update

app.py

@@ -1,196 +1,1263 @@
 import os
+import tempfile
+from pathlib import Path
+
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
 
-# (Keep Constants as is)
-# --- Constants ---
+
+# ============================================================
+# CONFIGURATION
+# ============================================================
+
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- 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):
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# Model used by the agent.
+# Hugging Face's current Agents Course examples use
+# InferenceClientModel with CodeAgent.
+MODEL_ID = os.getenv(
+    "MODEL_ID",
+    "Qwen/Qwen2.5-Coder-32B-Instruct"
+)
+
+
+# ============================================================
+# SMOLAGENTS IMPORTS
+# ============================================================
+
+from smolagents import (
+    CodeAgent,
+    InferenceClientModel,
+    DuckDuckGoSearchTool,
+    PythonInterpreterTool,
+    VisitWebpageTool,
+    SpeechToTextTool,
+    Tool,
+)
+
+
+# ============================================================
+# CUSTOM TOOL: READ LOCAL FILE
+# ============================================================
+
+class ReadLocalFileTool(Tool):
+
+    name = "read_local_file"
+
+    description = """
+    Read the contents of a local text, Python, CSV, JSON,
+    Markdown, or other text-based file.
+
+    Use this tool when a GAIA question provides a local file.
+    """
+
+    inputs = {
+        "file_path": {
+            "type": "string",
+            "description": "Path of the file to read."
+        }
+    }
+
+    output_type = "string"
+
+    def forward(self, file_path):
+
+        try:
+
+            path = Path(file_path)
+
+            if not path.exists():
+                return f"File does not exist: {file_path}"
+
+            # Limit extremely large files
+            text = path.read_text(
+                encoding="utf-8",
+                errors="ignore"
+            )
+
+            if len(text) > 50000:
+                text = text[:50000]
+
+            return text
+
+        except Exception as e:
+
+            return f"Could not read file: {e}"
+
+
+# ============================================================
+# CUSTOM TOOL: READ EXCEL
+# ============================================================
+
+class ReadExcelTool(Tool):
+
+    name = "read_excel_file"
+
+    description = """
+    Read an Excel XLSX file using pandas.
+
+    Returns sheet names and the contents of each sheet.
+    Use this for GAIA questions involving spreadsheets,
+    sales tables, numbers, or Excel data.
+    """
+
+    inputs = {
+        "file_path": {
+            "type": "string",
+            "description": "Path to an XLSX file."
+        }
+    }
+
+    output_type = "string"
+
+    def forward(self, file_path):
+
+        try:
+
+            path = Path(file_path)
+
+            if not path.exists():
+                return f"Excel file does not exist: {file_path}"
+
+            excel = pd.ExcelFile(path)
+
+            output = []
+
+            for sheet in excel.sheet_names:
+
+                df = pd.read_excel(
+                    path,
+                    sheet_name=sheet
+                )
+
+                output.append(
+                    f"\n--- SHEET: {sheet} ---\n"
+                )
+
+                output.append(
+                    df.to_string(index=False)
+                )
+
+            result = "\n".join(output)
+
+            if len(result) > 50000:
+                result = result[:50000]
+
+            return result
+
+        except Exception as e:
+
+            return f"Could not read Excel file: {e}"
+
+
+# ============================================================
+# CUSTOM TOOL: FILE INFORMATION
+# ============================================================
+
+class FileInfoTool(Tool):
+
+    name = "file_information"
+
+    description = """
+    Inspect a local file and return its name, extension,
+    size, and basic information.
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
+
+    inputs = {
+        "file_path": {
+            "type": "string",
+            "description": "Path to the local file."
+        }
+    }
+
+    output_type = "string"
+
+    def forward(self, file_path):
+
+        try:
+
+            path = Path(file_path)
+
+            if not path.exists():
+                return "File does not exist."
+
+            size = path.stat().st_size
+
+            return (
+                f"File name: {path.name}\n"
+                f"Extension: {path.suffix}\n"
+                f"Size: {size} bytes\n"
+                f"Path: {path}"
+            )
+
+        except Exception as e:
+
+            return f"Error inspecting file: {e}"
+
+
+# ============================================================
+# CUSTOM TOOL: YOUTUBE TRANSCRIPT
+# ============================================================
+
+class YouTubeTranscriptTool(Tool):
+
+    name = "youtube_transcript"
+
+    description = """
+    Retrieve a transcript from a YouTube video when captions
+    are available.
+
+    Use this for questions asking what someone said in a
+    YouTube video or asking about spoken dialogue.
     """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+
+    inputs = {
+        "video_url": {
+            "type": "string",
+            "description": "Full YouTube video URL."
+        }
+    }
+
+    output_type = "string"
+
+    def forward(self, video_url):
+
+        try:
+
+            from youtube_transcript_api import (
+                YouTubeTranscriptApi
+            )
+
+            # Extract video ID
+            video_id = None
+
+            if "v=" in video_url:
+                video_id = video_url.split("v=")[1].split("&")[0]
+
+            elif "youtu.be/" in video_url:
+                video_id = video_url.split("youtu.be/")[1].split("?")[0]
+
+            elif "youtube.com/shorts/" in video_url:
+                video_id = video_url.split("youtube.com/shorts/")[1].split("?")[0]
+
+            if not video_id:
+                return "Could not extract YouTube video ID."
+
+            api = YouTubeTranscriptApi()
+
+            transcript = api.fetch(video_id)
+
+            text_parts = []
+
+            for item in transcript:
+
+                try:
+                    text_parts.append(item.text)
+                except Exception:
+                    text_parts.append(str(item))
+
+            result = " ".join(text_parts)
+
+            if len(result) > 60000:
+                result = result[:60000]
+
+            return result
+
+        except Exception as e:
+
+            return (
+                "Could not retrieve YouTube transcript. "
+                f"Reason: {e}"
+            )
+
+
+# ============================================================
+# GAIA AGENT
+# ============================================================
+
+class BasicAgent:
+
+    def __init__(self):
+
+        print("=" * 60)
+        print("Initializing GAIA Agent")
+        print("=" * 60)
+
+        # ----------------------------------------------------
+        # MODEL
+        # ----------------------------------------------------
+
+        model_kwargs = {
+            "model_id": MODEL_ID,
+            "temperature": 0.1,
+            "max_tokens": 4096,
+        }
+
+        if HF_TOKEN:
+            model_kwargs["token"] = HF_TOKEN
+
+        self.model = InferenceClientModel(
+            **model_kwargs
+        )
+
+        # ----------------------------------------------------
+        # TOOLS
+        # ----------------------------------------------------
+
+        tools = []
+
+        # Web search
+        try:
+
+            tools.append(
+                DuckDuckGoSearchTool(
+                    max_results=8,
+                    rate_limit=1.0
+                )
+            )
+
+            print("Web search tool enabled.")
+
+        except Exception as e:
+
+            print(
+                f"Could not initialize web search: {e}"
+            )
+
+        # Webpage reader
+        try:
+
+            tools.append(
+                VisitWebpageTool(
+                    max_output_length=30000
+                )
+            )
+
+            print("Webpage tool enabled.")
+
+        except Exception as e:
+
+            print(
+                f"Could not initialize webpage tool: {e}"
+            )
+
+        # Python
+        try:
+
+            tools.append(
+                PythonInterpreterTool(
+                    timeout_seconds=30
+                )
+            )
+
+            print("Python tool enabled.")
+
+        except Exception as e:
+
+            print(
+                f"Could not initialize Python tool: {e}"
+            )
+
+        # File reader
+        tools.append(
+            ReadLocalFileTool()
+        )
+
+        # Excel
+        tools.append(
+            ReadExcelTool()
+        )
+
+        # File information
+        tools.append(
+            FileInfoTool()
+        )
+
+        # YouTube transcript
+        tools.append(
+            YouTubeTranscriptTool()
+        )
+
+        # Speech-to-text
+        try:
+
+            tools.append(
+                SpeechToTextTool()
+            )
+
+            print(
+                "Speech-to-text tool enabled."
+            )
+
+        except Exception as e:
+
+            print(
+                f"Speech-to-text unavailable: {e}"
+            )
+
+        # ----------------------------------------------------
+        # CREATE CODE AGENT
+        # ----------------------------------------------------
+
+        self.agent = CodeAgent(
+            model=self.model,
+            tools=tools,
+            max_steps=12,
+            verbosity_level=1,
+        )
+
+        print(
+            f"GAIA Agent ready with {len(tools)} tools."
+        )
+
+        print("=" * 60)
+
+
+    # ========================================================
+    # RUN AGENT
+    # ========================================================
+
+    def __call__(
+        self,
+        question: str,
+        file_path: str | None = None
+    ) -> str:
+
+        print("\n")
+        print("=" * 60)
+        print("NEW GAIA QUESTION")
+        print("=" * 60)
+
+        print(question)
+
+        # ----------------------------------------------------
+        # Build task
+        # ----------------------------------------------------
+
+        task = f"""
+You are an expert autonomous agent solving a GAIA Level 1
+benchmark question.
+
+Your goal is to produce the EXACT answer required by the
+question.
+
+QUESTION:
+
+{question}
+
+------------------------------------------------------------
+
+IMPORTANT RULES
+
+1. Carefully read the entire question.
+
+2. Determine exactly what the question is asking.
+
+3. If external information is required:
+   use web search.
+
+4. If a webpage must be inspected:
+   use the webpage tool.
+
+5. If calculations are required:
+   use Python.
+
+6. If a spreadsheet is provided:
+   inspect it with the Excel tool and Python/pandas.
+
+7. If a Python file is provided:
+   read and execute/analyze it with Python.
+
+8. If an audio file is provided:
+   use speech-to-text if necessary.
+
+9. If the question contains a YouTube URL:
+   try the YouTube transcript tool when the question
+   concerns spoken dialogue.
+
+10. Never guess when the information can be obtained
+    from a tool.
+
+11. Verify important calculations.
+
+12. Follow the requested output format EXACTLY.
+
+13. Pay attention to:
+    - capitalization
+    - commas
+    - ordering
+    - decimal places
+    - units
+    - first name vs surname
+    - city vs country
+    - IOC codes
+    - algebraic chess notation
+    - requested number of words
+
+14. Do not add explanations to the final answer.
+
+15. Do not write:
+    "The answer is..."
+
+16. Do not write:
+    "FINAL ANSWER"
+
+17. Return ONLY the answer requested by the question.
+
+------------------------------------------------------------
+"""
+
+        # ----------------------------------------------------
+        # Add file information
+        # ----------------------------------------------------
+
+        if file_path:
+
+            file_extension = Path(
+                file_path
+            ).suffix.lower()
+
+            task += f"""
+
+A FILE IS ATTACHED TO THIS QUESTION.
+
+Local file path:
+
+{file_path}
+
+File extension:
+
+{file_extension}
+
+You MUST inspect the file when it is relevant.
+
+Available file-related tools include:
+
+- file_information
+- read_local_file
+- read_excel_file
+- Python
+
+If the file is XLSX:
+use read_excel_file and/or pandas.
+
+If the file is Python:
+read the code and execute/analyze it.
+
+If the file is text:
+read it.
+
+If the file is audio:
+use speech-to-text.
+
+Do not ignore the attached file.
+"""
+
+        # ----------------------------------------------------
+        # Run
+        # ----------------------------------------------------
+
+        try:
+
+            result = self.agent.run(task)
+
+            answer = str(result).strip()
+
+            # ------------------------------------------------
+            # Clean accidental formatting
+            # ------------------------------------------------
+
+            answer = clean_final_answer(
+                answer
+            )
+
+            print(
+                "FINAL SUBMITTED ANSWER:"
+            )
+
+            print(answer)
+
+            print("=" * 60)
+
+            return answer
+
+        except Exception as e:
+
+            print(
+                f"Agent execution error: {e}"
+            )
+
+            return (
+                f"ERROR: {e}"
+            )
+
+
+# ============================================================
+# CLEAN FINAL ANSWER
+# ============================================================
+
+def clean_final_answer(answer: str) -> str:
+
+    answer = answer.strip()
+
+    # Remove common accidental prefixes
+
+    prefixes = [
+        "FINAL ANSWER:",
+        "FINAL ANSWER",
+        "Answer:",
+        "ANSWER:",
+        "The answer is:",
+        "The answer is"
+    ]
+
+    for prefix in prefixes:
+
+        if answer.lower().startswith(
+            prefix.lower()
+        ):
+
+            answer = answer[
+                len(prefix):
+            ].strip()
+
+    # Remove markdown code fences
+
+    if answer.startswith("```"):
+
+        lines = answer.splitlines()
+
+        if len(lines) >= 3:
+
+            lines = lines[1:-1]
+
+            answer = "\n".join(
+                lines
+            ).strip()
+
+    return answer
+
+
+# ============================================================
+# DOWNLOAD GAIA FILE
+# ============================================================
+
+def download_task_file(
+    task_id: str,
+    file_name: str
+):
+
+    if not file_name:
+
+        return None
+
+    try:
+
+        file_url = (
+            f"{DEFAULT_API_URL}/files/{task_id}"
+        )
+
+        print(
+            f"Downloading attachment from: {file_url}"
+        )
+
+        response = requests.get(
+            file_url,
+            timeout=60
+        )
+
+        response.raise_for_status()
+
+        # Temporary directory
+        temp_dir = (
+            Path(tempfile.gettempdir())
+            / "gaia_files"
+        )
+
+        temp_dir.mkdir(
+            parents=True,
+            exist_ok=True
+        )
+
+        safe_name = Path(
+            file_name
+        ).name
+
+        file_path = (
+            temp_dir / safe_name
+        )
+
+        file_path.write_bytes(
+            response.content
+        )
+
+        print(
+            f"Downloaded: {file_path}"
+        )
+
+        return str(file_path)
+
+    except Exception as e:
+
+        print(
+            f"File download failed: {e}"
+        )
+
+        return None
+
+
+# ============================================================
+# FETCH QUESTIONS
+# ============================================================
+
+def fetch_questions():
+
+    questions_url = (
+        f"{DEFAULT_API_URL}/questions"
+    )
+
+    print(
+        f"Fetching questions from: {questions_url}"
+    )
+
+    response = requests.get(
+        questions_url,
+        timeout=30
+    )
+
+    response.raise_for_status()
+
+    data = response.json()
+
+    if not isinstance(data, list):
+
+        raise ValueError(
+            "Questions API did not return a list."
+        )
+
+    print(
+        f"Fetched {len(data)} questions."
+    )
+
+    return data
+
+
+# ============================================================
+# RUN EVERYTHING AND SUBMIT
+# ============================================================
+
+def run_and_submit_all(
+    profile: gr.OAuthProfile | None
+):
+
+    # --------------------------------------------------------
+    # Check login
+    # --------------------------------------------------------
 
     if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
+
+        username = str(
+            profile.username
+        )
+
+        print(
+            f"Logged in user: {username}"
+        )
+
     else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
 
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
+        print(
+            "User is not logged in."
+        )
+
+        return (
+            "Please login to Hugging Face first.",
+            None
+        )
+
+    # --------------------------------------------------------
+    # Get Space ID
+    # --------------------------------------------------------
+
+    space_id = os.getenv(
+        "SPACE_ID"
+    )
+
+    if not space_id:
+
+        return (
+            "SPACE_ID environment variable was not found. "
+            "Run this inside your Hugging Face Space.",
+            None
+        )
+
+    agent_code = (
+        f"https://huggingface.co/spaces/"
+        f"{space_id}/tree/main"
+    )
+
+    print(
+        f"Agent code: {agent_code}"
+    )
+
+    # --------------------------------------------------------
+    # Initialize agent
+    # --------------------------------------------------------
 
-    # 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}")
+
+        print(
+            f"Agent initialization failed: {e}"
+        )
+
+        return (
+            f"Agent initialization failed: {e}",
+            None
+        )
+
+    # --------------------------------------------------------
+    # Fetch questions
+    # --------------------------------------------------------
+
     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
+
+        questions_data = (
+            fetch_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
+        return (
+            f"Could not fetch questions: {e}",
+            None
+        )
+
+    # --------------------------------------------------------
+    # Process questions
+    # --------------------------------------------------------
+
     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(
+        f"Processing {total_questions} questions..."
+    )
+
+    for index, item in enumerate(
+        questions_data,
+        start=1
+    ):
+
+        print("\n")
+        print(
+            f"QUESTION {index}/{total_questions}"
+        )
+
+        task_id = item.get(
+            "task_id"
+        )
+
+        question_text = item.get(
+            "question"
+        )
+
+        file_name = item.get(
+            "file_name",
+            ""
+        )
+
+        # ----------------------------------------------------
+        # Validate
+        # ----------------------------------------------------
+
+        if not task_id:
+
+            print(
+                "Skipping: missing task_id"
+            )
+
             continue
+
+        if question_text is None:
+
+            print(
+                "Skipping: missing question"
+            )
+
+            continue
+
+        # ----------------------------------------------------
+        # Download attachment
+        # ----------------------------------------------------
+
+        file_path = None
+
+        if file_name:
+
+            file_path = download_task_file(
+                task_id,
+                file_name
+            )
+
+        # ----------------------------------------------------
+        # 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,
+                file_path
+            )
+
+            # Safety conversion
+            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,
+                    "Submitted Answer": submitted_answer
+                }
+            )
+
+            print(
+                f"Question {index} completed."
+            )
+
         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"Question failed: {e}"
+            )
+
+            results_log.append(
+                {
+                    "Task ID": task_id,
+                    "Question": question_text,
+                    "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}'..."
-    print(status_update)
+        return (
+            "Agent produced no answers.",
+            pd.DataFrame(
+                results_log
+            )
+        )
+
+    # --------------------------------------------------------
+    # Submission
+    # --------------------------------------------------------
+
+    submission_data = {
+        "username": username.strip(),
+        "agent_code": agent_code,
+        "answers": answers_payload
+    }
+
+    submit_url = (
+        f"{DEFAULT_API_URL}/submit"
+    )
+
+    print("\n")
+    print("=" * 60)
+    print(
+        f"Submitting {len(answers_payload)} answers"
+    )
+    print("=" * 60)
 
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
+
+        response = requests.post(
+            submit_url,
+            json=submission_data,
+            timeout=300
+        )
+
         response.raise_for_status()
+
         result_data = response.json()
+
+        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."
+        )
+
         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: {result_data.get('username', username)}\n"
+            f"Score: {score}%\n"
+            f"Correct: "
+            f"{correct_count}/"
+            f"{total_attempted}\n\n"
+            f"Message: {message}"
+        )
+
+        print(
+            final_status
+        )
+
+        return (
+            final_status,
+            pd.DataFrame(
+                results_log
+            )
+        )
+
     except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
+
+        detail = (
+            f"HTTP error {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_json = (
+                e.response.json()
+            )
+
+            detail += (
+                f": {error_json}"
+            )
+
+        except Exception:
+
+            detail += (
+                f": {e.response.text[:1000]}"
+            )
+
+        return (
+            f"Submission failed: {detail}",
+            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
+
+        return (
+            "Submission failed: "
+            "request timed out. "
+            "The agent may have taken too long.",
+            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
+
+        return (
+            f"Submission network error: {e}",
+            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
 
+        return (
+            f"Unexpected submission error: {e}",
+            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"
+    )
+
     gr.Markdown(
         """
-        **Instructions:**
+        ### Final Agent Assignment
+
+        This Space runs an autonomous agent against the
+        GAIA evaluation questions.
+
+        **Capabilities**
+
+        - 🌐 Web search
+        - 🔗 Webpage reading
+        - 🐍 Python calculations
+        - 📊 Excel analysis
+        - 📁 File analysis
+        - 🎵 Speech-to-text
+        - ▶️ YouTube transcript extraction
+        - 🤖 Multi-step reasoning
+
+        **Important:** The final agent response is submitted
+        as the answer, so it must follow the exact format
+        requested by each question.
+        """
+    )
 
-        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.Markdown(
+        """
+        ### Instructions
 
-        ---
-        **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. Log in to Hugging Face.
+        2. Click **Run Evaluation & Submit All Answers**.
+        3. The agent will retrieve the GAIA questions.
+        4. The agent will solve each question.
+        5. Attachments will be downloaded automatically.
+        6. Answers will be submitted to the evaluation API.
+        7. Your score will appear below.
         """
     )
 
+    gr.Markdown(
+        "---"
+    )
+
     gr.LoginButton()
 
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
+    run_button = gr.Button(
+        "🚀 Run Evaluation & Submit All Answers",
+        variant="primary"
+    )
 
-    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)
+    status_output = gr.Textbox(
+        label="Run Status / Submission Result",
+        lines=8,
+        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]
+        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
-
-    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(
+        "\n"
+        + "-" * 30
+        + " GAIA Agent Starting "
+        + "-" * 30
+    )
+
+    space_host = os.getenv(
+        "SPACE_HOST"
+    )
+
+    space_id = os.getenv(
+        "SPACE_ID"
+    )
+
+    if space_host:
+
+        print(
+            f"SPACE_HOST: {space_host}"
+        )
+
+        print(
+            "Runtime URL: "
+            f"https://{space_host}.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."
+        )
+
+    if space_id:
+
+        print(
+            f"SPACE_ID: {space_id}"
+        )
+
+        print(
+            "Repository:"
+        )
+
+        print(
+            f"https://huggingface.co/spaces/{space_id}"
+        )
+
+        print(
+            "Code:"
+        )
+
+        print(
+            f"https://huggingface.co/spaces/"
+            f"{space_id}/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(
+        "-" * 70
+    )
 
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(
+        debug=True,
+        share=False
+    )