app.py

# app.py
from typing import List, Dict, Tuple, Optional

import gradio as gr

from config import (
    DEFAULT_MODEL,
    DEFAULT_COURSE_TOPICS,
    LEARNING_MODES,
    DOC_TYPES,
)
from clare_core import (
    parse_syllabus_docx,
    update_weaknesses_from_message,
    update_cognitive_state_from_message,
    render_session_status,
    find_similar_past_question,
    detect_language,
    chat_with_clare,
    export_conversation,
    generate_quiz_from_history,
    get_empty_input_prompt,
    summarize_conversation,
)
from rag_engine import (
    build_rag_chunks_from_file,
    retrieve_relevant_chunks,
)

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 different **learning modes** to change Clare's teaching style.  
        - Optionally upload your **course syllabus / slides / literature (.docx)** so Clare stays aligned with your course.
        """
    )

    # 顶部：模型、语言偏好、学习模式
    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",
        )
        learning_mode = gr.Radio(
            choices=LEARNING_MODES,
            value="Concept Explainer",
            label="Learning mode",
        )

    # 课程文件上传
    with gr.Row():
        syllabus_file = gr.File(
            label="Upload course file (.docx)",
            file_types=[".docx"],
        )
        doc_type = gr.Dropdown(
            choices=DOC_TYPES,
            value="Syllabus",
            label="File type",
        )

    # 状态：课程大纲 + 学生弱项 + 认知状态 + RAG chunks
    course_outline_state = gr.State(DEFAULT_COURSE_TOPICS)
    weakness_state = gr.State([])
    cognitive_state_state = gr.State({"confusion": 0, "mastery": 0})
    rag_chunks_state = gr.State([])  # Session 级 RAG 向量库

    # 上传 syllabus 时更新课程大纲 + RAG chunks
    def update_course_and_rag(file, doc_type_val):
        # 更新课程大纲（保持原有逻辑）
        if file is None:
            topics = DEFAULT_COURSE_TOPICS
        else:
            if doc_type_val == "Syllabus":
                try:
                    file_path = file.name
                    if file_path.lower().endswith(".docx"):
                        topics = parse_syllabus_docx(file_path)
                    else:
                        topics = DEFAULT_COURSE_TOPICS
                except Exception:
                    topics = DEFAULT_COURSE_TOPICS
            else:
                topics = DEFAULT_COURSE_TOPICS

        # 构建 RAG chunks
        rag_chunks = build_rag_chunks_from_file(file, doc_type_val)

        return topics, rag_chunks

    syllabus_file.change(
        fn=update_course_and_rag,
        inputs=[syllabus_file, doc_type],
        outputs=[course_outline_state, rag_chunks_state],
    )

    # 左侧聊天，右侧 Session 状态栏
    with gr.Row():
        chatbot = gr.Chatbot(
            label="Clare Chat",
            height=450,
        )
        session_status = gr.Markdown(
            value=render_session_status("Concept Explainer", [], {"confusion": 0, "mastery": 0}),
            label="Session status",
        )

    user_input = gr.Textbox(
        label="Your question",
        placeholder="Ask Clare anything about your course, assignment, or study plan...",
    )

    with gr.Row():
        clear_btn = gr.Button("Reset conversation")
        export_btn = gr.Button("Export conversation")
        quiz_btn = gr.Button("Generate 3 quiz questions")
        summary_btn = gr.Button("Summarize concepts")

    export_box = gr.Textbox(
        label="Conversation export (Markdown)",
        lines=8,
    )
    quiz_box = gr.Textbox(
        label="Generated quiz (with answer key)",
        lines=8,
    )
    summary_box = gr.Textbox(
        label="Concept summary (for study notes)",
        lines=8,
    )

    # 主对话逻辑
    def respond(
        message,
        chat_history,
        course_outline,
        weaknesses,
        cognitive_state,
        rag_chunks,
        model_name_val,
        language_pref_val,
        learning_mode_val,
        doc_type_val,
    ):
        # 1) 决定本轮语言（Auto / English / 中文）
        resolved_lang = detect_language(message or "", language_pref_val)

        # 2) 空输入防护
        if not message or not message.strip():
            empty_msg = get_empty_input_prompt(resolved_lang)
            new_history = chat_history + [("", empty_msg)]
            status_text = render_session_status(learning_mode_val, weaknesses or [], cognitive_state)
            return "", new_history, weaknesses, cognitive_state, status_text

        # 3) 更新弱项 & 认知状态
        weaknesses = update_weaknesses_from_message(message, weaknesses or [])
        cognitive_state = update_cognitive_state_from_message(message, cognitive_state)

        # 4) Same Question Check（session 内复用答案）
        dup = find_similar_past_question(message, chat_history)
        if dup is not None:
            past_q, past_a, sim = dup
            prefix_en = (
                "I noticed this question is very similar to one you asked earlier, "
                "so I'm showing the previous explanation again. "
                "If there's a specific part that's still unclear, tell me and I can "
                "re-explain it in a different way.\n\n"
                "**Earlier answer:**\n"
            )
            prefix_zh = (
                "我注意到你现在的问题和之前问过的非常相似，"
                "所以我先把当时的回答再展示一次。"
                "如果还有具体不清楚的地方，可以告诉我，我会换一种方式解释。\n\n"
                "**之前的回答：**\n"
            )
            if resolved_lang == "中文":
                answer = prefix_zh + past_a
            else:
                answer = prefix_en + past_a

            new_history = chat_history + [(message, answer)]
            status_text = render_session_status(learning_mode_val, weaknesses, cognitive_state)
            return "", new_history, weaknesses, cognitive_state, status_text

        # 5) RAG：基于上传文档做检索
        rag_context = retrieve_relevant_chunks(message, rag_chunks or [])

        # 6) 正常调用 Clare（带上 RAG context）
        answer, new_history = chat_with_clare(
            message=message,
            history=chat_history,
            model_name=model_name_val,
            language_preference=resolved_lang,
            learning_mode=learning_mode_val,
            doc_type=doc_type_val,
            course_outline=course_outline,
            weaknesses=weaknesses,
            cognitive_state=cognitive_state,
            rag_context=rag_context,
        )

        status_text = render_session_status(learning_mode_val, weaknesses, cognitive_state)
        return "", new_history, weaknesses, cognitive_state, status_text

    user_input.submit(
        respond,
        [
            user_input,
            chatbot,
            course_outline_state,
            weakness_state,
            cognitive_state_state,
            rag_chunks_state,
            model_name,
            language_preference,
            learning_mode,
            doc_type,
        ],
        [user_input, chatbot, weakness_state, cognitive_state_state, session_status],
    )

    # 清空对话 & 状态
    def clear_all():
        empty_state = {"confusion": 0, "mastery": 0}
        status_text = render_session_status("Concept Explainer", [], empty_state)
        return (
            [],          # chatbot
            [],          # weaknesses
            empty_state, # cognitive_state
            [],          # rag_chunks
            "",          # export_box
            "",          # quiz_box
            "",          # summary_box
            status_text, # session_status
        )

    clear_btn.click(
        clear_all,
        None,
        [
            chatbot,
            weakness_state,
            cognitive_state_state,
            rag_chunks_state,
            export_box,
            quiz_box,
            summary_box,
            session_status,
        ],
        queue=False,
    )

    # 导出对话
    def on_export(chat_history, course_outline, learning_mode_val, weaknesses, cognitive_state):
        return export_conversation(
            chat_history,
            course_outline,
            learning_mode_val,
            weaknesses or [],
            cognitive_state,
        )

    export_btn.click(
        on_export,
        [chatbot, course_outline_state, learning_mode, weakness_state, cognitive_state_state],
        [export_box],
    )

    # 生成 quiz
    def on_quiz(
        chat_history,
        course_outline,
        weaknesses,
        cognitive_state,
        model_name_val,
        language_pref_val,
    ):
        return generate_quiz_from_history(
            chat_history,
            course_outline,
            weaknesses or [],
            cognitive_state,
            model_name_val,
            language_pref_val,
        )

    quiz_btn.click(
        on_quiz,
        [
            chatbot,
            course_outline_state,
            weakness_state,
            cognitive_state_state,
            model_name,
            language_preference,
        ],
        [quiz_box],
    )

    # 概念总结
    def on_summary(
        chat_history,
        course_outline,
        weaknesses,
        cognitive_state,
        model_name_val,
        language_pref_val,
    ):
        return summarize_conversation(
            chat_history,
            course_outline,
            weaknesses or [],
            cognitive_state,
            model_name_val,
            language_pref_val,
        )

    summary_btn.click(
        on_summary,
        [
            chatbot,
            course_outline_state,
            weakness_state,
            cognitive_state_state,
            model_name,
            language_preference,
        ],
        [summary_box],
    )

if __name__ == "__main__":
    demo.launch()