from openai import OpenAI
 
+# ---------- 读取 API Key ----------
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+if not OPENAI_API_KEY:
+    raise RuntimeError(
+        "OPENAI_API_KEY is not set. Please go to Settings → Variables and secrets and add it."
+    )
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
+client = OpenAI(api_key=OPENAI_API_KEY)
+
+# 你可以按需要换成 gpt-4.1, gpt-4o, gpt-4.1-nano 等
+DEFAULT_MODEL = "gpt-4.1-mini"
+
+# ---------- Clare 助教的系统设定 ----------
+CLARE_SYSTEM_PROMPT = """
+You are Clare, an AI teaching assistant for Hanbridge University.
+
+Core identity:
+- You are patient, encouraging, and structured like a very good TA.
+- Your UI and responses should be in ENGLISH by default.
+- However, you can understand BOTH English and Chinese questions, and you may reply in Chinese if the user asks you to or is clearly more comfortable in Chinese.
+
+Your main jobs:
+1. Help students understand course concepts step by step.
+2. Ask short check-up questions to confirm understanding instead of giving huge long lectures.
+3. When the student is confused, break content into smaller chunks and use simple language first.
+4. When the student is advanced, you can switch to more technical explanations.
+
+Teaching style:
+- Prefer short paragraphs and bullet points.
+- Use concrete examples and analogies.
+- Frequently summarize: “So far, we have …”
+- When appropriate, give simple practice questions or mini-exercises.
+- If the user asks something outside the course, you can still help, but be honest about uncertainty.
+
+Safety and honesty:
+- If you don’t know, say you are not sure and suggest how to verify.
+- Do not fabricate references, exam answers, or grades.
+"""
+
+
+def build_messages(
+    user_message: str,
+    history: List[Tuple[str, str]],
+    language_preference: str,
+) -> List[Dict[str, str]]:
     """
-    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
+    把历史对话 + 当前问题 转成 OpenAI Chat API 需要的 messages 列表。
+    history: List of (user, assistant)
     """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+    messages: List[Dict[str, str]] = [{"role": "system", "content": CLARE_SYSTEM_PROMPT}]
 
-    messages = [{"role": "system", "content": system_message}]
+    # 语言偏好控制（英文/中文/自动）
+    if language_preference == "English":
+        messages.append(
+            {
+                "role": "system",
+                "content": "Please answer in English.",
+            }
+        )
+    elif language_preference == "中文":
+        messages.append(
+            {
+                "role": "system",
+                "content": "请你用中文回答学生的问题。",
+            }
+        )
 
-    messages.extend(history)
+    # 把历史对话加进去
+    for user, assistant in history:
+        messages.append({"role": "user", "content": user})
+        if assistant is not None:
+            messages.append({"role": "assistant", "content": assistant})
 
-    messages.append({"role": "user", "content": message})
+    # 当前这一轮的问题
+    messages.append({"role": "user", "content": user_message})
+    return messages
 
-    response = ""
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
+def chat_with_clare(
+    message: str,
+    history: List[Tuple[str, str]],
+    model_name: str,
+    language_preference: str,
+):
+    """
+    Gradio 调用的主函数：输入当前 message + 历史 history，返回 Clare 的回答和新的 history。
+    """
+    try:
+        messages = build_messages(message, history, language_preference)
+        response = client.chat.completions.create(
+            model=model_name or DEFAULT_MODEL,
+            messages=messages,
+            temperature=0.5,
+        )
+        answer = response.choices[0].message.content
+    except Exception as e:
+        answer = f"⚠️ Error talking to the model: {e}"
 
-        response += token
-        yield response
+    # Gradio 需要返回 (回答, 更新后的 history)
+    history = history + [(message, answer)]
+    return answer, history
 
 
-"""
-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="You are a friendly Chatbot.", 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()
+# ---------- Gradio UI ----------
+with gr.Blocks(title="Clare – Hanbridge AI Teaching Assistant") as demo:
+    gr.Markdown(
+        """
+        # 🎓 Clare – AI Teaching Assistant  
+        **Hanbridge University** · English UI · Supports both English & Chinese questions.
+
+        - Ask in English → Clare answers in English.  
+        - Ask in Chinese → Clare can answer in Chinese.  
+        - Use it as your study companion for courses, assignments, and exam review.
+        """
+    )
+
+    with gr.Row():
+        model_name = gr.Textbox(
+            label="Model name",
+            value=DEFAULT_MODEL,
+            info="For example: gpt-4.1-mini, gpt-4.1, gpt-4o, etc.",
+        )
+        language_preference = gr.Radio(
+            choices=["Auto", "English", "中文"],
+            value="Auto",
+            label="Preferred answer language",
+        )
+
+    chatbot = gr.Chatbot(
+        label="Clare Chat",
+        height=450,
+    )
+    user_input = gr.Textbox(
+        label="Your question",
+        placeholder="Ask Clare anything about your course, assignment, or study plan...",
+    )
+    clear_btn = gr.Button("Reset conversation")
+
+    # 使用 ChatInterface 的逻辑：手写一个简单的 handler
+    def respond(message, chat_history):
+        answer, new_history = chat_with_clare(
+            message,
+            chat_history,
+            model_name=model_name.value,
+            language_preference=language_preference.value,
+        )
+        return "", new_history  # 文本框清空 + 更新 history
 
+    user_input.submit(respond, [user_input, chatbot], [user_input, chatbot])
+    clear_btn.click(lambda: None, None, chatbot, queue=False)
 
+# Gradio 启动
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()