Update app.py

app.py

@@ -1,196 +1,1015 @@
+# ============================================================
+# HUGGING FACE AGENTS COURSE - UNIT 4
+# GENERAL PURPOSE GAIA AGENT
+# ============================================================
+
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
 
-# (Keep Constants as is)
-# --- Constants ---
+from smolagents import (
+    CodeAgent,
+    InferenceClientModel,
+    DuckDuckGoSearchTool,
+    VisitWebpageTool,
+    WikipediaSearchTool,
+    PythonInterpreterTool,
+)
+
+
+# ============================================================
+# CONSTANTS
+# ============================================================
+
+# IMPORTANT:
+# DO NOT CHANGE THIS URL.
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+
+# ============================================================
+# GENERAL PURPOSE GAIA AGENT
+# ============================================================
+
 class BasicAgent:
+
     def __init__(self):
-        print("BasicAgent initialized.")
+
+        print("=" * 60)
+        print("Initializing General Purpose GAIA Agent...")
+        print("=" * 60)
+
+        # ----------------------------------------------------
+        # Hugging Face token
+        # ----------------------------------------------------
+
+        hf_token = os.getenv("HF_TOKEN")
+
+        if not hf_token:
+
+            raise RuntimeError(
+                "HF_TOKEN is missing.\n"
+                "Go to Space Settings -> Secrets and create:\n"
+                "Name: HF_TOKEN\n"
+                "Value: Your Hugging Face token"
+            )
+
+        # ----------------------------------------------------
+        # MODEL
+        # ----------------------------------------------------
+
+        self.model = InferenceClientModel(
+            model_id="Qwen/Qwen3-Next-80B-A3B-Thinking",
+            token=hf_token,
+            temperature=0.1,
+            max_tokens=3000,
+        )
+
+        # ----------------------------------------------------
+        # WEB SEARCH TOOL
+        # ----------------------------------------------------
+
+        self.search_tool = DuckDuckGoSearchTool(
+            max_results=8,
+            rate_limit=1.0
+        )
+
+        # ----------------------------------------------------
+        # WEBPAGE TOOL
+        # ----------------------------------------------------
+
+        self.webpage_tool = VisitWebpageTool(
+            max_output_length=40000
+        )
+
+        # ----------------------------------------------------
+        # WIKIPEDIA TOOL
+        # ----------------------------------------------------
+
+        self.wikipedia_tool = WikipediaSearchTool(
+            user_agent="StructuralGPT-GAIA-Agent/1.0",
+            language="en",
+            content_type="text",
+            extract_format="WIKI"
+        )
+
+        # ----------------------------------------------------
+        # PYTHON / CALCULATION TOOL
+        # ----------------------------------------------------
+
+        self.python_tool = PythonInterpreterTool(
+            authorized_imports=[
+                "math",
+                "statistics",
+                "datetime",
+                "json",
+                "re",
+                "decimal"
+            ],
+            timeout_seconds=30
+        )
+
+        # ----------------------------------------------------
+        # SYSTEM INSTRUCTIONS
+        # ----------------------------------------------------
+
+        self.instructions = """
+You are a highly capable general-purpose AI agent designed
+to solve GAIA benchmark questions.
+
+Your objective is to provide the CORRECT answer to the
+user's question.
+
+IMPORTANT RULES
+===============
+
+1. READ THE COMPLETE QUESTION CAREFULLY.
+
+2. Determine exactly what the question is asking.
+
+3. Use tools whenever they are useful.
+
+4. Use web search for:
+   - current information
+   - factual information
+   - obscure information
+   - information that should be verified
+   - people, companies, events, dates, statistics, etc.
+
+5. Use the webpage tool when the question provides a URL
+   or when you need the contents of a webpage.
+
+6. Use Wikipedia search when the question specifically
+   refers to Wikipedia or historical/general information.
+
+7. Use Python for:
+   - arithmetic
+   - percentages
+   - dates
+   - counting
+   - comparisons
+   - numerical reasoning
+   - data processing
+
+8. NEVER guess a numerical answer when you can calculate it.
+
+9. When a question contains several steps:
+   solve every required step before giving the answer.
+
+10. Verify important facts whenever possible.
+
+11. Do not invent information.
+
+12. Pay close attention to:
+   - exact dates
+   - names
+   - numbers
+   - units
+   - percentages
+   - spelling
+   - requested formats
+
+13. If the question asks:
+   "How many?"
+   return the number.
+
+14. If it asks:
+   "Who?"
+   return the person's name.
+
+15. If it asks:
+   "When?"
+   return the date/year.
+
+16. If it asks for a calculation,
+   calculate it using Python.
+
+17. If the question asks for a specific list,
+   provide exactly the requested list.
+
+18. If the question contains a URL,
+   investigate the URL rather than guessing.
+
+19. Do not include citations unless the question asks for them.
+
+20. Do not say "FINAL ANSWER".
+
+21. Do not write:
+   "Here is the answer:"
+   "The answer is:"
+   or unnecessary explanations.
+
+22. The final response must contain ONLY the answer
+   required by the question.
+
+23. The GAIA evaluator uses exact matching, so be precise
+   and concise.
+
+24. Think through the problem carefully before producing
+   the final response.
+
+25. NEVER intentionally return a generic response.
+
+SPECIAL CASES
+=============
+
+For calculations:
+- Use Python.
+- Verify the result.
+- Give the exact required number.
+
+For web research:
+- Search using precise keywords.
+- Open useful results.
+- Compare information when needed.
+
+For Wikipedia questions:
+- Use Wikipedia search.
+- Pay attention to the requested Wikipedia version/date.
+
+For questions involving a webpage:
+- Visit the webpage.
+- Extract the relevant information.
+- Answer only what is requested.
+
+For civil/structural engineering questions:
+- You may use engineering knowledge.
+- Use Indian Standards when appropriate.
+- Keep the final answer focused on what was requested.
+
+FINAL RESPONSE
+==============
+
+Return ONLY the concise answer.
+"""
+
+
+        # ----------------------------------------------------
+        # CREATE AGENT
+        # ----------------------------------------------------
+
+        self.agent = CodeAgent(
+            model=self.model,
+
+            tools=[
+                self.search_tool,
+                self.webpage_tool,
+                self.wikipedia_tool,
+                self.python_tool,
+            ],
+
+            max_steps=12,
+
+            instructions=self.instructions,
+
+            add_base_tools=True
+        )
+
+        print("GAIA Agent initialized successfully.")
+        print("=" * 60)
+
+
+    # ========================================================
+    # AGENT CALL
+    # ========================================================
+
     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):
+        print("\n")
+        print("=" * 70)
+        print("NEW GAIA QUESTION")
+        print("=" * 70)
+        print(question)
+        print("=" * 70)
+
+        try:
+
+            result = self.agent.run(question)
+
+            answer = str(result).strip()
+
+            # ------------------------------------------------
+            # Remove accidental answer labels
+            # ------------------------------------------------
+
+            unwanted_prefixes = [
+                "FINAL ANSWER:",
+                "FINAL ANSWER",
+                "Answer:",
+                "ANSWER:",
+                "The answer is:",
+                "The answer is"
+            ]
+
+            for prefix in unwanted_prefixes:
+
+                if answer.lower().startswith(prefix.lower()):
+
+                    answer = answer[len(prefix):].strip()
+
+            print("\nAGENT ANSWER:")
+            print(answer)
+            print("=" * 70)
+
+            return answer
+
+        except Exception as e:
+
+            print("\nAGENT ERROR:")
+            print(str(e))
+            print("=" * 70)
+
+            return f"Agent execution error: {e}"
+
+
+# ============================================================
+# RUN AND SUBMIT ALL QUESTIONS
+# ============================================================
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    Fetches all GAIA questions,
+    runs the General Purpose Agent,
+    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
+
+    # --------------------------------------------------------
+    # 1. CHECK HUGGING FACE LOGIN
+    # --------------------------------------------------------
 
     if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
+
+        username = profile.username
+
+        print(
+            f"User logged in: {username}"
+        )
+
     else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
+
+        print("User is not logged in.")
+
+        return (
+            "Please Login to Hugging Face with the button.",
+            None
+        )
+
+
+    # --------------------------------------------------------
+    # 2. SCORING API URLS
+    # --------------------------------------------------------
 
     api_url = DEFAULT_API_URL
+
     questions_url = f"{api_url}/questions"
+
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
+
+    # --------------------------------------------------------
+    # 3. GET SPACE CODE URL
+    # --------------------------------------------------------
+
+    space_id = os.getenv("SPACE_ID")
+
+    if not space_id:
+
+        print(
+            "WARNING: SPACE_ID was not found."
+        )
+
+        agent_code = "SPACE_ID_NOT_FOUND"
+
+    else:
+
+        agent_code = (
+            f"https://huggingface.co/spaces/"
+            f"{space_id}/tree/main"
+        )
+
+    print(
+        f"Agent code URL: {agent_code}"
+    )
+
+
+    # --------------------------------------------------------
+    # 4. CREATE 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}")
+
+        print(
+            f"Error initializing agent: {e}"
+        )
+
+        return (
+            f"Error initializing agent: {e}",
+            None
+        )
+
+
+    # --------------------------------------------------------
+    # 5. FETCH QUESTIONS
+    # --------------------------------------------------------
+
+    print("\n")
+    print("=" * 70)
+    print("FETCHING GAIA QUESTIONS")
+    print("=" * 70)
+
     try:
-        response = requests.get(questions_url, timeout=15)
+
+        response = requests.get(
+            questions_url,
+            timeout=30
+        )
+
         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.")
+
+            print("Questions list is empty.")
+
+            return (
+                "Fetched questions list is empty.",
+                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
+
+        print(
+            f"Error fetching questions: {e}"
+        )
+
+        return (
+            f"Error fetching questions: {e}",
+            None
+        )
+
+    except ValueError as e:
+
+        print(
+            f"Invalid JSON response: {e}"
+        )
+
+        return (
+            f"Invalid JSON response: {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
+        print(
+            f"Unexpected error fetching questions: {e}"
+        )
+
+        return (
+            f"Unexpected error fetching questions: {e}",
+            None
+        )
+
+
+    # --------------------------------------------------------
+    # 6. RUN AGENT ON EVERY QUESTION
+    # --------------------------------------------------------
+
     results_log = []
+
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
-        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}")
+
+    total_questions = len(questions_data)
+
+    print("\n")
+    print("=" * 70)
+    print("RUNNING GENERAL PURPOSE AGENT")
+    print("=" * 70)
+
+
+    for number, item in enumerate(
+        questions_data,
+        start=1
+    ):
+
+        task_id = item.get(
+            "task_id"
+        )
+
+        question_text = item.get(
+            "question"
+        )
+
+        # ----------------------------------------------------
+        # Validate question
+        # ----------------------------------------------------
+
+        if not task_id:
+
+            print(
+                f"Skipping question {number}: "
+                "missing task_id."
+            )
+
+            continue
+
+        if question_text is None:
+
+            print(
+                f"Skipping question {number}: "
+                "missing question text."
+            )
+
             continue
+
+
+        print("\n")
+        print(
+            f"QUESTION {number}/{total_questions}"
+        )
+        print(
+            f"TASK ID: {task_id}"
+        )
+
+
+        # ----------------------------------------------------
+        # Run agent
+        # ----------------------------------------------------
+
         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})
+
+            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}"})
+
+            error_message = (
+                f"AGENT ERROR: {e}"
+            )
+
+            print(error_message)
+
+            results_log.append(
+                {
+                    "Task ID": task_id,
+                    "Question": question_text,
+                    "Submitted Answer":
+                        error_message
+                }
+            )
+
+
+    # --------------------------------------------------------
+    # 7. CHECK ANSWERS
+    # --------------------------------------------------------
 
     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(
+            "Agent did not produce any answers."
+        )
+
+        return (
+            "Agent did not produce any answers.",
+            pd.DataFrame(results_log)
+        )
+
+
+    print("\n")
+    print("=" * 70)
+    print(
+        f"Agent produced {len(answers_payload)} "
+        f"answers."
+    )
+    print("=" * 70)
+
+
+    # --------------------------------------------------------
+    # 8. PREPARE SUBMISSION
+    # --------------------------------------------------------
+
+    submission_data = {
+
+        "username":
+            username.strip(),
+
+        "agent_code":
+            agent_code,
+
+        "answers":
+            answers_payload
+    }
+
+
+    status_update = (
+        f"Agent finished.\n"
+        f"Submitting {len(answers_payload)} answers "
+        f"for user '{username}'..."
+    )
+
     print(status_update)
 
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+
+    # --------------------------------------------------------
+    # 9. SUBMIT ANSWERS
+    # --------------------------------------------------------
+
+    print("\n")
+    print("=" * 70)
+    print("SUBMITTING TO GAIA SCORING SERVER")
+    print("=" * 70)
+
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
+
+        response = requests.post(
+            submit_url,
+            json=submission_data,
+            timeout=120
+        )
+
         response.raise_for_status()
+
         result_data = response.json()
+
+
+        # ----------------------------------------------------
+        # SCORE
+        # ----------------------------------------------------
+
+        score = result_data.get(
+            "score",
+            "N/A"
+        )
+
+        correct_count = result_data.get(
+            "correct_count",
+            "?"
+        )
+
+        total_attempted = result_data.get(
+            "total_attempted",
+            "?"
+        )
+
+        message = result_data.get(
+            "message",
+            "No message received."
+        )
+
+
         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.')}"
-        )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
+
+            "Submission Successful!\n\n"
+
+            f"User: "
+            f"{result_data.get('username', username)}\n"
+
+            f"Overall Score: "
+            f"{score}%\n"
+
+            f"Correct: "
+            f"{correct_count}/"
+            f"{total_attempted}\n\n"
+
+            f"Message: "
+            f"{message}"
+        )
+
+
+        print(final_status)
+
+
+        results_df = pd.DataFrame(
+            results_log
+        )
+
+
+        return (
+            final_status,
+            results_df
+        )
+
+
+    # --------------------------------------------------------
+    # HTTP ERROR
+    # --------------------------------------------------------
+
     except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
+
+        error_detail = (
+            f"Server responded with "
+            f"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}"
+
+            error_detail += (
+                f" Detail: "
+                f"{error_json.get('detail', '')}"
+            )
+
+        except Exception:
+
+            error_detail += (
+                f" Response: "
+                f"{e.response.text[:500]}"
+            )
+
+
+        status_message = (
+            f"Submission Failed: "
+            f"{error_detail}"
+        )
+
         print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+
+
+        return (
+            status_message,
+            pd.DataFrame(results_log)
+        )
+
+
+    # --------------------------------------------------------
+    # TIMEOUT
+    # --------------------------------------------------------
+
     except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
+
+        status_message = (
+            "Submission Failed: "
+            "The request timed out."
+        )
+
         print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+
+
+        return (
+            status_message,
+            pd.DataFrame(results_log)
+        )
+
+
+    # --------------------------------------------------------
+    # NETWORK ERROR
+    # --------------------------------------------------------
+
     except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
+
+        status_message = (
+            f"Submission Failed: "
+            f"Network error - {e}"
+        )
+
         print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+
+
+        return (
+            status_message,
+            pd.DataFrame(results_log)
+        )
+
+
+    # --------------------------------------------------------
+    # OTHER ERROR
+    # --------------------------------------------------------
+
     except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
+
+        status_message = (
+            f"Unexpected error during submission: "
+            f"{e}"
+        )
+
         print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
 
 
-# --- Build Gradio Interface using Blocks ---
+        return (
+            status_message,
+            pd.DataFrame(results_log)
+        )
+
+
+# ============================================================
+# GRADIO INTERFACE
+# ============================================================
+
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+
+    gr.Markdown(
+        """
+        # 🤖 General Purpose GAIA Agent
+
+        This agent uses:
+
+        - Qwen through Hugging Face
+        - Web Search
+        - Webpage retrieval
+        - Wikipedia
+        - Python calculations
+        - Multi-step reasoning
+
+        It is designed for the Hugging Face Agents Course
+        Unit 4 GAIA evaluation.
+        """
+    )
+
+
     gr.Markdown(
         """
-        **Instructions:**
+        ### 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.
+        1. Log in to your Hugging Face account.
+        2. Make sure `HF_TOKEN` is configured as a Space Secret.
+        3. Click **Run Evaluation & Submit All Answers**.
+        4. Wait while the agent processes all questions.
+        5. Your score will appear below.
 
-        ---
-        **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.
+        **Important:** The evaluation can take several minutes
+        because the agent processes each question individually.
         """
     )
 
+
+    # --------------------------------------------------------
+    # LOGIN
+    # --------------------------------------------------------
+
     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
+    # --------------------------------------------------------
+
+    run_button = gr.Button(
+        "Run Evaluation & Submit All Answers",
+        variant="primary"
+    )
+
+
+    # --------------------------------------------------------
+    # STATUS
+    # --------------------------------------------------------
+
+    status_output = gr.Textbox(
+        label="Run Status / Submission Result",
+        lines=8,
+        interactive=False
+    )
+
+
+    # --------------------------------------------------------
+    # RESULTS
+    # --------------------------------------------------------
+
+    results_table = gr.DataFrame(
+        label="Questions and Agent Answers",
+        wrap=True
+    )
+
+
+    # --------------------------------------------------------
+    # BUTTON ACTION
+    # --------------------------------------------------------
 
     run_button.click(
         fn=run_and_submit_all,
-        outputs=[status_output, results_table]
+        outputs=[
+            status_output,
+            results_table
+        ]
     )
 
+
+# ============================================================
+# START APPLICATION
+# ============================================================
+
 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
+
+    print(
+        "\n" +
+        "-" * 30 +
+        " App Starting " +
+        "-" * 30
+    )
+
+
+    # --------------------------------------------------------
+    # SPACE INFORMATION
+    # --------------------------------------------------------
+
+    space_host_startup = os.getenv(
+        "SPACE_HOST"
+    )
+
+    space_id_startup = os.getenv(
+        "SPACE_ID"
+    )
+
+
+    # --------------------------------------------------------
+    # SPACE HOST
+    # --------------------------------------------------------
 
     if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
+
+        print(
+            f"SPACE_HOST found: "
+            f"{space_host_startup}"
+        )
+
+        print(
+            "Runtime URL:"
+        )
+
+        print(
+            f"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")
+        print(
+            "SPACE_HOST not found."
+        )
+
+
+    # --------------------------------------------------------
+    # SPACE ID
+    # --------------------------------------------------------
+
+    if space_id_startup:
+
+        print(
+            f"SPACE_ID found: "
+            f"{space_id_startup}"
+        )
+
+        print(
+            "Repository URL:"
+        )
+
+        print(
+            f"https://huggingface.co/spaces/"
+            f"{space_id_startup}"
+        )
+
+        print(
+            "Code URL:"
+        )
+
+        print(
+            f"https://huggingface.co/spaces/"
+            f"{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(
+            "SPACE_ID not found."
+        )
+
+
+    print(
+        "\nLaunching General Purpose GAIA Agent..."
+    )
+
+
+    # --------------------------------------------------------
+    # LAUNCH
+    # --------------------------------------------------------
 
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(
+        debug=True,
+        share=False
+    )