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app.py

@@ -1,519 +1,221 @@
+# app.py
 import os
 import gradio as gr
 import requests
+import openai
+from smolagents import OpenAIServerModel, DuckDuckGoSearchTool, CodeAgent, WikipediaSearchTool
+from pathlib import Path
+import tempfile
+from smolagents.tools import PipelineTool, Tool
+import pathlib
+from typing import Union, Optional
 import pandas as pd
-import time
+from tabulate import tabulate  # pragma: no cover – fallback path
 import re
-import json
-import traceback
-import tempfile
-from urllib.parse import urlparse
-from dotenv import load_dotenv
-
-# Import necessary components from smolagents
-from smolagents import (
-    CodeAgent,  # Using CodeAgent as the core agent
-    DuckDuckGoSearchTool, 
-    OpenAIServerModel,
-    PythonInterpreterTool,
-    tool  # Import tool decorator
-)
-from typing import List, Dict, Any, Optional, Tuple
-
-# Load environment variables
-load_dotenv()
 
+# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Custom Tool Definitions ---
-@tool
-def reverse_text(text: str) -> str:
+class SpeechToTextTool(PipelineTool):
     """
-    Reverses a text string. Useful for handling reversed text questions.
-    
-    Args:
-        text: The text to reverse
-        
-    Returns:
-        The reversed text
+    Transcribes an audio file to text using the OpenAI Whisper API.
+    Only local file paths are supported.
     """
-    return text[::-1]
+    default_checkpoint = "openai/whisper-1"   # purely informational here
+    description = (
+        "This tool sends an audio file to OpenAI Whisper and returns the "
+        "transcribed text."
+    )
+    name = "transcriber"
+    inputs = {
+        "audio": {
+            "type": "string",
+            "description": "Absolute or relative path to a local audio file.",
+        }
+    }
+    output_type = "string"
 
-@tool
-def analyze_csv_file(file_path: str, query: str) -> str:
-    """
-    Analyze a CSV file using pandas and answer a question about it.
-    
-    Args:
-        file_path: Path to the CSV file
-        query: Question about the data
-        
-    Returns:
-        Analysis result or error message
-    """
-    try:
-        import pandas as pd
-        
-        # Read the CSV file
-        df = pd.read_csv(file_path)
-        
-        # Run various analyses based on the query
-        result = f"CSV file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
-        result += f"Columns: {', '.join(df.columns)}\n\n"
-        
-        # Add summary statistics
-        result += "Summary statistics:\n"
-        result += str(df.describe())
-        
-        return result
-    except ImportError:
-        return "Error: pandas is not installed. Please install it with 'pip install pandas'."
-    except Exception as e:
-        return f"Error analyzing CSV file: {str(e)}"
+    # ──────────────────────────────────────────────────────────────────────────
+    # Public interface
+    # ──────────────────────────────────────────────────────────────────────────
+    def __call__(self, audio: str) -> str:
+        """
+        Convenience wrapper so the tool can be used like a regular function:
+            text = SpeechToTextTool()(path_to_audio)
+        """
+        return self._transcribe(audio)
+
+    # ──────────────────────────────────────────────────────────────────────────
+    # Internal helpers
+    # ──────────────────────────────────────────────────────────────────────────
+    @staticmethod
+    def _transcribe(audio_path: str) -> str:
+        # ----- validation ----------------------------------------------------
+        if not isinstance(audio_path, str):
+            raise TypeError(
+                "Parameter 'audio' must be a string containing the file path."
+            )
+        path = Path(audio_path).expanduser().resolve()
+        if not path.is_file():
+            raise FileNotFoundError(f"No such audio file: {path}")
+
+        # ----- API call ------------------------------------------------------
+        with path.open("rb") as fp:
+            response = openai.audio.transcriptions.create(
+                file=fp,
+                model="whisper-1",        # currently the only Whisper model
+                response_format="text"    # returns plain text instead of JSON
+            )
 
-@tool
-def analyze_excel_file(file_path: str, query: str) -> str:
-    """
-    Analyze an Excel file using pandas and answer a question about it.
-    
-    Args:
-        file_path: Path to the Excel file
-        query: Question about the data
-        
-    Returns:
-        Analysis result or error message
-    """
-    try:
-        import pandas as pd
-        
-        # Read the Excel file
-        df = pd.read_excel(file_path)
-        
-        # Run various analyses based on the query
-        result = f"Excel file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
-        result += f"Columns: {', '.join(df.columns)}\n\n"
-        
-        # Add summary statistics
-        result += "Summary statistics:\n"
-        result += str(df.describe())
-        
-        return result
-    except ImportError:
-        return "Error: pandas and openpyxl are not installed. Please install them with 'pip install pandas openpyxl'."
-    except Exception as e:
-        return f"Error analyzing Excel file: {str(e)}"
+        # For response_format="text", `response` is already the raw transcript
+        return response
 
-@tool
-def parse_table(table_text: str) -> str:
-    """
-    Parses an ASCII or markdown table into a structured format
-    
-    Args:
-        table_text: The raw table string
-        
-    Returns:
-        The parsed table (as a string representation)
-    """
-    try:
-        import pandas as pd
-        from io import StringIO
-        # Clean pipes and extra spaces
-        clean = re.sub(r"^\||\|$", "", table_text.strip(), flags=re.MULTILINE)
-        df = pd.read_csv(StringIO(clean), sep=r"\s*\|\s*", engine="python")
-        # Return DataFrame as string
-        return df.to_string()
-    except Exception as e:
-        return f"Error parsing table: {str(e)}"
+class ExcelToTextTool(Tool):
+    """Render an Excel worksheet as Markdown text."""
 
-@tool
-def browse_webpage(url: str) -> str:
-    """
-    Browses the web to fetch information from websites
-    
-    Args:
-        url: The URL to visit
-        
-    Returns:
-        The webpage content
-    """
-    try:
-        import requests
-        from bs4 import BeautifulSoup
-        
-        headers = {
-            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
-        }
-        
-        response = requests.get(url, headers=headers, timeout=10)
-        
-        if response.status_code != 200:
-            return f"Error: Failed to fetch the webpage. Status code: {response.status_code}"
-        
-        # Parse the HTML content
-        soup = BeautifulSoup(response.text, 'html.parser')
-        
-        # Remove script and style elements
-        for script in soup(["script", "style"]):
-            script.extract()
-        
-        # Get the text content
-        text = soup.get_text()
-        
-        # Clean up the text
-        lines = (line.strip() for line in text.splitlines())
-        chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
-        text = '\n'.join(chunk for chunk in chunks if chunk)
-        
-        # Truncate if too long
-        if len(text) > 10000:
-            text = text[:10000] + "...\n[Content truncated due to length]"
-        
-        return text
-            
-    except Exception as e:
-        return f"Error browsing the web: {str(e)}"
+    # ------------------------------------------------------------------
+    # Required smol‑agents metadata
+    # ------------------------------------------------------------------
+    name = "excel_to_text"
+    description = (
+        "Read an Excel file and return a Markdown table of the requested sheet. "
+        "Accepts either the sheet name or the zero-based index."
+    )
 
-@tool
-def save_and_read_file(content: str, filename: Optional[str] = None) -> str:
-    """
-    Save content to a temporary file and return the path.
-    Useful for processing files from the GAIA API.
-    
-    Args:
-        content: The content to save to the file
-        filename: Optional filename, will generate a random name if not provided
-        
-    Returns:
-        Path to the saved file
-    """
-    temp_dir = tempfile.gettempdir()
-    if filename is None:
-        temp_file = tempfile.NamedTemporaryFile(delete=False)
-        filepath = temp_file.name
-    else:
-        filepath = os.path.join(temp_dir, filename)
-    
-    # Write content to the file
-    with open(filepath, 'w') as f:
-        f.write(content)
-    
-    return f"File saved to {filepath}. You can read this file to process its contents."
-
-@tool
-def download_file_from_url(url: str, filename: Optional[str] = None) -> str:
+    inputs = {
+        "excel_path": {
+            "type": "string",
+            "description": "Path to the Excel file (.xlsx / .xls).",
+        },
+        "sheet_name": {
+            "type": "string",
+            "description": (
+                "Worksheet name or zero‑based index *as a string* (optional; default first sheet)."
+            ),
+            "nullable": True,
+        },
+    }
+
+    output_type = "string"
+
+    # ------------------------------------------------------------------
+    # Core logic
+    # ------------------------------------------------------------------
+    def forward(
+            self,
+            excel_path: str,
+            sheet_name: Optional[str] = None,
+    ) -> str:
+        """Load *excel_path* and return the sheet as a Markdown table."""
+
+        path = pathlib.Path(excel_path).expanduser().resolve()
+        if not path.exists():
+            return f"Error: Excel file not found at {path}"
+
+        try:
+            # Interpret sheet identifier -----------------------------------
+            sheet: Union[str, int]
+            if sheet_name is None or sheet_name == "":
+                sheet = 0  # first sheet
+            else:
+                # If the user passed a numeric string (e.g. "1"), cast to int
+                sheet = int(sheet_name) if sheet_name.isdigit() else sheet_name
+
+            # Load worksheet ----------------------------------------------
+            df = pd.read_excel(path, sheet_name=sheet)
+
+            # Render to Markdown; fall back to tabulate if needed ---------
+            if hasattr(pd.DataFrame, "to_markdown"):
+                return df.to_markdown(index=False)
+            from tabulate import tabulate  # pragma: no cover – fallback path
+
+            return tabulate(df, headers="keys", tablefmt="github", showindex=False)
+
+        except Exception as exc:  # broad catch keeps the agent chat‑friendly
+            return f"Error reading Excel file: {exc}"
+
+
+def download_file_if_any(base_api_url: str, task_id: str) -> str | None:
     """
-    Download a file from a URL and save it to a temporary location.
-    
-    Args:
-        url: The URL to download from
-        filename: Optional filename, will generate one based on URL if not provided
-        
-    Returns:
-        Path to the downloaded file
+    Try GET /files/{task_id}.
+    • On HTTP 200 → save to a temp dir and return local path.
+    • On 404 → return None.
+    • On other errors → raise so caller can log / handle.
     """
+    url = f"{base_api_url}/files/{task_id}"
     try:
-        # Parse URL to get filename if not provided
-        if not filename:
-            path = urlparse(url).path
-            filename = os.path.basename(path)
-            if not filename:
-                # Generate a random name if we couldn't extract one
-                import uuid
-                filename = f"downloaded_{uuid.uuid4().hex[:8]}"
-        
-        # Create temporary file
-        temp_dir = tempfile.gettempdir()
-        filepath = os.path.join(temp_dir, filename)
-        
-        # Download the file
-        response = requests.get(url, stream=True)
-        response.raise_for_status()
-        
-        # Save the file
-        with open(filepath, 'wb') as f:
-            for chunk in response.iter_content(chunk_size=8192):
-                f.write(chunk)
-        
-        return f"File downloaded to {filepath}. You can now process this file."
-    except Exception as e:
-        return f"Error downloading file: {str(e)}"
-
-# --- GAIA Agent Enhanced System Prompt ---
-ENHANCED_SYSTEM_PROMPT = """You are an expert AI assistant for the GAIA benchmark.
-
-IMPORTANT GUIDELINES:
-1. Provide EXACT answers with no explanations or extra text.
-2. Only return the final answer, not your reasoning.
-3. For lists, alphabetize and provide comma-separated values.
-4. For numerical answers, return the number as a string.
-5. For chess positions, analyze the board carefully and provide the winning move.
-6. For "countries that no longer exist" questions, consider: USSR, East Germany, Yugoslavia, Czechoslovakia.
-7. For reversed text questions, handle backwards text by reversing it first, then answer directly. For example, if the reversed text asks for the opposite of "left", answer "right" not the reversed text.
-8. For mathematical calculations, perform the calculation precisely.
-9. For web research tasks, verify from multiple sources, and return only the exact answer.
-10. For file analysis, extract only the specific information requested.
-11. For image analysis, describe what you see in detail.
-12. For YouTube videos, try to get the transcript if possible.
-
-SPECIAL CASES:
-1. When asked about recent dates, use the current date (April 25, 2025) as reference.
-2. If a question contains a URL, extract information from it.
-3. If a question requires using a web service that outputs different values each time (like exchange rates), take the most common value.
-4. For calculations involving current data, perform the calculation after fetching the most up-to-date information.
-5. For problems that require complex reasoning, break them down into steps.
-
-KNOWN QUESTIONS:
-- If asked about Mercedes Sosa albums between 2000 and 2009, the answer is "3".
-- If asked about a Malko Competition recipient from a country that no longer exists, the answer is "Pavel".
-- If asked about Vietnamese specimens and Nedoshiva, the answer is "Saint Petersburg".
-- If asked about an equine veterinarian and chemistry materials, the answer is "Jones".
-- If text is reversed and asks for the opposite of "left", the answer is "right".
-
-TASK APPROACH:
-1. Carefully analyze the question to determine the exact information needed.
-2. Choose the most appropriate approach for the task.
-3. If needed, break complex tasks into smaller steps.
-4. Double-check your answer before submitting.
-5. Return ONLY the final answer, with no explanations or reasoning.
-
-Remember: precision and exactness are crucial. Provide only the requested information in the simplest possible format.
-"""
-
-# --- Main Application Class ---
-class GAIABenchmarkAgent:
-    """GAIA Benchmark Agent using CodeAgent"""
-    
+        resp = requests.get(url, timeout=30)
+        if resp.status_code == 404:
+            return None          # no file
+        resp.raise_for_status()   # raise on 4xx/5xx ≠ 404
+    except requests.exceptions.HTTPError as e:
+        # propagate non-404 errors (403, 500, …)
+        raise e
+
+    # ▸ Save bytes to a named file inside the system temp dir
+    #    Try to keep original extension from Content-Disposition if present.
+    cdisp = resp.headers.get("content-disposition", "")
+    filename = task_id                                 # default base name
+    if "filename=" in cdisp:
+        m = re.search(r'filename="([^"]+)"', cdisp)
+        if m:
+            filename = m.group(1)                      # keep provided name
+
+    tmp_dir = Path(tempfile.gettempdir()) / "gaia_files"
+    tmp_dir.mkdir(exist_ok=True)
+    file_path = tmp_dir / filename
+    with open(file_path, "wb") as f:
+        f.write(resp.content)
+    return str(file_path)
+
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+class BasicAgent:
     def __init__(self):
-        print("Initializing GAIA Benchmark Agent...")
-        
-        try:
-            # Check for API key
-            api_key = os.environ.get("OPENAI_API_KEY")
-            if not api_key:
-                print("WARNING: OPENAI_API_KEY environment variable not set!")
-                raise ValueError("No OpenAI API key found, please set the OPENAI_API_KEY environment variable")
-                
-            # Determine which model to use
-            model_id = os.environ.get("AGENT_MODEL_ID", "gpt-3.5-turbo")
-            print(f"Using model: {model_id}")
-            
-            # Initialize OpenAI model
-            model = OpenAIServerModel(
-                model_id=model_id,
-                api_key=api_key,
-                temperature=0.1
-            )
-            
-            # Initialize tools list
-            tools = [
-                DuckDuckGoSearchTool(),  # Web search
-                PythonInterpreterTool(),  # Python interpreter
-                reverse_text,            # Text reversal
-                analyze_csv_file,        # CSV analysis
-                analyze_excel_file,      # Excel analysis
-                parse_table,             # Table parsing
-                browse_webpage,          # Web browsing
-                save_and_read_file,      # File operations
-                download_file_from_url   # File download
-            ]
-            
-            # Create CodeAgent
-            self.agent = CodeAgent(
-                model=model,
-                tools=tools,
-                system_prompt=ENHANCED_SYSTEM_PROMPT,
-                verbose=True
-            )
-            
-            print("GAIA Benchmark Agent initialized successfully.")
-        except Exception as e:
-            print(f"Error initializing agent: {e}")
-            traceback.print_exc()
-            self.agent = None
-            raise
-    
+        self.agent = CodeAgent(
+            model=OpenAIServerModel(model_id="gpt-4o"),
+            tools=[DuckDuckGoSearchTool(), WikipediaSearchTool(), SpeechToTextTool(), ExcelToTextTool()],
+            add_base_tools=True,
+            additional_authorized_imports=['pandas','numpy','csv','subprocess']
+        )
+
+        print("BasicAgent initialized.")
+
     def __call__(self, question: str) -> str:
-        """Process a GAIA benchmark question and return the answer"""
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        
-        try:
-            # Process special cases first
-            direct_answer = self._check_special_cases(question)
-            if direct_answer:
-                print(f"Direct answer for special case: {direct_answer}")
-                return direct_answer
-            
-            # Use CodeAgent to process the question
-            start_time = time.time()
-            answer = self.agent.run(question, max_steps=3)
-            end_time = time.time()
-            
-            # Process the answer
-            # Sometimes CodeAgent returns a string, sometimes it has additional step info
-            # Here we prioritize extracting from final_answer if available, otherwise use last step result
-            if isinstance(answer, dict) and "final_answer" in answer:
-                final_answer = answer["final_answer"]
-            elif isinstance(answer, dict) and "steps" in answer and answer["steps"]:
-                # Get the result from the last step
-                last_step = answer["steps"][-1]
-                if "output" in last_step:
-                    final_answer = last_step["output"]
-                else:
-                    final_answer = str(last_step)
-            else:
-                final_answer = str(answer)
-            
-            # Clean the answer, removing common prefixes
-            final_answer = self._clean_answer(final_answer)
-            
-            print(f"Agent returned answer (first 50 chars): {final_answer[:50] if final_answer else 'None'}... Time taken: {end_time - start_time:.2f}s")
-            return final_answer
-        except Exception as e:
-            print(f"Error processing question: {e}")
-            traceback.print_exc()
-            
-            # Fallback mechanisms for specific error cases
-            fallback_answer = self._get_fallback_answer(question, e)
-            return fallback_answer
-    
-    def _check_special_cases(self, question: str) -> Optional[str]:
-        """Check for special cases and known questions, return direct answers"""
-        # Special handling for reversed text with "answer" reversed
-        if ".rewsna eht sa " in question:
-            return "right"
-            
-        # Special handling for known questions
-        if "Mercedes Sosa" in question and "2000" in question and "2009" in question:
-            return "3"
-            
-        if "Malko Competition" in question and "country that no longer exist" in question:
-            return "Pavel"
-            
-        if "Vietnamese specimens" in question and "Nedoshivina" in question:
-            return "Saint Petersburg"
-            
-        if "equine veterinarian" in question and "chemistry materials" in question:
-            return "Jones"
-            
-        # Media content handling
-        if any(term in question.lower() for term in ["youtube.com", "youtube video", "watch?v="]):
-            return "Unable to access video content directly. Please provide a transcript or description."
-            
-        if any(term in question.lower() for term in ["mp3", "audio file", "recording"]):
-            return "Unable to process audio content directly. Please provide a transcript if available."
-            
-        if any(term in question.lower() for term in ["jpg", "png", "image file"]):
-            return "Unable to analyze image content directly. Please provide a detailed description."
-            
-        # File processing
-        if any(term in question.lower() for term in ["excel file", "xlsx", "spreadsheet"]):
-            return "Unable to access the Excel file directly. Please provide the data in another format."
-            
-        if any(term in question.lower() for term in ["pdf file", "pdf document"]):
-            return "Unable to access the PDF file directly. Please provide the data in another format."
-            
-        if any(term in question.lower() for term in ["csv file", "comma-separated values"]):
-            return "Unable to access the CSV file directly. Please provide the data in another format."
-            
-        # Chess position handling
-        if "chess position" in question.lower() and "image" in question.lower():
-            return "Unable to analyze the chess position without a description or tool support."
-            
-        return None
-        
-    def _get_fallback_answer(self, question: str, error: Exception) -> str:
-        """Provide fallback answers for specific error cases"""
-        if ".rewsna eht sa " in question:
-            return "right"
-                
-        if any(term in question.lower() for term in ["excel", "spreadsheet", "file"]):
-            return "Unable to access the file directly."
-                
-        if "chess position" in question.lower():
-            return "Unable to analyze the chess position."
-                
-        if any(term in question.lower() for term in ["youtube", "video"]):
-            return "Unable to access video content directly."
-            
-        return f"Error processing question: {str(error)}"
-    
-    def _clean_answer(self, answer: Any) -> str:
-        """
-        Clean up the answer to remove common prefixes and formatting
-        """
-        # Convert non-string types to strings
-        if not isinstance(answer, str):
-            # Handle numeric types (float, int)
-            if isinstance(answer, float):
-                # Format floating point numbers properly
-                if answer.is_integer():
-                    formatted_answer = str(int(answer))
-                else:
-                    formatted_answer = str(answer)
-                return formatted_answer
-            elif isinstance(answer, int):
-                return str(answer)
-            else:
-                # For any other type
-                return str(answer)
-        
-        # Now we know answer is a string, so we can safely use string methods
-        # Normalize whitespace
-        answer = answer.strip()
-        
-        # Remove common prefixes and formatting that models add
-        prefixes_to_remove = [
-            "The answer is ", 
-            "Answer: ",
-            "Final answer: ",
-            "The result is ",
-            "To answer this question: ",
-            "Based on the information provided, ",
-            "According to the information: ",
-        ]
-        
-        for prefix in prefixes_to_remove:
-            if answer.lower().startswith(prefix.lower()):
-                answer = answer[len(prefix):].strip()
-        
-        # Remove quotes if they wrap the entire answer
-        if (answer.startswith('"') and answer.endswith('"')) or (answer.startswith("'") and answer.endswith("'")):
-            answer = answer[1:-1].strip()
-        
-        return answer
-
-
-def run_and_submit_all(profile: gr.OAuthProfile | None):
+        fixed_answer = self.agent.run(question)
+        print(f"Agent returning answer: {fixed_answer}")
+        return fixed_answer
+
+def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the GAIA Benchmark Agent on them, submits all answers,
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.
     """
     # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending code link
+    space_id = "l3xv/Final_Assignment_Template"
 
     if profile:
-        username = f"{profile.username}"
+        username= f"{profile.username}"
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
-        return "Please login to Hugging Face using the button below.", None
+        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"
 
-    # 1. Instantiate Agent
+    # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = GAIABenchmarkAgent()
+        agent = BasicAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
-        traceback.print_exc()
         return f"Error initializing agent: {e}", None
-    
-    # For HuggingFace spaces, this points to the repository
+    # 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(f"Agent code URL: {agent_code}")
+    print(agent_code)
 
     # 2. Fetch Questions
     print(f"Fetching questions from: {questions_url}")
@@ -536,66 +238,40 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
 
-    # 3. Run Agent
+    # 3. Run your Agent
     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")
+
+        # ----------fetch any attached file ----------
+        try:
+            file_path = download_file_if_any(api_url, task_id)
+        except Exception as e:
+            file_path = None
+            print(f"[file fetch error] {task_id}: {e}")
+
+        # ---------- Build the prompt sent to the agent ----------
+        if file_path:
+            q_for_agent = (
+                f"{question_text}\n\n"
+                f"---\n"
+                f"A file was downloaded for this task and saved locally at:\n"
+                f"{file_path}\n"
+                f"---\n\n"
+            )
+        else:
+            q_for_agent = question_text
+
         if not task_id or question_text is None:
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            print(f"Processing task {task_id}: {question_text[:50]}...")
-            
-            # Run the agent with retry mechanism
-            max_retries = 2
-            submitted_answer = None
-            last_error = None
-            
-            for retry in range(max_retries + 1):
-                try:
-                    if retry > 0:
-                        print(f"Retry {retry}/{max_retries} for task {task_id}")
-                        
-                    submitted_answer = agent(question_text)
-                    
-                    # Very short answers might be incorrect - check length
-                    if submitted_answer and len(submitted_answer) < 2:
-                        # For extremely short answers, try one more time
-                        backup_answer = agent(question_text)
-                        # Choose the longer answer if both are very short
-                        if len(backup_answer) > len(submitted_answer):
-                            submitted_answer = backup_answer
-                            
-                    break
-                except Exception as e:
-                    last_error = e
-                    print(f"Error on attempt {retry+1}: {e}")
-                    # Small delay before retry
-                    time.sleep(1)
-            
-            # If all retries failed, use error message or fallbacks
-            if submitted_answer is None:
-                if last_error:
-                    # Try to use special case handling
-                    if "opposite of left" in question_text.lower() or "rewsna eht sa" in question_text:
-                        submitted_answer = "right"
-                    else:
-                        submitted_answer = f"Error: {str(last_error)}"
-                else:
-                    submitted_answer = "Unable to determine answer after multiple attempts."
-            
-            # Add to answers and log
+            submitted_answer = agent(q_for_agent)
             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"Completed task {task_id}")
-            
-            # Add small delay to avoid API rate limits
-            time.sleep(0.5)
-            
         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}"})
@@ -655,16 +331,17 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
 
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Advanced GAIA Agent Evaluation Runner")
+    gr.Markdown("# Basic Agent Evaluation Runner")
     gr.Markdown(
         """
         **Instructions:**
-
-        1. Use the login button below to sign in with your Hugging Face account.
-        2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run the agent, and submit answers.
-
-        **Note:** This process may take several minutes to complete as the agent processes each question.
-        The agent uses advanced tools for web search, code execution, and data analysis to solve GAIA benchmark tasks.
+        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
+        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+        ---
+        **Disclaimers:**
+        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
+        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
         """
     )
 
@@ -673,6 +350,7 @@ with gr.Blocks() as demo:
     run_button = gr.Button("Run Evaluation & Submit All Answers")
 
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+    # Removed max_rows=10 from DataFrame constructor
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
     run_button.click(
@@ -682,24 +360,24 @@ with gr.Blocks() as demo:
 
 if __name__ == "__main__":
     print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup
+    # 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
+    space_id_startup = "l3xv/Final_Assignment_Template"
 
     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: {space_host_startup}")
+        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
     else:
-        print("ℹ SPACE_HOST environment variable not found (running locally?).")
+        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(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("ℹ SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
 
     print("-"*(60 + len(" App Starting ")) + "\n")
 
-    print("Launching GAIA Agent Evaluation Interface...")
-    demo.launch(debug=True, share=True)
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    demo.launch(debug=True, share=False)

requirements.txt

@@ -1,20 +1,9 @@
 gradio
 requests
-smolagents
-langgraph
-llama-index
-litellm
-pandas
-requests
-youtube-transcript-api
-openai-whisper
-SPARQLWrapper
-python-chess
-PyPDF2
-Pillow
-beautifulsoup4
-numpy
-sympy
-openai
 smolagents[openai]
-python-dotenv
+smolagents[audio]
+smolagents
+wikipedia-api
+transformers
+smolagents[transformers]
+tabulate