main.py

import json
import gradio as gr
from pathlib import Path
from counselor import PsychodynamicCounselor

counselor = None


def _depth_level(score):
    """将 1-10 分映射为探索层级"""
    if score <= 2:
        return "L1 · 表层", "社交性叙述，回避情感"
    elif score <= 4:
        return "L2 · 事件", "具体事件浮现，情绪初现"
    elif score <= 6:
        return "L3 · 情感", "情绪深化，开始反思模式"
    elif score <= 8:
        return "L4 · 核心", "触及核心冲突与早期经历"
    else:
        return "L5 · 突破", "深层揭露，防御松动"


def _phase_label(turn):
    """会话阶段"""
    if turn <= 3:
        return "建立联盟"
    elif turn <= 8:
        return "探索展开"
    elif turn <= 15:
        return "深层工作"
    else:
        return "整合修通"


def build_status_panel(c):
    """构建会话状态 Markdown 富文本面板"""
    score = c._last_disclosure_score
    turn = c.turn_number
    history = c._disclosure_history

    # ── 探索层级 ──
    level_name, level_desc = _depth_level(score)
    phase = _phase_label(turn)

    # 揭露深度进度条（渐变风格）
    bar_chars = "░▒▓█"
    filled_bar = ""
    for i in range(10):
        if i < score:
            ci = min(i // 3, 3)
            filled_bar += bar_chars[ci]
        else:
            filled_bar += "·"

    # 趋势计算
    if len(history) >= 2:
        diff = history[-1] - history[-2]
        trend = "▲" if diff > 0 else ("▼" if diff < 0 else "━")
        trend_word = f"+{diff}" if diff > 0 else str(diff) if diff < 0 else "±0"
    else:
        trend = "·"
        trend_word = "—"

    # 均值 & 峰值
    avg_score = sum(history) / len(history) if history else 0
    peak_score = max(history) if history else 0

    # 历史火花线 (sparkline)
    spark_chars = " ▁▂▃▄▅▆▇█"
    spark = ""
    for s in history[-20:]:
        idx = min(s, 9)
        spark += spark_chars[idx]
    if not spark:
        spark = "—"

    # 维度指示灯
    dims = c._last_dimensions
    dim_labels = {
        "A": "具体事件", "B": "情绪表达", "C": "具体情绪",
        "D": "自我反思", "E": "回避触及",
    }
    dim_on = sum(1 for k in ["A", "B", "C", "D", "E"] if dims.get(k, False))
    dim_parts = []
    for k in ["A", "B", "C", "D", "E"]:
        on = dims.get(k, False)
        icon = "🟢" if on else "⚫"
        dim_parts.append(f"{icon} {dim_labels[k]}")
    dim_line = " · ".join(dim_parts)

    # ── 构建面板 ──
    lines = []
    lines.append("### ◈ SESSION MONITOR")
    lines.append("")

    # 核心指标表
    lines.append("| | |")
    lines.append("|:---|:---|")
    lines.append(f"| **轮次** | `T{turn}` · {phase} |")
    lines.append(f"| **探索层级** | **{level_name}** — {level_desc} |")
    lines.append(f"| **揭露深度** | `{filled_bar}` **{score}/10** {trend} ({trend_word}) |")
    lines.append(f"| **均值 / 峰值** | avg `{avg_score:.1f}` · peak `{peak_score}` |")
    lines.append(f"| **深度轨迹** | `{spark}` |")
    lines.append(f"| **维度命中** | **{dim_on}/5** |")
    lines.append("")

    # 维度详情
    lines.append(f"> {dim_line}")
    lines.append("")

    # ── 督导模块 ──
    lines.append("---")
    if c.current_guidance:
        g = c.current_guidance
        direction = g.get("direction", "—")
        principles = g.get("principles", [])
        evidence = g.get("evidence", "")

        lines.append("#### ▸ 督导指令")
        lines.append("")
        lines.append(f"> 🎯 **{direction}**")
        if principles:
            lines.append(">")
            for i, p in enumerate(principles[:3], 1):
                lines.append(f"> {i}. {p}")
        if evidence:
            lines.append(">")
            lines.append(f"> 📌 {evidence[:100]}{'…' if len(evidence) > 100 else ''}")
        lines.append("")

        # ── PUCT 推理引擎 ──
        ts = c._last_trace_stats
        if ts:
            timing = ts.get("timing", {})
            total_s = timing.get("total_seconds", 0)
            total_paths = ts.get("total_paths", 0)
            deep = ts.get("deep_paths", 0)
            seeds = ts.get("seeds", [])
            selected = ts.get("selected", "")
            best_score = ts.get("best_score", 0)
            best_delta = ts.get("best_delta", 0)
            predicted = ts.get("predicted_disclosure", "?")

            lines.append("---")
            lines.append("#### ▸ PUCT 推理引擎")
            lines.append("")
            lines.append("| | |")
            lines.append("|:---|:---|")
            lines.append(f"| **搜索架构** | `L1 → L2 → L3 → L4 → L5 → L6` (6层深度) |")
            lines.append(f"| **种子方向** | {' / '.join(seeds) if seeds else '—'} |")
            lines.append(f"| **路径探索** | {total_paths} 候选 → {deep} 深探 |")
            lines.append(f"| **最优路径** | `{selected}` |")
            lines.append(f"| **路径评分** | score=**{best_score}** · Δ=**{best_delta:+.1f}** |")
            lines.append(f"| **预测揭露** | **{predicted}**/10 |")
            lines.append(f"| **推理耗时** | `{total_s:.1f}s` |")
    else:
        lines.append("#### ▸ 推理引擎")
        lines.append("")
        lines.append("> ⏳ 同步推理中…")

    # ── 评估理由（折叠） ──
    if c._last_reasoning:
        lines.append("")
        lines.append(f"<details><summary>📋 评估理由</summary>\n\n{c._last_reasoning}\n\n</details>")

    return "\n".join(lines)


def start_session():
    global counselor
    counselor = PsychodynamicCounselor()
    return [], "### 🧠 SESSION MONITOR\n\n> 新会话已开始，等待来访者发言…"


def chat(user_message, chat_history):
    global counselor
    if counselor is None:
        counselor = PsychodynamicCounselor()

    if not user_message.strip():
        return chat_history, "", "", ""

    response = counselor.respond(user_message)

    chat_history = chat_history or []
    chat_history.append({"role": "user", "content": user_message})
    chat_history.append({"role": "assistant", "content": response})

    # 构建富文本状态面板
    status = build_status_panel(counselor)

    return chat_history, "", status, ""


def end_session():
    global counselor
    if counselor:
        path = counselor.get_session_filepath()
        counselor = None
        return f"会话已结束。日志保存于：{path}"
    return "当前无活跃会话。"


def view_sessions():
    files = sorted(Path("sessions").glob("session_*.json"))
    if not files:
        return "暂无会话记录"

    output = ""
    for f in files:
        with open(f, encoding="utf-8") as fp:
            data = json.load(fp)

        total = data.get("total_turns", len(data["turns"]))
        output += f"\n{'='*50}\n"
        output += f"Session: {data['session_id']} | 轮次: {total}\n"
        output += f"{'='*50}\n"

        for t in data["turns"]:
            score = t.get("disclosure_score", "?")
            output += f"\n[轮次 {t['turn_number']}] 揭露评分: {score}/5\n"
            output += f"来访者: {t['user_message']}\n"
            output += f"咨询师: {t['counselor_message']}\n"
            dims = t.get("dimension_score", {})
            reason = t.get("reason", "")
            if dims:
                output += f"维度: {dims}\n"
            if reason:
                output += f"理由: {reason}\n"

            # 战略推理记录（每5轮）
            trace = t.get("mcts_trace")
            if trace and "selected_direction" in trace:
                output += f"\n  === 战略推理（第{t['turn_number']}轮触发） ===\n"
                output += f"  总结: {trace.get('summary', '')[:80]}...\n"
                output += f"  选中: {trace['selected']} → {trace['selected_direction'][:50]}\n"
                output += f"  预测揭露: {trace.get('selected_score', '?')}/10\n"
                for d in trace.get("directions", []):
                    marker = " ★" if d["id"] == trace["selected"] else ""
                    output += f"  [{d['id']}]{marker} {d.get('direction', '')[:40]} → 揭露={d.get('disclosure_level', '?')}/10\n"
                output += f"  ========================\n"

    return output


def download_all_sessions():
    files = sorted(Path("sessions").glob("session_*.json"))
    if not files:
        return None
    return [str(f) for f in files]


with gr.Blocks(title="Freud-Zero MVP") as app:
    gr.Markdown("# Freud-Zero MVP")
    gr.Markdown("精神动力学取向回应性咨询师 · 自我揭露深度追踪")

    with gr.Row():
        btn_start = gr.Button("开始新会话", variant="primary")
        btn_end = gr.Button("结束会话", variant="stop")

    chatbot = gr.Chatbot(label="对话", height=480, type="messages")

    with gr.Row():
        user_input = gr.Textbox(placeholder="说你想说的……", show_label=False, scale=4)
        btn_send = gr.Button("发送", scale=1)

    status_output = gr.Markdown(value="### 🧠 SESSION MONITOR\n\n> 等待开始会话…")

    with gr.Accordion("研究者面板", open=False):
        with gr.Row():
            btn_view = gr.Button("查看所有会话记录")
            btn_download = gr.Button("下载日志文件")
        log_display = gr.Textbox(label="会话日志", lines=20, interactive=False)
        file_output = gr.File(label="日志文件")

    # 绑定事件
    btn_start.click(start_session, outputs=[chatbot, status_output])
    btn_end.click(end_session, outputs=[status_output])
    btn_send.click(chat, inputs=[user_input, chatbot], outputs=[chatbot, user_input, status_output, log_display])
    user_input.submit(chat, inputs=[user_input, chatbot], outputs=[chatbot, user_input, status_output, log_display])
    btn_view.click(view_sessions, outputs=[log_display])
    btn_download.click(download_all_sessions, outputs=[file_output])

if __name__ == "__main__":
    app.launch(server_name="0.0.0.0", server_port=7860, share=True)