Update app.py

app.py

@@ -1,70 +1,215 @@
+# app.py
+# Naver News Search API -> Gradio chat-like UI (HF Spaces friendly)
+#
+# HF Spaces Secrets 설정:
+#   NAVER_CLIENT_ID     = 발급받은 Client ID
+#   NAVER_CLIENT_SECRET = 발급받은 Client Secret
+#
+# 로컬 실행 시(선택):
+#   export NAVER_CLIENT_ID="..."
+#   export NAVER_CLIENT_SECRET="..."
+
+import os
+import html
+import re
+from datetime import datetime
+from typing import Dict, Any, List, Tuple, Optional
+
+import requests
 import gradio as gr
-from huggingface_hub import InferenceClient
 
 
-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
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+NAVER_NEWS_ENDPOINT = "https://openapi.naver.com/v1/search/news.json"
 
-    messages = [{"role": "system", "content": system_message}]
 
-    messages.extend(history)
+def _get_env(name: str) -> str:
+    v = os.getenv(name, "").strip()
+    return v
 
-    messages.append({"role": "user", "content": message})
 
-    response = ""
+def _strip_tags(text: str) -> str:
+    # 네이버 뉴스 API 결과의 title/description은 <b>...</b>가 포함될 수 있어 제거
+    # (HTML 태그 제거 + 엔티티 처리)
+    if not text:
+        return ""
+    text = re.sub(r"<[^>]+>", "", text)
+    return html.unescape(text).strip()
 
-    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
 
-        response += token
-        yield response
+def _format_pubdate(pub_date: str) -> str:
+    # 예: "Mon, 19 Jan 2026 09:30:00 +0900"
+    # 파싱 실패 시 원문 반환
+    if not pub_date:
+        return ""
+    try:
+        dt = datetime.strptime(pub_date, "%a, %d %b %Y %H:%M:%S %z")
+        # 한국 환경에서 보기 좋은 포맷
+        return dt.strftime("%Y-%m-%d %H:%M:%S %z")
+    except Exception:
+        return pub_date
 
 
+def naver_news_search(
+    query: str,
+    display: int = 10,
+    sort: str = "date",
+    start: int = 1,
+    timeout: int = 10,
+) -> Dict[str, Any]:
+    client_id = _get_env("NAVER_CLIENT_ID")
+    client_secret = _get_env("NAVER_CLIENT_SECRET")
+
+    if not client_id or not client_secret:
+        raise RuntimeError(
+            "환경변수 NAVER_CLIENT_ID / NAVER_CLIENT_SECRET 이 설정되어 있지 않습니다. "
+            "Hugging Face Spaces의 Secrets에 추가해 주세요."
+        )
+
+    headers = {
+        "X-Naver-Client-Id": client_id,
+        "X-Naver-Client-Secret": client_secret,
+    }
+
+    params = {
+        "query": query,
+        "display": int(display),
+        "start": int(start),
+        "sort": sort,
+    }
+
+    r = requests.get(
+        NAVER_NEWS_ENDPOINT,
+        headers=headers,
+        params=params,
+        timeout=timeout,
+    )
+
+    # 네이버 API가 에러를 반환하는 경우, body에 상세가 있을 수 있어 그대로 보여주기 위해 처리
+    if r.status_code != 200:
+        try:
+            detail = r.json()
+        except Exception:
+            detail = {"text": r.text}
+        raise RuntimeError(f"네이버 API 호출 실패 (HTTP {r.status_code}): {detail}")
+
+    return r.json()
+
+
+def render_results(data: Dict[str, Any], max_items: int = 10) -> str:
+    items = data.get("items", [])[:max_items]
+    total = data.get("total", None)
+
+    lines: List[str] = []
+    if total is not None:
+        lines.append(f"- 총 검색결과: {total:,}건")
+    lines.append(f"- 반환 개수: {len(items)}건")
+    lines.append("")
+
+    for i, it in enumerate(items, start=1):
+        title = _strip_tags(it.get("title", ""))
+        desc = _strip_tags(it.get("description", ""))
+        link = it.get("link", "")
+        origin = it.get("originallink", "")
+        pub = _format_pubdate(it.get("pubDate", ""))
+
+        # 가독성을 위해 항목별 블록 형태로 구성
+        lines.append(f"{i}. **{title}**")
+        if pub:
+            lines.append(f"   - 발행: {pub}")
+        if origin:
+            lines.append(f"   - 원문: {origin}")
+        if link:
+            lines.append(f"   - 링크: {link}")
+        if desc:
+            lines.append(f"   - 요약: {desc}")
+        lines.append("")
+
+    return "\n".join(lines).strip()
+
+
+def handle_search(
+    user_query: str,
+    chat_history: List[Dict[str, str]],
+    display: int,
+    sort: str,
+) -> Tuple[List[Dict[str, str]], str]:
+    q = (user_query or "").strip()
+    if not q:
+        return chat_history, ""
+
+    # 사용자 메시지 추가
+    chat_history = chat_history + [{"role": "user", "content": q}]
+
+    try:
+        data = naver_news_search(query=q, display=display, sort=sort, start=1)
+        assistant_text = render_results(data, max_items=display)
+    except Exception as e:
+        assistant_text = f"오류가 발생했습니다.\n\n- {e}"
+
+    # 응답 추가
+    chat_history = chat_history + [{"role": "assistant", "content": assistant_text}]
+    return chat_history, ""
+
+
+with gr.Blocks(title="Naver News Search (Chat UI)") as demo:
+    gr.Markdown(
+        """
+# 네이버 뉴스 검색 테스트 (Gradio)
+- 하단 입력창에 검색어를 입력하면, 결과를 **채팅 형태**로 표시합니다.
+- 인증키는 Hugging Face Spaces **Secrets**의 `NAVER_CLIENT_ID`, `NAVER_CLIENT_SECRET`을 사용합니다.
 """
-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()
+    )
+
+    with gr.Row():
+        display = gr.Slider(
+            minimum=1, maximum=20, value=10, step=1, label="표시 개수(display)"
+        )
+        sort = gr.Dropdown(
+            choices=["date", "sim"],
+            value="date",
+            label="정렬(sort)",
+            info="date=최신순, sim=정확도순",
+        )
+
+    chatbot = gr.Chatbot(
+        value=[],
+        label="검색 결과",
+        type="messages",
+        height=520,
+    )
+
+    # 하단 입력 영역(요청하신 '바닥' 배치)
+    with gr.Row():
+        query_in = gr.Textbox(
+            label="검색어",
+            placeholder="예: 인공지능, 감사원, 방위산업, 반도체 ...",
+            scale=8,
+        )
+        search_btn = gr.Button("검색", variant="primary", scale=2)
+
+    with gr.Row():
+        clear_btn = gr.Button("대화 지우기", variant="secondary")
+
+    # 이벤트 바인딩: 엔터/버튼 모두 동일 처리
+    search_btn.click(
+        fn=handle_search,
+        inputs=[query_in, chatbot, display, sort],
+        outputs=[chatbot, query_in],
+        api_name="search",
+    )
+    query_in.submit(
+        fn=handle_search,
+        inputs=[query_in, chatbot, display, sort],
+        outputs=[chatbot, query_in],
+        api_name="search_submit",
+    )
+
+    def clear_chat():
+        return []
+
+    clear_btn.click(fn=clear_chat, inputs=[], outputs=[chatbot], api_name="clear")
 
 
 if __name__ == "__main__":
-    demo.launch()
+    # HF Spaces에서는 기본으로 7860 포트를 사용합니다.
+    demo.launch(server_name="0.0.0.0", server_port=7860)