update codes

app.py

@@ -3,54 +3,130 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+import time
+import re
+from bs4 import BeautifulSoup
 
-import random
-from smolagents import CodeAgent, InferenceClientModel, TransformersModel, OpenAIModel
-from smolagents import DuckDuckGoSearchTool
+from smolagents import CodeAgent, InferenceClientModel, Tool
 
-
-# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
+# --- High-Performance Tool 1: 다차원 구조화 웹 검색 툴 ---
+class AdvancedSearchTool(Tool):
+    name = "web_search"
+    description = "Executes a deep web search via DuckDuckGo HTML architecture and extracts exact URLs and targeted meta-snippets."
+    inputs = {"query": {"type": "string", "description": "The precise keyword query to search for"}}
+    output_type = "string"
+
+    def forward(self, query: str) -> str:
+        try:
+            url = f"https://html.duckduckgo.com/html/?q={requests.utils.quote(query)}"
+            headers = {"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"}
+            response = requests.get(url, headers=headers, timeout=12)
+            if response.status_code != 200:
+                return f"Search Gateway Error: HTTP {response.status_code}"
+            
+            soup = BeautifulSoup(response.text, "lxml")
+            results = []
+            for i, item in enumerate(soup.select(".result__body")[:5]):
+                title_anchor = item.select_one(".result__title a")
+                snippet_div = item.select_one(".result__snippet")
+                if title_anchor and snippet_div:
+                    title = title_anchor.get_text(strip=True)
+                    link = title_anchor.get("href")
+                    # 내부 리다이렉트 URL 정제
+                    if "uddg=" in link:
+                        link = requests.utils.unquote(link.split("uddg=")[1].split("&")[0])
+                    snippet = snippet_div.get_text(strip=True)
+                    results.append(f"[{i+1}] Title: {title}\nURL: {link}\nContext: {snippet}")
+            return "\n\n".join(results) if results else "No indexing data found."
+        except Exception as e:
+            return f"Search Engine Exception: {str(e)}"
+
+# --- High-Performance Tool 2: 마크다운 변환형 웹 및 도큐먼트 파서 툴 ---
+class DeepPageVisitTool(Tool):
+    name = "visit_webpage"
+    description = "Visits a specific URL, bypasses layout boilerplate, and converts raw HTML into a dense Markdown/Table format for complex data analysis."
+    inputs = {"url": {"type": "string", "description": "The target exact URL to scrape content from"}}
+    output_type = "string"
+
+    def forward(self, url: str) -> str:
+        try:
+            headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36"}
+            response = requests.get(url, headers=headers, timeout=15)
+            if response.status_code != 200:
+                return f"HTTP Access Failure: Status {response.status_code}"
+            
+            soup = BeautifulSoup(response.text, "lxml")
+            # 노이즈 태그 전량 제거
+            for element in soup(["script", "style", "nav", "footer", "header", "aside"]):
+                element.extract()
+                
+            # GAIA 핵심 지표인 '표 데이터' 보존 처리
+            for table in soup.find_all("table"):
+                markdown_table = []
+                for row in table.find_all("tr"):
+                    cells = [f" {cell.get_text(strip=True)} " for cell in row.find_all(["td", "th"])]
+                    markdown_table.append("|" + "|".join(cells) + "|")
+                if markdown_table:
+                    table.replace_with(soup.new_string("\n" + "\n".join(markdown_table) + "\n"))
+                    
+            text = soup.get_text(separator="\n")
+            text = re.sub(r'\n+', '\n', text).strip()
+            return text[:6000] # 컨텍스트 상한치 확보
+        except Exception as e:
+            return f"Page Scraping Exception: {str(e)}"
+
 # --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
-        # Initialize the Hugging Face model
-        # hf_token = os.getenv("HF_TOKEN")
-
-        # model = InferenceClientModel(
-        #     token=hf_token
-        # )
-
-        model = OpenAIModel(
-            model_id="deepseek-chat",
-            api_base="https://api.deepseek.com",
-            api_key=os.getenv("DEEPSEEK_API_KEY"),
+        self.model = InferenceClientModel(
+            model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
+            token=os.getenv("HF_TOKEN")
         )
+        self.search_tool = AdvancedSearchTool()
+        self.visit_tool = DeepPageVisitTool()
         
-        # Initialize the web search tool
-        search_tool = DuckDuckGoSearchTool()
-        
-        # Create Alfred with all the tools
-        self.alfred = CodeAgent(
-            tools=[search_tool], 
-            model=model,
-            add_base_tools=True,  # Add any additional base tools
-            planning_interval=3   # Enable planning every 3 steps
+        # 가드레일 강화를 위해 verbosity_level을 높이고 복합 연산 지시 프롬프트 템플릿 조정
+        self.agent = CodeAgent(
+            tools=[self.search_tool, self.visit_tool],
+            model=self.model,
+            max_steps=12,
+            verbosity_level=2
         )
+        print("BasicAgent: Guardrail & Self-Correction Engine Loaded.")
 
     def __call__(self, question: str) -> str:
-        return self.alfred.run(question)
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        try:
+            # 에이전트가 단발성 판단을 내리지 않고 명확한 실행 계획(Execution Plan)을 세우도록 강제하는 엔지니어링 프롬프트
+            structured_prompt = (
+                f"You are an expert AI agent solving a GAIA task.\n"
+                f"Task: {question}\n\n"
+                f"Strict Protocol:\n"
+                f"1. Plan: Break down the research and computation into clear sub-tasks.\n"
+                f"2. Action: Use your code interpreter or tools to gather and verify facts.\n"
+                f"3. Self-Correction: If any code execution fails with a Traceback, analyze the error, rewrite the script, and run it again.\n"
+                f"4. Output: Extract the absolute raw answer value (e.g., specific number, name, date) without any markdown formatting wrappers or conversational text. Present this on the very last line."
+            )
+            
+            result = self.agent.run(structured_prompt)
+            if result is None:
+                return "unknown"
+                
+            # 정답 유실 방지를 위한 최종 타겟 파싱 가드레일 처리
+            final_output = str(result).strip()
+            if "\n" in final_output:
+                final_output = final_output.split("\n")[-1].replace("Final Answer:", "").strip()
+            return final_output
+            
+        except Exception as e:
+            print(f"Critical System Failure during agent execution: {e}")
+            return "unknown"
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
-    """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+    space_id = os.getenv("SPACE_ID")
 
     if profile:
         username= f"{profile.username}"
@@ -63,17 +139,14 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 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}")
     try:
         response = requests.get(questions_url, timeout=15)
@@ -94,7 +167,6 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         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...")
@@ -116,12 +188,10 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         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)
 
-    # 5. Submit
     print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
         response = requests.post(submit_url, json=submission_data, timeout=60)
@@ -164,29 +234,20 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         results_df = pd.DataFrame(results_log)
         return status_message, results_df
 
-
-# --- Build Gradio Interface using Blocks ---
 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.
+        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.
         """
     )
 
     gr.LoginButton()
-
     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
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
     run_button.click(
@@ -195,25 +256,4 @@ with gr.Blocks() as demo:
     )
 
 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)