Update app.py

app.py

@@ -1,196 +1,273 @@
+import spaces
 import os
+import re
+import time
+
 import gradio as gr
-import requests
-import inspect
 import pandas as pd
+import requests
+from smolagents import (
+    CodeAgent,
+    DuckDuckGoSearchTool,
+    InferenceClientModel,
+    LiteLLMModel,
+    VisitWebpageTool,
+    tool,
+)
 
-# (Keep Constants as is)
-# --- Constants ---
+# ---------------------------------------------------------------------------
+# 상수
+# ---------------------------------------------------------------------------
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+FILES_DIR = "/tmp/task_files"
+os.makedirs(FILES_DIR, exist_ok=True)
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
-
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+# 질문 사이 대기 시간(초). 무료 API의 rate limit 방지용
+SLEEP_BETWEEN_QUESTIONS = float(os.getenv("SLEEP_BETWEEN_QUESTIONS", "2"))
+
+# 채점이 '정확히 일치(exact match)' 방식이므로 답변 형식 규칙이 매우 중요합니다.
+ANSWER_RULES = """
+You are a careful research and reasoning agent solving GAIA-style questions.
+Rules:
+1. Use the tools (web search, webpage visit, file reading, python code) whenever the
+   question needs facts you are not 100% sure about. Do not guess.
+2. The final answer is graded by EXACT MATCH. Return ONLY the answer value, with no
+   explanation, no units unless asked, no trailing period, no "FINAL ANSWER:" prefix.
+3. If the answer is a number: no thousands separators, no currency/percent symbols
+   unless the question asks for them.
+4. If the answer is a list: comma-separated, in the order the question requests.
+5. If the answer is a string: no articles (a, an, the) and no abbreviations unless
+   the question asks for them.
+6. If a file path is given, read it with the read_local_file tool or with python code.
+7. Finish by calling final_answer(...) with the answer only.
+"""
+
+
+# ---------------------------------------------------------------------------
+# 도구
+# ---------------------------------------------------------------------------
+@tool
+def read_local_file(path: str) -> str:
+    """Reads a local file (txt, csv, json, py, md, xlsx) and returns its content as text.
+
+    Args:
+        path: Absolute path of the file to read.
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
+    try:
+        lower = path.lower()
+        if lower.endswith((".xlsx", ".xls")):
+            sheets = pd.read_excel(path, sheet_name=None)
+            parts = []
+            for name, df in sheets.items():
+                parts.append(f"## Sheet: {name}\n{df.to_string(max_rows=200)}")
+            return "\n\n".join(parts)
+        if lower.endswith(".csv"):
+            return pd.read_csv(path).to_string(max_rows=200)
+        with open(path, "r", encoding="utf-8", errors="ignore") as f:
+            return f.read()[:20000]
+    except Exception as e:  # noqa: BLE001
+        return f"Could not read file: {e}"
+
+
+# ---------------------------------------------------------------------------
+# 에이전트
+# ---------------------------------------------------------------------------
+def build_model():
+    """환경변수(Secrets)에 따라 사용할 LLM을 선택합니다.
+
+    우선순위: GEMINI_API_KEY > OPENAI_API_KEY > HF_TOKEN(기본)
     """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+    gemini_key = os.getenv("GEMINI_API_KEY")
+    if gemini_key:
+        model_id = os.getenv("GEMINI_MODEL", "gemini-2.5-flash")
+        print(f"[model] Gemini: {model_id}")
+        return LiteLLMModel(model_id=f"gemini/{model_id}", api_key=gemini_key)
 
-    if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
-    else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
+    openai_key = os.getenv("OPENAI_API_KEY")
+    if openai_key:
+        model_id = os.getenv("OPENAI_MODEL", "gpt-4o-mini")
+        print(f"[model] OpenAI: {model_id}")
+        return LiteLLMModel(model_id=f"openai/{model_id}", api_key=openai_key)
 
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
+    model_id = os.getenv("HF_MODEL", "Qwen/Qwen2.5-Coder-32B-Instruct")
+    print(f"[model] HF Inference: {model_id}")
+    return InferenceClientModel(model_id=model_id, token=os.getenv("HF_TOKEN"))
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
-    try:
-        agent = BasicAgent()
-    except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
 
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
+class BasicAgent:
+    def __init__(self):
+        print("BasicAgent 초기화 중...")
+        self.agent = CodeAgent(
+            tools=[DuckDuckGoSearchTool(), VisitWebpageTool(), read_local_file],
+            model=build_model(),
+            additional_authorized_imports=[
+                "pandas", "numpy", "re", "json", "math", "datetime",
+                "collections", "itertools", "statistics", "requests", "bs4",
+            ],
+            max_steps=int(os.getenv("MAX_STEPS", "12")),
+            verbosity_level=1,
+        )
+
+    @staticmethod
+    def _clean(answer) -> str:
+        text = str(answer).strip()
+        text = re.sub(r"^\s*final answer\s*:\s*", "", text, flags=re.IGNORECASE)
+        return text.strip().rstrip(".").strip()
+
+    def __call__(self, question: str, file_path: str | None = None) -> str:
+        prompt = ANSWER_RULES + "\n\nQuestion:\n" + question
+        if file_path:
+            prompt += f"\n\nAn attached file is available at: {file_path}"
+        result = self.agent.run(prompt)
+        return self._clean(result)
+
+
+# ---------------------------------------------------------------------------
+# 채점 서버 통신 유틸
+# ---------------------------------------------------------------------------
+def fetch_questions(api_url: str) -> list[dict]:
+    resp = requests.get(f"{api_url}/questions", timeout=30)
+    resp.raise_for_status()
+    data = resp.json()
+    if not data:
+        raise ValueError("질문 목록이 비어 있습니다.")
+    return data
+
+
+def download_task_file(api_url: str, task_id: str, file_name: str) -> str | None:
+    """질문에 첨부 파일이 있으면 내려받아 경로를 반환합니다."""
+    if not file_name:
+        return None
     try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-             print("Fetched questions list is empty.")
-             return "Fetched questions list is empty or invalid format.", None
-        print(f"Fetched {len(questions_data)} questions.")
-    except requests.exceptions.RequestException as e:
-        print(f"Error fetching questions: {e}")
-        return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
-         print(f"Error decoding JSON response from questions endpoint: {e}")
-         print(f"Response text: {response.text[:500]}")
-         return f"Error decoding server response for questions: {e}", None
-    except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
-
-    # 3. Run your Agent
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
+        resp = requests.get(f"{api_url}/files/{task_id}", timeout=60)
+        resp.raise_for_status()
+        path = os.path.join(FILES_DIR, f"{task_id}_{file_name}")
+        with open(path, "wb") as f:
+            f.write(resp.content)
+        return path
+    except Exception as e:  # noqa: BLE001
+        print(f"파일 다운로드 실패 ({task_id}): {e}")
+        return None
+
+
+def run_questions(agent: BasicAgent, questions: list[dict], api_url: str):
+    answers_payload, results_log = [], []
+    for i, item in enumerate(questions, start=1):
         task_id = item.get("task_id")
         question_text = item.get("question")
         if not task_id or question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
             continue
+        print(f"\n===== [{i}/{len(questions)}] task_id={task_id} =====")
+        file_path = download_task_file(api_url, task_id, item.get("file_name", ""))
         try:
-            submitted_answer = agent(question_text)
-            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
-        except Exception as e:
-             print(f"Error running agent on task {task_id}: {e}")
-             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+            answer = agent(question_text, file_path)
+        except Exception as e:  # noqa: BLE001
+            print(f"에이전트 오류: {e}")
+            answer = f"AGENT ERROR: {e}"
+        answers_payload.append({"task_id": task_id, "submitted_answer": answer})
+        results_log.append(
+            {"Task ID": task_id, "Question": question_text, "Submitted Answer": answer}
+        )
+        time.sleep(SLEEP_BETWEEN_QUESTIONS)
+    return answers_payload, results_log
 
-    if not answers_payload:
-        print("Agent did not produce any answers to submit.")
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    # 4. Prepare Submission 
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
-    print(status_update)
+# ---------------------------------------------------------------------------
+# Gradio 콜백
+# ---------------------------------------------------------------------------
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    """전체 질문 실행 -> 제출 -> 점수 확인"""
+    space_id = os.getenv("SPACE_ID")
+    if not profile:
+        return "먼저 Hugging Face 로그인 버튼으로 로그인해 주세요.", None
+    username = profile.username
+    api_url = DEFAULT_API_URL
+
+    if not space_id:
+        return "SPACE_ID를 찾을 수 없습니다. Space 안에서 실행해야 합니다.", None
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+
+    try:
+        agent = BasicAgent()
+    except Exception as e:  # noqa: BLE001
+        return f"에이전트 초기화 오류: {e}", None
+
+    try:
+        questions = fetch_questions(api_url)
+    except Exception as e:  # noqa: BLE001
+        return f"질문 가져오기 오류: {e}", None
 
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    answers_payload, results_log = run_questions(agent, questions, api_url)
+    results_df = pd.DataFrame(results_log)
+    if not answers_payload:
+        return "제출할 답변이 없습니다.", results_df
+
+    submission = {
+        "username": username.strip(),
+        "agent_code": agent_code,
+        "answers": answers_payload,
+    }
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
+        resp = requests.post(f"{api_url}/submit", json=submission, timeout=120)
+        resp.raise_for_status()
+        r = resp.json()
+        status = (
+            "제출 성공!\n"
+            f"사용자: {r.get('username')}\n"
+            f"점수: {r.get('score', 'N/A')}% "
+            f"({r.get('correct_count', '?')}/{r.get('total_attempted', '?')} 정답)\n"
+            f"메시지: {r.get('message', '')}"
         )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
+        return status, results_df
     except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-
-
-# --- Build Gradio Interface using Blocks ---
+        detail = e.response.text[:500] if e.response is not None else str(e)
+        return f"제출 실패: {detail}", results_df
+    except Exception as e:  # noqa: BLE001
+        return f"제출 중 오류: {e}", results_df
+
+
+def run_test_only(n_questions: int):
+    """제출 없이 앞의 N개 질문만 실행해 보는 테스트 (점수에 영향 없음)"""
+    api_url = DEFAULT_API_URL
+    try:
+        agent = BasicAgent()
+        questions = fetch_questions(api_url)[: int(n_questions)]
+    except Exception as e:  # noqa: BLE001
+        return f"준비 중 오류: {e}", None
+    _, results_log = run_questions(agent, questions, api_url)
+    return f"테스트 완료: {len(results_log)}개 질문 실행 (제출하지 않음)", pd.DataFrame(results_log)
+
+
+# ---------------------------------------------------------------------------
+# UI
+# ---------------------------------------------------------------------------
 with gr.Blocks() as demo:
     gr.Markdown("# Basic Agent Evaluation Runner")
     gr.Markdown(
         """
-        **Instructions:**
-
-        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
-        ---
-        **Disclaimers:**
-        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
-        """
+**Instructions:**
+1. 이 Space를 복제(Duplicate)한 뒤 코드를 수정해 에이전트 로직을 정의합니다.
+2. 아래 버튼으로 Hugging Face에 로그인합니다. (HF 사용자명으로 제출됩니다)
+3. 먼저 '테스트 실행'으로 소수 문항을 확인하고, 문제없으면 'Run Evaluation & Submit All Answers'를 누릅니다.
+"""
     )
-
     gr.LoginButton()
 
+    with gr.Row():
+        test_n = gr.Number(value=2, label="테스트할 문항 수 (제출 안 함)", precision=0)
+        test_button = gr.Button("테스트 실행 (제출 안 함)")
     run_button = gr.Button("Run Evaluation & Submit All Answers")
 
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
+    status_output = gr.Textbox(label="Run Status / Submission Result", lines=6, interactive=False)
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
+    test_button.click(fn=run_test_only, inputs=[test_n], outputs=[status_output, results_table])
+    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
+
+@spaces.GPU
+def dummy():
+    return "ok"
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-    else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-
-    print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False)
+