Update app.py

app.py

@@ -1,196 +1,973 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
+import tempfile
+import json
+import subprocess
+import sys
+from pathlib import Path
+
+from smolagents import (
+    CodeAgent,
+    HfApiModel,
+    DuckDuckGoSearchTool,
+    tool,
+)
+
+# ============================================================
+# CONSTANTS
+# ============================================================
 
-# (Keep Constants as is)
-# --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+
+# ============================================================
+# CUSTOM TOOLS
+# ============================================================
+
+@tool
+def calculate(expression: str) -> str:
+    """
+    Calculate a mathematical expression.
+
+    Args:
+        expression: A Python mathematical expression such as
+                    "17 * 23" or "(100 / 4) + 7".
+
+    Returns:
+        The calculated result as a string.
+    """
+    try:
+        allowed = {
+            "abs": abs,
+            "round": round,
+            "min": min,
+            "max": max,
+            "sum": sum,
+        }
+
+        result = eval(
+            expression,
+            {"__builtins__": {}},
+            allowed
+        )
+
+        return str(result)
+
+    except Exception as e:
+        return f"Calculation error: {e}"
+
+
+@tool
+def analyze_csv_or_excel(file_path: str, operation: str) -> str:
+    """
+    Analyze a CSV or Excel file.
+
+    Args:
+        file_path: Path to the CSV or Excel file.
+        operation: Describe what should be calculated or inspected.
+
+    Returns:
+        A textual summary of the file and requested operation.
+    """
+
+    try:
+        path = Path(file_path)
+
+        if not path.exists():
+            return "File does not exist."
+
+        if path.suffix.lower() in [".xlsx", ".xls"]:
+            df = pd.read_excel(path)
+        elif path.suffix.lower() == ".csv":
+            df = pd.read_csv(path)
+        else:
+            return "Unsupported file type."
+
+        result = {
+            "columns": list(df.columns),
+            "rows": len(df),
+            "preview": df.head(10).to_dict(orient="records"),
+            "numeric_summary": df.describe(
+                include="all"
+            ).to_dict()
+        }
+
+        return json.dumps(result, default=str)
+
+    except Exception as e:
+        return f"Spreadsheet analysis error: {e}"
+
+
+@tool
+def read_text_file(file_path: str) -> str:
+    """
+    Read a text-based file.
+
+    Args:
+        file_path: Path to the text file.
+
+    Returns:
+        The contents of the file.
+    """
+
+    try:
+        path = Path(file_path)
+
+        if not path.exists():
+            return "File does not exist."
+
+        return path.read_text(
+            encoding="utf-8",
+            errors="ignore"
+        )
+
+    except Exception as e:
+        return f"Could not read file: {e}"
+
+
+@tool
+def execute_python_file(file_path: str) -> str:
+    """
+    Execute a Python file and return its output.
+
+    Args:
+        file_path: Path to the Python file.
+
+    Returns:
+        Standard output produced by the Python program.
+    """
+
+    try:
+        path = Path(file_path)
+
+        if not path.exists():
+            return "Python file does not exist."
+
+        result = subprocess.run(
+            [sys.executable, str(path)],
+            capture_output=True,
+            text=True,
+            timeout=30
+        )
+
+        output = result.stdout.strip()
+
+        if result.stderr:
+            output += "\nSTDERR:\n" + result.stderr.strip()
+
+        return output
+
+    except subprocess.TimeoutExpired:
+        return "Python execution timed out."
+
+    except Exception as e:
+        return f"Python execution error: {e}"
+
+
+# ============================================================
+# GAIA AGENT
+# ============================================================
+
 class BasicAgent:
+
     def __init__(self):
-        print("BasicAgent initialized.")
+
+        print("=" * 60)
+        print("Initializing GAIA Agent...")
+        print("=" * 60)
+
+        # ----------------------------------------------------
+        # Hugging Face model
+        # ----------------------------------------------------
+
+        self.model = HfApiModel(
+            model_id="Qwen/Qwen2.5-Coder-32B-Instruct"
+        )
+
+        # ----------------------------------------------------
+        # Web search
+        # ----------------------------------------------------
+
+        self.search_tool = DuckDuckGoSearchTool()
+
+        # ----------------------------------------------------
+        # Agent
+        # ----------------------------------------------------
+
+        self.agent = CodeAgent(
+
+            model=self.model,
+
+            tools=[
+                self.search_tool,
+                calculate,
+                analyze_csv_or_excel,
+                read_text_file,
+                execute_python_file,
+            ],
+
+            additional_authorized_imports=[
+                "requests",
+                "bs4",
+                "datetime",
+                "pandas",
+                "math",
+                "statistics",
+                "json",
+                "re",
+            ],
+
+            max_steps=15,
+
+            verbosity_level=1,
+        )
+
+        print("GAIA Agent initialized successfully.")
+
+
+    # --------------------------------------------------------
+    # Clean final answer
+    # --------------------------------------------------------
+
+    def clean_answer(self, answer):
+
+        if answer is None:
+            return "unknown"
+
+        answer = str(answer).strip()
+
+        # Remove common unwanted prefixes
+        prefixes = [
+            "FINAL ANSWER:",
+            "Final Answer:",
+            "FINAL ANSWER",
+            "Final answer:",
+            "Answer:",
+            "answer:",
+        ]
+
+        for prefix in prefixes:
+
+            if answer.startswith(prefix):
+                answer = answer[len(prefix):].strip()
+
+        # Remove markdown code fences
+        answer = answer.replace("```", "").strip()
+
+        # Remove accidental surrounding quotes
+        if (
+            len(answer) >= 2
+            and answer[0] == '"'
+            and answer[-1] == '"'
+        ):
+            answer = answer[1:-1].strip()
+
+        return answer
+
+
+    # --------------------------------------------------------
+    # Run Agent
+    # --------------------------------------------------------
+
     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" + "=" * 70)
+        print("QUESTION")
+        print(question)
+        print("=" * 70)
+
+        prompt = f"""
+You are a highly capable general-purpose AI agent solving a
+GAIA Level-1 benchmark question.
+
+Your goal is to obtain the CORRECT final answer.
+
+You have access to:
+
+1. Web search
+2. Python calculations
+3. Spreadsheet analysis
+4. Python file execution
+5. Text file reading
+
+GENERAL RULES:
+
+- Carefully understand the question.
+- Do not guess if you can verify the information.
+- Use web search for factual/current/research questions.
+- Use Python for calculations, sorting, counting and data processing.
+- Use spreadsheet tools for Excel/CSV questions.
+- Use the Python execution tool when a Python file is provided.
+- Perform multi-step reasoning when necessary.
+- Verify important results before answering.
+
+IMPORTANT OUTPUT RULE:
+
+The benchmark uses exact matching.
+
+Therefore your FINAL RESPONSE must contain ONLY the answer.
+
+Do NOT write:
+
+"FINAL ANSWER:"
+
+Do NOT write:
+
+"The answer is..."
+
+Do NOT explain your reasoning.
+
+Do NOT add unnecessary punctuation.
+
+Examples:
+
+If the answer is a number:
+
+391
+
+If the answer is a name:
+
+Smith
+
+If the question asks for a comma-separated list:
+
+apples, bananas, oranges
+
+Question:
+
+{question}
+"""
+
+        try:
+
+            result = self.agent.run(prompt)
+
+            answer = self.clean_answer(result)
+
+            print("\nAGENT ANSWER:")
+            print(answer)
+
+            return answer
+
+        except Exception as e:
+
+            print("\nAGENT ERROR:")
+            print(e)
+
+            return "unknown"
+
+
+# ============================================================
+# DOWNLOAD ATTACHED GAIA FILE
+# ============================================================
+
+def download_task_file(task_id):
+
+    file_url = f"{DEFAULT_API_URL}/files/{task_id}"
+
+    try:
+
+        response = requests.get(
+            file_url,
+            timeout=30
+        )
+
+        if response.status_code != 200:
+            return None
+
+        # Try to determine extension from headers
+        content_type = response.headers.get(
+            "content-type",
+            ""
+        ).lower()
+
+        extension = ".bin"
+
+        if "png" in content_type:
+            extension = ".png"
+
+        elif "jpeg" in content_type:
+            extension = ".jpg"
+
+        elif "audio" in content_type:
+            extension = ".mp3"
+
+        elif "excel" in content_type:
+            extension = ".xlsx"
+
+        elif "csv" in content_type:
+            extension = ".csv"
+
+        elif "python" in content_type:
+            extension = ".py"
+
+        # Temporary file
+        temp = tempfile.NamedTemporaryFile(
+            delete=False,
+            suffix=extension
+        )
+
+        temp.write(response.content)
+        temp.close()
+
+        print(
+            f"Downloaded task file: {temp.name}"
+        )
+
+        return temp.name
+
+    except Exception as e:
+
+        print(
+            f"Could not download task file: {e}"
+        )
+
+        return None
+
+
+# ============================================================
+# MAIN EVALUATION FUNCTION
+# ============================================================
+
+def run_and_submit_all(
+    profile: gr.OAuthProfile | None
+):
+
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
+    Fetch all GAIA questions,
+    run the Agent,
+    and submit answers.
     """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+
+    # --------------------------------------------------------
+    # Get username
+    # --------------------------------------------------------
+
+    space_id = os.getenv("SPACE_ID")
 
     if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
+
+        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
+
+        return (
+            "Please Login to Hugging Face with the button.",
+            None
+        )
+
+
+    # --------------------------------------------------------
+    # API endpoints
+    # --------------------------------------------------------
 
     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)
+
+    # --------------------------------------------------------
+    # 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
+        )
+
+
+    # --------------------------------------------------------
+    # Code link
+    # --------------------------------------------------------
+
+    agent_code = (
+        f"https://huggingface.co/spaces/"
+        f"{space_id}/tree/main"
+    )
+
+    print(
+        f"Agent code: {agent_code}"
+    )
+
+
+    # --------------------------------------------------------
+    # Fetch questions
+    # --------------------------------------------------------
+
+    print(
+        f"Fetching questions from: {questions_url}"
+    )
+
     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.")
-    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
+
+            return (
+                "Question list is empty.",
+                None
+            )
+
+        print(
+            f"Fetched {len(questions_data)} questions."
+        )
+
     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"Error fetching questions: {e}"
+        )
+
+        return (
+            f"Error fetching questions: {e}",
+            None
+        )
+
+
+    # --------------------------------------------------------
+    # Run Agent
+    # --------------------------------------------------------
+
     results_log = []
+
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
+
+    print(
+        f"\nRunning Agent on "
+        f"{len(questions_data)} questions..."
+    )
+
+
+    for index, item in enumerate(
+        questions_data,
+        start=1
+    ):
+
         task_id = item.get("task_id")
-        question_text = item.get("question")
+
+        question_text = item.get(
+            "question"
+        )
+
+        file_name = item.get(
+            "file_name",
+            ""
+        )
+
+
         if not task_id or question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
+
+            print(
+                "Skipping invalid question."
+            )
+
             continue
+
+
+        print("\n")
+        print("#" * 80)
+        print(
+            f"QUESTION {index}/{len(questions_data)}"
+        )
+        print(
+            f"TASK ID: {task_id}"
+        )
+
+        if file_name:
+
+            print(
+                f"ATTACHED FILE: {file_name}"
+            )
+
+
+        # ----------------------------------------------------
+        # Handle attached file
+        # ----------------------------------------------------
+
+        enhanced_question = question_text
+
+        if file_name:
+
+            file_path = download_task_file(
+                task_id
+            )
+
+            if file_path:
+
+                enhanced_question += f"""
+
+IMPORTANT:
+
+This GAIA question has an attached file.
+
+Original file name:
+{file_name}
+
+The file has been downloaded locally to:
+
+{file_path}
+
+You MUST use the appropriate tool to inspect this file
+when the question requires information from it.
+
+For an Excel/CSV file:
+use analyze_csv_or_excel.
+
+For a Python file:
+use execute_python_file.
+
+For a text file:
+use read_text_file.
+
+Do not ignore the attached file.
+"""
+
+            else:
+
+                enhanced_question += f"""
+
+IMPORTANT:
+
+The question references this attached file:
+
+{file_name}
+
+The file could not be downloaded automatically.
+Try to solve the question using the information available
+in the question or other available tools.
+"""
+
+
+        # ----------------------------------------------------
+        # 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(
+                enhanced_question
+            )
+
+            submitted_answer = str(
+                submitted_answer
+            ).strip()
+
+            answers_payload.append(
+                {
+                    "task_id": task_id,
+                    "submitted_answer": submitted_answer
+                }
+            )
+
+            results_log.append(
+                {
+                    "Task ID": task_id,
+                    "Question": question_text,
+                    "File": file_name,
+                    "Submitted Answer": submitted_answer
+                }
+            )
+
+            print(
+                f"FINAL 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}"})
+
+            print(
+                f"Agent failed on task "
+                f"{task_id}: {e}"
+            )
+
+            results_log.append(
+                {
+                    "Task ID": task_id,
+                    "Question": question_text,
+                    "File": file_name,
+                    "Submitted Answer":
+                        f"AGENT ERROR: {e}"
+                }
+            )
+
+
+    # --------------------------------------------------------
+    # 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}'..."
+        return (
+            "Agent did not produce any answers.",
+            pd.DataFrame(results_log)
+        )
+
+
+    # --------------------------------------------------------
+    # 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)} "
+        f"answers for user '{username}'..."
+    )
+
     print(status_update)
 
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+
+    # --------------------------------------------------------
+    # Submit
+    # --------------------------------------------------------
+
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
+
+        response = requests.post(
+
+            submit_url,
+
+            json=submission_data,
+
+            timeout=180
+        )
+
         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.')}"
-        )
-        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')}\n"
+
+            f"Overall Score: "
+            f"{result_data.get('score', 'N/A')}%\n"
+
+            f"Correct: "
+            f"{result_data.get('correct_count', '?')}/"
+            f"{result_data.get('total_attempted', '?')}\n\n"
+
+            f"Message: "
+            f"{result_data.get('message', '')}"
+        )
+
+
+        print(final_status)
+
+
+        results_df = pd.DataFrame(
+            results_log
+        )
+
+
+        return (
+            final_status,
+            results_df
+        )
+
+
     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}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+
+            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}"
+        )
+
+
+        return (
+            status_message,
+            pd.DataFrame(results_log)
+        )
+
+
     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
+
+        status_message = (
+            "Submission Failed: "
+            "The request timed out."
+        )
+
+        return (
+            status_message,
+            pd.DataFrame(results_log)
+        )
+
+
     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
+
+        status_message = (
+            f"Submission Failed: "
+            f"Network error - {e}"
+        )
+
+        return (
+            status_message,
+            pd.DataFrame(results_log)
+        )
+
+
     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
 
+        status_message = (
+            f"Unexpected submission error: {e}"
+        )
+
+        return (
+            status_message,
+            pd.DataFrame(results_log)
+        )
+
+
+# ============================================================
+# GRADIO INTERFACE
+# ============================================================
 
-# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+
+    gr.Markdown(
+        "# 🤖 GAIA Agent Evaluation Runner"
+    )
+
     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. Your Agent will receive the GAIA evaluation questions.
+        3. Your Agent will use tools to solve them.
+        4. The answers will automatically be submitted.
+        5. Your score will be displayed 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 benchmark uses exact matching,
+        so the Agent should return only the final answer.
         """
     )
 
     gr.LoginButton()
 
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
+    run_button = gr.Button(
+        "🚀 Run Evaluation & Submit All Answers"
+    )
+
+    status_output = gr.Textbox(
+        label="Run Status / Submission Result",
+        lines=7,
+        interactive=False
+    )
+
+    results_table = gr.DataFrame(
+        label="Questions and Agent Answers",
+        wrap=True
+    )
 
-    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(
+
         fn=run_and_submit_all,
-        outputs=[status_output, results_table]
+
+        outputs=[
+            status_output,
+            results_table
+        ]
     )
 
+
+# ============================================================
+# START
+# ============================================================
+
 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(
+        "\n" +
+        "=" * 60 +
+        "\n GAIA AGENT STARTING \n" +
+        "=" * 60
+    )
 
-    print("-"*(60 + len(" App Starting ")) + "\n")
+    space_id = os.getenv(
+        "SPACE_ID"
+    )
+
+    if space_id:
+
+        print(
+            f"Space ID: {space_id}"
+        )
+
+        print(
+            "Code URL: "
+            f"https://huggingface.co/spaces/"
+            f"{space_id}/tree/main"
+        )
 
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(
+        debug=True,
+        share=False
+    )