Update app.py

app.py

@@ -1,228 +1,23 @@
-# pixal_agent_full.py
-import os
-import datetime
-import gradio as gr
-import requests
-from typing import Optional, List
-from langchain.llms.base import LLM
-from langchain.agents import initialize_agent, AgentType,load_tools
-from langchain.agents import AgentExecutor, create_structured_chat_agent
-from langchain.tools import Tool
-from langchain_experimental.tools.python.tool import PythonREPLTool
-import queue
-from typing import Any, Dict
-import gradio as gr
-from langchain.callbacks.base import BaseCallbackHandler
-
-
-from langchain.tools import YouTubeSearchTool as YTS
-# 2. 커스텀 콜백 핸들러
-
-# github_model_llm.py
-"""
-GitHub Models API 기반 LLM 래퍼 (LangChain LLM 호환)
-- OpenAI-style chat completions 호환
-- function calling (OPENAI_MULTI_FUNCTIONS) 지원: functions, function_call 전달 가능
-- system prompt (system_prompt) 지원
-- 옵션: temperature, max_tokens, top_p 등 전달
-- raw response 반환 메서드 포함
-"""
-
+import os, json, pickle, datetime, requests, re, gradio as gr
 from typing import Optional, List, Dict, Any
-import os
-import time
-import json
-import requests
 from requests.adapters import HTTPAdapter, Retry
 from langchain.llms.base import LLM
+from langchain.agents import initialize_agent, AgentType, load_tools
+from langchain.tools import Tool
+from langchain.memory import ConversationBufferMemory
+from langchain_experimental.tools.python.tool import PythonREPLTool
+from langchain_community.retrievers import WikipediaRetriever
+from langchain.tools.retriever import create_retriever_tool
+from langchain_community.tools.shell.tool import ShellTool
+from langchain.tools import YouTubeSearchTool
 
-'''
-class GitHubModelLLM(LLM):
-
-    def __init__(
-        self,
-        model: str = "openai/gpt-4.1",
-        token: Optional[str] = os.environ["token"],
-        endpoint: str = "https://models.github.ai/inference",
-        system_prompt: Optional[str] = "너는 PIXAL(Primary Interactive X-ternal Assistant with multi Language)이야.너의 개발자는 정성윤 이라는 6학년 파이썬 프로그래머야.",
-        request_timeout: float = 30.0,
-        max_retries: int = 2,
-        backoff_factor: float = 0.3,
-        **kwargs,
-    ):
-        """
-        Args:
-            model: 모델 이름 (예: "openai/gpt-4.1")
-            token: GitHub Models API 토큰 (Bearer). 환경변수 GITHUB_TOKEN / token 사용 가능 as fallback.
-            endpoint: API endpoint (기본: https://models.github.ai/inference)
-            system_prompt: (선택) system role 메시지로 항상 앞에 붙임
-            request_timeout: 요청 타임아웃 (초)
-            max_retries: 네트워크 재시도 횟수
-            backoff_factor: 재시도 지수 보정
-            kwargs: LangChain LLM 부모에 전달할 추가 인자
-        """
-        super().__init__(**kwargs)
-        self.model = model
-        self.endpoint = endpoint.rstrip("/")
-        self.token = token or os.getenv("GITHUB_TOKEN") or os.getenv("token")
-        self.system_prompt = system_prompt
-        self.request_timeout = request_timeout
-
-        # requests 세션 + 재시도 설정
-        self.session = requests.Session()
-        retries = Retry(total=max_retries, backoff_factor=backoff_factor,
-                        status_forcelist=[429, 500, 502, 503, 504],
-                        allowed_methods=["POST", "GET"])
-        self.session.mount("https://", HTTPAdapter(max_retries=retries))
-        self.session.headers.update({
-            "Content-Type": "application/json"
-        })
-        if self.token:
-            self.session.headers.update({"Authorization": f"Bearer {self.token}"})
-
-    @property
-    def _llm_type(self) -> str:
-        return "github_models_api"
-
-    # ---------- 편의 internal helper ----------
-    def _build_messages(self, prompt: str, extra_messages: Optional[List[Dict[str, Any]]] = None) -> List[Dict[str, Any]]:
-        """
-        messages 배열 생성: system (optional) + extra_messages (if any) + user prompt
-        extra_messages: 이미 role keys로 구성된 메시지 리스트 (예: conversation history)
-        """
-        msgs: List[Dict[str, Any]] = []
-        if self.system_prompt:
-            msgs.append({"role": "system", "content": self.system_prompt})
-        if extra_messages:
-            # ensure format: list of {"role":..,"content":..}
-            msgs.extend(extra_messages)
-        msgs.append({"role": "user", "content": prompt})
-        return msgs
-
-    def _post_chat(self, body: Dict[str, Any]) -> Dict[str, Any]:
-        url = f"{self.endpoint}/chat/completions"
-        # ensure Authorization present
-        if "Authorization" not in self.session.headers and not self.token:
-            raise ValueError("GitHub Models token not set. Provide token param or set GITHUB_TOKEN env var.")
-
-        resp = self.session.post(url, json=body, timeout=self.request_timeout)
-        try:
-            resp.raise_for_status()
-        except requests.HTTPError as e:
-            # try to surface JSON error if present
-            content = resp.text
-            try:
-                j = resp.json()
-                content = json.dumps(j, ensure_ascii=False, indent=2)
-            except Exception:
-                pass
-            raise RuntimeError(f"GitHub Models API error: {e} - {content}")
-        return resp.json()
-
-    # ---------- LangChain LLM interface ----------
-    def _call(self, prompt: str, stop: Optional[List[str]] = None, **kwargs) -> str:
-        """
-        LangChain LLM `_call` 구현 (동기).
-        Supports kwargs:
-            - functions: list[dict] (function schemas)
-            - function_call: "auto" | {"name": "..."} | etc.
-            - messages: list[dict]  (if you want to pass full conversation instead of prompt)
-            - temperature, top_p, max_tokens, n, stream, etc.
-        Returns:
-            assistant content (string). If function_call is returned by model, returns the 'content' if present,
-            otherwise returns function_call object as JSON string (so caller can parse).
-        """
-        # support passing full messages via kwargs['messages']
-        messages = None
-        extra_messages = None
-        if "messages" in kwargs and isinstance(kwargs["messages"], list):
-            messages = kwargs.pop("messages")
-        else:
-            # optionally allow 'history' or 'extra_messages'
-            extra_messages = kwargs.pop("extra_messages", None)
-
-        if messages is None:
-            messages = self._build_messages(prompt, extra_messages=extra_messages)
-
-        body: Dict[str, Any] = {
-            "model": self.model,
-            "messages": messages,
-        }
-
-        # pass optional top-level params (temperature, max_tokens, etc.) from kwargs
-        for opt in ["temperature", "top_p", "max_tokens", "n", "stream", "presence_penalty", "frequency_penalty"]:
-            if opt in kwargs:
-                body[opt] = kwargs.pop(opt)
-
-        # pass function-calling related keys verbatim if provided
-        if "functions" in kwargs:
-            body["functions"] = kwargs.pop("functions")
-        if "function_call" in kwargs:
-            body["function_call"] = kwargs.pop("function_call")
-
-        # include stop if present
-        if stop:
-            body["stop"] = stop
-
-        # send request
-        raw = self._post_chat(body)
-
-        # save raw for caller if needed
-        self._last_raw = raw
-
-        # parse assistant message
-        choices = raw.get("choices") or []
-        if not choices:
-            return ""
-
-        message_obj = choices[0].get("message", {})
-
-        # if assistant returned a function_call, include that info
-        if "function_call" in message_obj:
-            # return function_call as JSON string so agent/tool orchestrator can parse it
-            # but if content also exists, prefer content
-            func = message_obj["function_call"]
-            # sometimes content may be absent; return structured JSON string
-            return json.dumps({"function_call": func}, ensure_ascii=False)
-
-        # otherwise return assistant content
-        return message_obj.get("content", "") or ""
-
-    # optional: expose raw response getter
-    def last_raw_response(self) -> Optional[Dict[str, Any]]:
-        return getattr(self, "_last_raw", None)
-
-    # optional: provide a convenience chat method to get full message object
-    def chat_completions(self, prompt: str, messages: Optional[List[Dict[str, Any]]] = None, **kwargs) -> Dict[str, Any]:
-        """
-        Directly call chat completions and return full parsed JSON response.
-        - If `messages` provided, it's used as the full messages array (system/user/assistant roles as needed)
-        - else uses prompt + system_prompt to construct messages.
-        """
-        if messages is None:
-            messages = self._build_messages(prompt)
-        body: Dict[str, Any] = {"model": self.model, "messages": messages}
-        for opt in ["temperature", "top_p", "max_tokens", "n", "stream"]:
-            if opt in kwargs:
-                body[opt] = kwargs.pop(opt)
-        if "functions" in kwargs:
-            body["functions"] = kwargs.pop("functions")
-        if "function_call" in kwargs:
-            body["function_call"] = kwargs.pop("function_call")
-        raw = self._post_chat(body)
-        self._last_raw = raw
-        return raw
-'''
-from typing import Optional, List, Dict, Any
-from langchain.llms.base import LLM
-import requests, os, json
-from requests.adapters import HTTPAdapter, Retry
-
+# ──────────────────────────────
+# ✅ GitHubModelLLM (그대로 유지)
+# ──────────────────────────────
 class GitHubModelLLM(LLM):
-    """GitHub Models API 기반 LangChain LLM (Pydantic 호환)"""
     model: str = "openai/gpt-4.1"
     endpoint: str = "https://models.github.ai/inference"
-    token: Optional[str] = os.environ["token"]
+    token: Optional[str] = os.environ.get("token")
     system_prompt: Optional[str] = "너는 PIXAL(Primary Interactive X-ternal Assistant with multi Language)이야.너의 개발자는 정성윤 이라는 6학년 파이썬 프로그래머야."
     request_timeout: float = 30.0
     max_retries: int = 2
@@ -236,7 +31,6 @@ class GitHubModelLLM(LLM):
         token = self.token or os.getenv("GITHUB_TOKEN") or os.getenv("token")
         if not token:
             raise ValueError("❌ GitHub token이 설정되지 않았습니다.")
-
         session = requests.Session()
         retries = Retry(total=self.max_retries, backoff_factor=self.backoff_factor,
                         status_forcelist=[429, 500, 502, 503, 504])
@@ -250,182 +44,86 @@ class GitHubModelLLM(LLM):
         return resp.json()
 
     def _call(self, prompt: str, stop: Optional[List[str]] = None, **kwargs) -> str:
-        body = {
-            "model": self.model,
-            "messages": []
-        }
+        body = {"model": self.model, "messages": []}
         if self.system_prompt:
             body["messages"].append({"role": "system", "content": self.system_prompt})
         body["messages"].append({"role": "user", "content": prompt})
-
-        for key in ["temperature", "max_tokens", "functions", "function_call"]:
-            if key in kwargs:
-                body[key] = kwargs[key]
         if stop:
             body["stop"] = stop
-
         res = self._post_chat(body)
         msg = res.get("choices", [{}])[0].get("message", {})
         return msg.get("content") or json.dumps(msg.get("function_call", {}))
 
-from langchain_community.retrievers import WikipediaRetriever
-from langchain.tools.retriever import create_retriever_tool
-retriever = WikipediaRetriever(lang="ko",top_k_results=10)
-wiki=Tool(func=retriever.get_relevant_documents,name="WIKI SEARCH",description="위키백과에서 필요한 정보를 불러옵니다.결괴를 검증하여 사용하시오.")
 # ──────────────────────────────
-# ✅ GitHub Models LLM
+# ✅ HuggingFace API (프로필)
 # ──────────────────────────────
-'''
-class GitHubModelLLM(LLM):
-    model: str = "openai/gpt-4.1"
-    endpoint: str = "https://models.github.ai/inference"
-    token: Optional[str] = None
-
-    @property
-    def _llm_type(self) -> str:
-        return "github_models_api"
-
-    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
-        if not self.token:
-            raise ValueError("GitHub API token이 필요합니다.")
-
-        headers = {
-            "Authorization": "Bearer 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",
-            "Content-Type": "application/json",
-        }
-        body = {"model": self.model, "messages": [{"role": "user", "content": prompt}]}
+def get_hf_userinfo(hf_token: str) -> dict:
+    try:
+        r = requests.get("https://huggingface.co/api/whoami-v2",
+                         headers={"Authorization": f"Bearer {hf_token}"}, timeout=5)
+        if r.status_code == 200:
+            j = r.json()
+            return {
+                "name": j.get("name", "guest"),
+                "avatar": j.get("avatar", "https://huggingface.co/front/assets/huggingface_logo-noborder.svg")
+            }
+    except Exception:
+        pass
+    return {"name": "guest", "avatar": "https://huggingface.co/front/assets/huggingface_logo-noborder.svg"}
 
-        resp = requests.post(f"{self.endpoint}/chat/completions", json=body, headers=headers)
-        if resp.status_code != 200:
-            raise ValueError(f"API 오류: {resp.status_code} - {resp.text}")
-        return resp.json()["choices"][0]["message"]["content"]
-'''
 # ──────────────────────────────
-# ✅ LLM 설정
+# ✅ Agent 구성
 # ──────────────────────────────
-token = os.getenv("GITHUB_TOKEN") or os.getenv("token")
-if not token:
-    print("⚠️ GitHub Token이 필요합니다. 예: setx GITHUB_TOKEN your_token")
-
 llm = GitHubModelLLM()
-
-# ──────────────────────────────
-# ✅ LangChain 기본 도구 불러오기
-# ──────────────────────────────
-tools = load_tools(
-    ["ddg-search", "requests_all", "llm-math"],
-    llm=llm,allow_dangerous_tools=True
-)+[YTS()]+[wiki]
-# ──────────────────────────────
-# ✅ Python 실행 도구 (LangChain 내장)
-# ──────────────────────────────
-python_tool = PythonREPLTool()
-tools.append(Tool(name="python_repl", func=python_tool.run, description="Python 코드를 실행합니다."))
-from langchain import hub
-prompt=hub.pull("hwchase17/structured-chat-agent")
-from langchain_community.tools.shell.tool import ShellTool
-shell_tool = ShellTool()
-tools.append(Tool(name="shell_exec", func=shell_tool.run, description="로컬 명령어를 실행합니다."))
-
-# ──────────────────────────────
-# ✅ 파일 도구
-# ──────────────────────────────
-
-
-# ──────────────────────────────
-# ✅ 정확한 한국 시간 함수 (Asia/Seoul)
-# ──────────────────────────────
-import requests
-from zoneinfo import ZoneInfo
+tools = load_tools(["ddg-search", "requests_all", "llm-math"], llm=llm, allow_dangerous_tools=True)
+tools += [YouTubeSearchTool(), ShellTool(), PythonREPLTool()]
+retriever = WikipediaRetriever(lang="ko")
+retriever_tool = create_retriever_tool(retriever, name="wiki_search", description="위키백과 검색 도구")
+tools.append(retriever_tool)
 
 def time_now(_=""):
-    try:
-        # 정확한 UTC 시각을 외부 API에서 가져옴
-        resp = requests.get("https://timeapi.io/api/Time/current/zone?timeZone=Asia/Seoul", timeout=5)
-        if resp.status_code == 200:
-            data = resp.json()
-            dt = data["dateTime"].split(".")[0].replace("T", " ")
-            return f"현재 시각: {dt} (Asia/Seoul, 서버 기준 NTP 동기화)"
-        else:
-            # API 실패 시 로컬 시스템 시각으로 대체
-            tz = ZoneInfo("Asia/Seoul")
-            now = datetime.datetime.now(tz)
-            return f"현재 시각(로컬): {now.strftime('%Y-%m-%d %H:%M:%S')} (Asia/Seoul)"
-    except Exception as e:
-        tz = ZoneInfo("Asia/Seoul")
-        now = datetime.datetime.now(tz)
-        return f"현재 시각(백업): {now.strftime('%Y-%m-%d %H:%M:%S')} (Asia/Seoul, 오류: {e})"
-# ──────────────────────────────
-# ✅ 도구 등록
-# ──────────────────────────────
-tools.extend([Tool(name="time_now", func=time_now, description="현재 시간을 반환합니다.")])
-from langchain.memory import ConversationBufferMemory as MEM
-from langchain.agents.agent_toolkits import FileManagementToolkit as FMT
-tools.extend(FMT(root_dir=str(os.getcwd())).get_tools())
-# ──────────────────────────────
-# ✅ Agent 초기화
-# ──────────────────────────────
-mem=MEM()
-agent=initialize_agent(tools,llm,agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,verbose=True,memory=mem)
-#agent = create_structured_chat_agent(llm, tools, prompt)
-#agent= AgentExecutor(agent=agent, tools=tools,memory=mem)
+    now = datetime.datetime.now(datetime.timezone(datetime.timedelta(hours=9)))
+    return f"현재 시각: {now.strftime('%Y-%m-%d %H:%M:%S')} (Asia/Seoul)"
+tools.append(Tool(name="time_now", func=time_now, description="현재 시간을 반환합니다."))
 
-# ... (위의 LLM, tools, agent 설정 부분은 동일)
-
-
-import json
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+agent = initialize_agent(tools, llm, agent_type=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
+                         memory=memory, verbose=True)
 
 # ──────────────────────────────
-# ✅ 대화 요약 함수
+# ✅ 대화 저장/로드
 # ──────────────────────────────
+os.chdir(os.path.dirname(os.path.abspath(__file__)))
+
 def summarize_title(history):
-    """대화 제목 요약"""
-    if not history: return "새 대화"
-    text = "\n".join(f"User:{h[0]} AI:{h[1]}" for h in history[-3:])
+    if not history:
+        return "새 대화"
+    text = "\n".join(f"User:{m} AI:{r}" for m, r in history[-3:])
     try:
-        title = llm._call(f"다음 대화의 주제를 한 줄로 요약해줘:\n{text}")
-        return title.strip().replace("\n", " ")[:60]
+        title = llm._call(f"이 대화의 주제를 한 줄로 요약해줘:\n{text}")
+        return title.strip().replace("\n", " ")[:50]
     except Exception:
         return "요약 실패"
 
-import pickle
-import os, datetime
-
-# 현재 디렉터리로 고정
-os.chdir(os.path.dirname(os.path.abspath(__file__)))
-os.makedirs("user_logs", exist_ok=True)
-
-# --- 대화 기록 저장/불러오기 ---
-def save_conversation(username, history):
-    """로그인된 사용자는 여러 대화를 하나의 pickle 파일에 저장"""
-    if not username or username.lower() == "guest":
+def save_conversation(history, hf_token):
+    info = get_hf_userinfo(hf_token)
+    username = info["name"]
+    if username.lower() == "guest":
         return
-
     fname = f"{username}.pkl"
-    # 기존 데이터 불러오기
     data = {}
     if os.path.exists(fname):
-        try:
-            with open(fname, "rb") as f:
-                data = pickle.load(f)
-        except Exception:
-            data = {}
-
-    # 새로운 대화 요약 제목과 함께 추가
+        with open(fname, "rb") as f:
+            data = pickle.load(f)
     title = summarize_title(history)
-    data[title] = {
-        "title": title,
-        "updated": datetime.datetime.now().isoformat(),
-        "history": history
-    }
-
+    data[title] = {"title": title, "updated": datetime.datetime.now().isoformat(), "history": history}
     with open(fname, "wb") as f:
         pickle.dump(data, f)
 
-# --- 대화 불러오기 ---
-def load_conversation(username, conv_title=None):
-    """특정 사용자와 대화 제목으로 불러오기"""
-    if not username or username.lower() == "guest":
+def load_conversation(hf_token, conv_title=None):
+    info = get_hf_userinfo(hf_token)
+    username = info["name"]
+    if username.lower() == "guest":
         return []
     fname = f"{username}.pkl"
     if not os.path.exists(fname):
@@ -435,31 +133,29 @@ def load_conversation(username, conv_title=None):
     if conv_title and conv_title in data:
         return data[conv_title]["history"]
     elif data:
-        # 가장 최근 대화 반환
         latest = max(data.values(), key=lambda x: x["updated"])
         return latest["history"]
     return []
 
-# --- 대화 목록 갱신 ---
-def refresh_conversation_list(username=None):
-    """사용자별 저장된 대화 제목 목록을 새로고침"""
-    if not username or username.lower() == "guest":
+def refresh_conversation_list(hf_token):
+    info = get_hf_userinfo(hf_token)
+    username = info["name"]
+    if username.lower() == "guest":
         return gr.update(choices=[], value=None)
     fname = f"{username}.pkl"
     if not os.path.exists(fname):
         return gr.update(choices=[], value=None)
+    with open(fname, "rb") as f:
+        data = pickle.load(f)
+    titles = sorted(data.keys(), reverse=True)
+    return gr.update(choices=titles, value=titles[0] if titles else None)
+
+# ──────────────────────────────
+# ✅ Chat 함수
+# ──────────────────────────────
+def chat(message, history,hf_token):
     try:
-        with open(fname, "rb") as f:
-            data = pickle.load(f)
-        titles = sorted(data.keys(), reverse=True)
-        return gr.update(choices=titles, value=titles[0] if titles else None)
-    except Exception as e:
-        print(f"⚠️ 목록 불러오기 오류: {e}")
-        return gr.update(choices=[], value=None)
-# --- chat 함수 수정 ---
-def chat(message, history, username="guest", conv_name="current"):
-    try:
-        raw_response = agent.run(message)
+        raw_response = agent.invoke(message)
         text = str(raw_response)
 
         # JSON 형식 응답 파싱
@@ -482,178 +178,45 @@ def chat(message, history, username="guest", conv_name="current"):
 
     # 기록 추가 및 즉시 저장
     history = history + [(message, output)]
-    save_conversation(username, history, conv_name)
+    save_conversation(history, hf_token)
     return history, history, ""
 
-# --- 불러오기 버튼 함수 ---
-
 # ──────────────────────────────
-# ✅ 로그인 후 사용자 정보 가져오기
+# ✅ Gradio UI (ChatGPT 스타일)
 # ──────────────────────────────
-def get_hf_user(token):
-    """HF OAuth 토큰으로 사용자 정보 조회"""
-    try:
-        r = requests.get("https://huggingface.co/api/whoami-v2", headers={"Authorization": f"Bearer {token}"})
-        if r.status_code == 200:
-            data = r.json()
-            return data.get("name") or data.get("email") or "unknown_user"
-    except Exception:
-        pass
-    return "guest"
-import re, json
-
-
-'''
-def chat(message, history, hf_token):
-    username = get_hf_user(hf_token) if hf_token else "guest"
-    try:
-        response = agent.invoke(message)
-        if isinstance(response, dict):
-            if "action_input" in response:
-                response = response["action_input"]
-            elif "output" in response:
-                response = response["output"]
-            elif "text" in response:
-                response = response["text"]
-            else:
-                response = str(response)
-        elif isinstance(response, str):
-            # "Final Answer"가 포함된 문자열이면 그 부분만 추출
-            if '"action_input":' in response:
-                import re, json
-                match = re.search(r'["\']action_input["\']\s*:\s*["\'](.*?)["\']', response)
-                if match:
-                    response = match.group(1)
-            elif "Final Answer" in response:
-                # {"action": "Final Answer", "action_input": "..."} 형식일 때
-                try:
-                    data = json.loads(response)
-                    if isinstance(data, dict) and "action_input" in data:
-                        response = data["action_input"]
-                except Exception:
-                    response = response.replace("Final Answer", "").strip()
-    except Exception as e:
-        response = f"⚠️ 오류: {e}"
-    history = history + [(message, response)]
-    if username:
-        save_conversation(username, history)
-    return history, history, ""  # 입력 초기화
-'''
-# 예: hf_token (혹은 username) 을 입력으로 받도록 변경
+with gr.Blocks(theme=gr.themes.Soft(), title="PIXAL Assistant (HuggingFace OAuth)") as demo:
+    with gr.Row(elem_id="header", style="background:#f5f5f5;padding:12px;border-bottom:1px solid #ddd;align-items:center;"):
+        gr.HTML("<h2 style='margin:0;padding-left:12px;'>🤖 PIXAL Assistant</h2>")
+        user_avatar = gr.Image(show_label=False, width=40, height=40, elem_id="avatar")
+        user_name = gr.Markdown("로그인 필요", elem_id="username", elem_classes="text-right")
+        login_btn = gr.LoginButton(label="🔐 로그인", elem_id="login-btn")
+        hf_token = gr.State("")
 
+        def on_login(token):
+            info = get_hf_userinfo(token)
+            return token, info["avatar"], f"**{info['name']}**"
 
-
-
-# ──────────────────────────────
-# ✅ Gradio UI with HF Auth
-# ──────────────────────────────
-with gr.Blocks(theme=gr.themes.Soft(), title="PIXAL Assistant (HF Auth)") as demo:
-    gr.Markdown("## 🤖 PIXAL Assistant — Hugging Face 계정 기반 대화 저장")
-
-    hf_login = gr.LoginButton()
-    hf_token = gr.State()
-
-    @hf_login.click(inputs=None, outputs=hf_token)
-    def login(token):  # 로그인 후 token 반환
-        return token
+        login_btn.login(on_login, None, [hf_token, user_avatar, user_name])
 
     with gr.Row():
-        with gr.Column(scale=2):
-            chatbot = gr.Chatbot(label="PIXAL 대화", height=600, render_markdown=True)
-            msg = gr.Textbox(label="메시지", placeholder="입력 후 Enter 또는 전송 클릭")
-            send = gr.Button("전송")
-            clear = gr.Button("초기화")
+        with gr.Column(scale=3):
+            chatbot = gr.Chatbot(label=None, height=600, render_markdown=True)
+            msg = gr.Textbox(placeholder="메시지를 입력하세요...", show_label=False)
+            send = gr.Button("전송", variant="primary")
+            clear = gr.Button("🧹 초기화")
 
             msg.submit(chat, [msg, chatbot, hf_token], [chatbot, chatbot, msg])
             send.click(chat, [msg, chatbot, hf_token], [chatbot, chatbot, msg])
             clear.click(lambda: None, None, chatbot, queue=False)
 
         with gr.Column(scale=1):
-            gr.Markdown("### 💾 저장된 대화 기록")
-            convo_files = gr.Dropdown(label="대화 선택", choices=[])
-            refresh_btn = gr.Button("🔄 목록 새로고침")
-            load_btn = gr.Button("불러오기")
+            gr.Markdown("### 💾 저장된 대화")
+            convo_list = gr.Dropdown(label="대화 선택", choices=[])
+            refresh_btn = gr.Button("🔄 새로고침")
+            load_btn = gr.Button("📂 불러오기")
 
-            refresh_btn.click(refresh_conversation_list, None, convo_files)
-            load_btn.click(load_conversation, [convo_files], chatbot)
+            refresh_btn.click(refresh_conversation_list, [hf_token], convo_list)
+            load_btn.click(load_conversation, [hf_token, convo_list], chatbot)
 
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)
-'''
-def chat(message, history):
-    try:
-        response = agent.run(message)
-
-        # JSON 형태로 출력될 가능성이 있는 경우 처리
-        if isinstance(response, dict):
-            if "action_input" in response:
-                response = response["action_input"]
-            elif "output" in response:
-                response = response["output"]
-            elif "text" in response:
-                response = response["text"]
-            else:
-                response = str(response)
-        elif isinstance(response, str):
-            # "Final Answer"가 포함된 문자열이면 그 부분만 추출
-            if '"action_input":' in response:
-                import re, json
-                match = re.search(r'["\']action_input["\']\s*:\s*["\'](.*?)["\']', response)
-                if match:
-                    response = match.group(1)
-            elif "Final Answer" in response:
-                # {"action": "Final Answer", "action_input": "..."} 형식일 때
-                try:
-                    data = json.loads(response)
-                    if isinstance(data, dict) and "action_input" in data:
-                        response = data["action_input"]
-                except Exception:
-                    response = response.replace("Final Answer", "").strip()
-
-    except Exception as e:
-        response = f"⚠️ 오류: {e}"
-
-    history = history + [(message, response)]
-    return history, history,""
-
-# ──────────────────────────────
-# ✅ Gradio UI
-# ──────────────────────────────
-def load_selected(file):
-    return load_conversation(file)
-
-# ──────────────────────────────
-# ✅ Gradio UI
-# ──────────────────────────────
-with gr.Blocks(theme=gr.themes.Soft(), title="PIXAL Assistant") as demo:
-    gr.Markdown("## 🤖 PIXAL Assistant — LangChain 기반 멀티툴 에이전트")
-
-    with gr.Row():
-        with gr.Column(scale=2):
-            chatbot = gr.Chatbot(label="PIXAL 대화", height=600)
-            msg = gr.Textbox(label="메시지", placeholder="입력 후 Enter 또는 전송 클릭")
-            send = gr.Button("전송")
-            clear = gr.Button("초기화")
-
-            username = gr.Textbox(label="Hugging Face 사용자명", placeholder="로그인 대신 이름 입력", value=os.getenv("HF_USER", "guest"))
-            msg.submit(chat, [msg, chatbot, username], [chatbot, chatbot, msg])
-            send.click(chat, [msg, chatbot, username], [chatbot, chatbot, msg])
-            clear.click(lambda: None, None, chatbot, queue=False)
-
-        with gr.Column(scale=1):
-            gr.Markdown("### 💾 저장된 대화 기록")
-            convo_files = gr.Dropdown(label="대화 선택", choices=[])
-            refresh_btn = gr.Button("🔄 목록 새로고침")
-            load_btn = gr.Button("불러오기")
-
-            def refresh_list(user):
-                if not user: return gr.Dropdown.update(choices=[])
-                return gr.Dropdown.update(choices=[x[1] for x in list_conversations(user)])
-
-            refresh_btn.click(refresh_list, [username], convo_files)
-            load_btn.click(lambda f: load_conversation(f), [convo_files], chatbot)
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
-
-'''