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app.py

@@ -1,454 +1,204 @@
-import os
-import gradio as gr
-import requests
-import pandas as pd
-from smolagents import CodeAgent, DuckDuckGoSearchTool, OpenAIServerModel, Tool, PythonInterpreterTool
-
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# Custom file reading tool
-class FileReadTool(Tool):
-    name = "file_reader"
-    description = """
-    This tool reads the content of text files.
-    It's useful for processing plain text files (.txt, .csv, .json, etc).
-    """
-    inputs = {
-        "file_path": {
-            "type": "string",
-            "description": "The path to the file to read",
-        }
-    }
-    output_type = "string"
-    
-    def forward(self, file_path: str) -> str:
-        """
-        Reads the content of the given file.
-        """
-        try:
-            # Check if the file exists
-            if not os.path.exists(file_path):
-                return f"Error: File not found at {file_path}"
-                
-            # Read the file
-            with open(file_path, 'r', encoding='utf-8') as file:
-                content = file.read()
-                
-            # If the content is too long, truncate it
-            if len(content) > 10000:
-                content = content[:10000] + "...\n[Text truncated due to length]"
-                
-            return content or "File is empty."
-            
-        except Exception as e:
-            return f"Error reading file: {str(e)}"
-
-class PDFReaderTool(Tool):
-    name = "pdf_reader"
-    description = """
-    This tool extracts text content from PDF files.
-    It's useful for reading research papers, reports, or other document types.
-    """
-    inputs = {
-        "pdf_path": {
-            "type": "string",
-            "description": "The path to the PDF file to read",
-        }
-    }
-    output_type = "string"
-    
-    def forward(self, pdf_path: str) -> str:
-        """
-        Extracts text from the given PDF file.
-        """
-        try:
-            # Check if the file exists
-            if not os.path.exists(pdf_path):
-                return f"Error: PDF file not found at {pdf_path}"
-                
-            import PyPDF2
-            
-            # Open the PDF file
-            with open(pdf_path, 'rb') as file:
-                # Create a PDF reader object
-                pdf_reader = PyPDF2.PdfReader(file)
-                
-                # Get the number of pages
-                num_pages = len(pdf_reader.pages)
-                
-                # Extract text from all pages
-                text = ""
-                for page_num in range(num_pages):
-                    page = pdf_reader.pages[page_num]
-                    text += page.extract_text() + "\n\n"
-                
-                # If the text is too long, truncate it
-                if len(text) > 10000:
-                    text = text[:10000] + "...\n[Text truncated due to length]"
-                
-                return text or "No text could be extracted from the PDF."
-                
-        except Exception as e:
-            return f"Error reading PDF: {str(e)}"
-
-class ExcelReaderTool(Tool):
-    name = "excel_reader"
-    description = """
-    This tool reads and processes Excel files (.xlsx, .xls).
-    It can extract data, calculate statistics, and perform data analysis on spreadsheets.
-    """
-    inputs = {
-        "excel_path": {
-            "type": "string",
-            "description": "The path to the Excel file to read",
-        },
-        "sheet_name": {
-            "type": "string",
-            "description": "The name of the sheet to read (optional, defaults to first sheet)",
-            "nullable": True
-        }
-    }
-    output_type = "string"
-    
-    def forward(self, excel_path: str, sheet_name: str = None) -> str:
-        """
-        Reads and processes the given Excel file.
-        """
-        try:
-            # Check if the file exists
-            if not os.path.exists(excel_path):
-                return f"Error: Excel file not found at {excel_path}"
-                
-            import pandas as pd
-            
-            # Read the Excel file
-            if sheet_name:
-                df = pd.read_excel(excel_path, sheet_name=sheet_name)
-            else:
-                df = pd.read_excel(excel_path)
-                
-            # Get basic info about the data
-            info = {
-                "shape": df.shape,
-                "columns": list(df.columns),
-                "dtypes": df.dtypes.to_dict(),
-                "head": df.head(5).to_dict()
-            }
-            
-            # Return formatted info
-            result = f"Excel file: {excel_path}\n"
-            result += f"Shape: {info['shape'][0]} rows × {info['shape'][1]} columns\n\n"
-            result += "Columns:\n"
-            for col in info['columns']:
-                result += f"- {col} ({info['dtypes'].get(col)})\n"
-            
-            result += "\nPreview (first 5 rows):\n"
-            result += df.head(5).to_string()
-            
-            return result
-            
-        except Exception as e:
-            return f"Error reading Excel file: {str(e)}"
-
-class ImageAnalysisTool(Tool):
-    name = "image_analysis"
-    description = """
-    This tool analyzes an image and extracts relevant information from it.
-    It can describe image content, extract text from images, identify objects, etc.
-    """
-    inputs = {
-        "image_path": {
-            "type": "string",
-            "description": "The path to the image file to analyze",
-        }
-    }
-    output_type = "string"
-    
-    def forward(self, image_path: str) -> str:
-        """
-        Analyzes the given image and returns relevant information using OpenAI's ChatGPT API.
-        """
-        try:
-            # Check if the file exists
-            if not os.path.exists(image_path):
-                return f"Error: Image file not found at {image_path}"
-                
-            import requests
-            import base64
-            import json
-            from PIL import Image
-            
-            # Load the image
-            with open(image_path, "rb") as image_file:
-                image_bytes = image_file.read()
-                
-            # Convert to base64 for OpenAI API
-            encoded_image = base64.b64encode(image_bytes).decode('utf-8')
-            
-            # Get API key from environment
-            api_key = os.getenv('OPENAI_API_KEY', '')
-            if not api_key:
-                return "OpenAI API key not configured. Please add the OPENAI_API_KEY to your environment variables."
-            
-            # Prepare the API request for ChatGPT with vision capabilities
-            api_url = "https://api.openai.com/v1/chat/completions"
-            headers = {
-                "Content-Type": "application/json",
-                "Authorization": f"Bearer {api_key}"
-            }
-            
-            payload = {
-                "model": "gpt-4o-mini-2024-07-18",  # Vision-capable model
-                "messages": [
-                    {
-                        "role": "user",
-                        "content": [
-                            {
-                                "type": "text",
-                                "text": "Analyze this image in detail. Describe what you see, including main subjects, activities, background elements, colors, and any text visible in the image. If there's text in the image, please extract it."
-                            },
-                            {
-                                "type": "image_url",
-                                "image_url": {
-                                    "url": f"data:image/jpeg;base64,{encoded_image}"
-                                }
-                            }
-                        ]
-                    }
-                ],
-                "max_tokens": 500
-            }
-            
-            response = requests.post(
-                api_url,
-                headers=headers,
-                json=payload
-            )
-            
-            if response.status_code != 200:
-                return f"Error: OpenAI API returned status code {response.status_code}. Details: {response.text}"
-                
-            result = response.json()
-            
-            # Extract the response content
-            if "choices" in result and len(result["choices"]) > 0:
-                analysis = result["choices"][0]["message"]["content"]
-                return f"Image analysis result: {analysis}"
-            else:
-                return f"Error: Unexpected response format from OpenAI API: {result}"
-                
-        except Exception as e:
-            return f"Error analyzing image: {str(e)}"
-
-# --- Basic Agent Definition ---
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-        # Initialize the model
-        model = OpenAIServerModel(model_id="openai/gpt-4o-mini",api_key=os.environ["API_KEY"],api_base="https://models.github.ai/inference")
-        
-        # Initialize tools
-        self.tools = [
-            DuckDuckGoSearchTool(),  # Built-in web search tool
-            FileReadTool(),          # Custom file reader
-            PDFReaderTool(),         # PDF reader
-            ExcelReaderTool(),       # Excel reader
-            ImageAnalysisTool(),     # Image analysis
-                   # Code execution
-        ]
-        
-        # Initialize Agent
-        self.agent = CodeAgent(
-            model=model,
-            tools=self.tools,
-            add_base_tools=True  # Add basic tools like math
-        )
-        
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        try:
-            answer = self.agent.run(question)
-            print(f"Agent returned answer (first 50 chars): {answer[:50]}...")
-            return answer
-        except Exception as e:
-            error_msg = f"Error running agent: {str(e)}"
-            print(error_msg)
-            return f"I encountered an issue while processing your question: {str(e)}"
-
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
-    """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
-
-    if profile:
-        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
-
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-
-    # 1. Instantiate 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
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
-
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
-    try:
-        response = requests.get(questions_url, timeout=15)
-        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
-    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
-    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}")
-            continue
-        try:
-            print(f"Processing task {task_id}: {question_text[:50]}...")
-            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})
-            print(f"Completed task {task_id}")
-        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}"})
-
-    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)
-
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
-    try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        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
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with 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
-    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
-    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
-    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
-
-
-# --- Build Gradio Interface using Blocks ---
-with gr.Blocks() as demo:
-    gr.Markdown("# Advanced Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **Instructions:**
-
-        1. Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-        2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
-        ---
-        **Note:**
-        Once you click on the "submit" button, it may take quite some time as the agent processes all the questions.
-        The agent is using SmolaAgents with multiple tools including web search, file processing, and code execution.
-        """
-    )
-
-    gr.LoginButton()
-
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, 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]
-    )
-
-if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-    else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-
-    print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Advanced Agent Evaluation...")
+import os
+import gradio as gr
+import requests
+import pandas as pd
+from dotenv import load_dotenv
+from gemini_agent import GeminiAgent
+
+# Constants
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+class BasicAgent:
+    def __init__(self):
+        print("Initializing the BasicAgent")
+        
+        # Get Gemini API key
+        api_key = os.getenv('GOOGLE_API_KEY')
+        if not api_key:
+            raise ValueError("GOOGLE_API_KEY environment variable not set.")
+            
+        # Initialize GeminiAgent
+        self.agent = GeminiAgent(api_key=api_key)
+        print("GeminiAgent initialized successfully")
+
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        final_answer = self.agent.run(question)
+        print(f"Agent returning fixed answer: {final_answer}")
+        return final_answer
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    and displays the results.
+    """
+    # --- Determine HF Space Runtime URL and Repo URL ---
+    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+
+    if profile:
+        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
+
+    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)
+    try:
+        agent = BasicAgent()
+    except Exception as e:
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
+    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(agent_code)
+
+    # 2. Fetch Questions
+    print(f"Fetching questions from: {questions_url}")
+    try:
+        response = requests.get(questions_url, timeout=15)
+        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
+    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
+    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}")
+            continue
+        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})
+        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}"})
+
+    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)
+
+    # 5. Submit
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    try:
+        response = requests.post(submit_url, json=submission_data, timeout=60)
+        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
+    except requests.exceptions.HTTPError as e:
+        error_detail = f"Server responded with 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
+    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
+    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
+    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
+
+
+# --- Build Gradio Interface using Blocks ---
+with gr.Blocks() as demo:
+    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown(
+        """
+        **Instructions:**
+
+        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
+        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+
+        ---
+        **Disclaimers:**
+        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
+        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+        """
+    )
+
+    gr.LoginButton()
+
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
+
+    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+    # Removed max_rows=10 from DataFrame constructor
+    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
+
+if __name__ == "__main__":
+    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    # Check for SPACE_HOST and SPACE_ID at startup for information
+    space_host_startup = os.getenv("SPACE_HOST")
+    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
+
+    if space_host_startup:
+        print(f"✅ SPACE_HOST found: {space_host_startup}")
+        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
+    else:
+        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+
+    if space_id_startup: # Print repo URLs if SPACE_ID is found
+        print(f"✅ SPACE_ID found: {space_id_startup}")
+        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
+        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
+    else:
+        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+
+    print("-"*(60 + len(" App Starting ")) + "\n")
+
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
     demo.launch(debug=True, share=False)

gemini_agent.py

@@ -0,0 +1,660 @@
+import os
+import tempfile
+import time
+import re
+import json
+from typing import List, Optional, Dict, Any
+from urllib.parse import urlparse
+import requests
+import yt_dlp
+from bs4 import BeautifulSoup
+from difflib import SequenceMatcher
+
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_community.utilities import DuckDuckGoSearchAPIWrapper, WikipediaAPIWrapper
+from langchain.agents import Tool, AgentExecutor, ConversationalAgent, initialize_agent, AgentType
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import MessagesPlaceholder
+from langchain.tools import BaseTool, Tool, tool
+from google.generativeai.types import HarmCategory, HarmBlockThreshold
+from PIL import Image
+import google.generativeai as genai
+from pydantic import Field
+
+from smolagents import WikipediaSearchTool
+
+class SmolagentToolWrapper(BaseTool):
+    """Wrapper for smolagents tools to make them compatible with LangChain."""
+    
+    wrapped_tool: object = Field(description="The wrapped smolagents tool")
+    
+    def __init__(self, tool):
+        """Initialize the wrapper with a smolagents tool."""
+        super().__init__(
+            name=tool.name,
+            description=tool.description,
+            return_direct=False,
+            wrapped_tool=tool
+        )
+
+    def _run(self, query: str) -> str:
+        """Use the wrapped tool to execute the query."""
+        try:
+            # For WikipediaSearchTool
+            if hasattr(self.wrapped_tool, 'search'):
+                return self.wrapped_tool.search(query)
+            # For DuckDuckGoSearchTool and others
+            return self.wrapped_tool(query)
+        except Exception as e:
+            return f"Error using tool: {str(e)}"
+    
+    def _arun(self, query: str) -> str:
+        """Async version - just calls sync version since smolagents tools don't support async."""
+        return self._run(query)
+
+class WebSearchTool:
+    def __init__(self):
+        self.last_request_time = 0
+        self.min_request_interval = 2.0  # Minimum time between requests in seconds
+        self.max_retries = 10
+
+    def search(self, query: str, domain: Optional[str] = None) -> str:
+        """Perform web search with rate limiting and retries."""
+        for attempt in range(self.max_retries):
+            # Implement rate limiting
+            current_time = time.time()
+            time_since_last = current_time - self.last_request_time
+            if time_since_last < self.min_request_interval:
+                time.sleep(self.min_request_interval - time_since_last)
+            
+            try:
+                # Make the search request
+                results = self._do_search(query, domain)
+                self.last_request_time = time.time()
+                return results
+            except Exception as e:
+                if "202 Ratelimit" in str(e):
+                    if attempt < self.max_retries - 1:
+                        # Exponential backoff
+                        wait_time = (2 ** attempt) * self.min_request_interval
+                        time.sleep(wait_time)
+                        continue
+                return f"Search failed after {self.max_retries} attempts: {str(e)}"
+        
+        return "Search failed due to rate limiting"
+
+    def _do_search(self, query: str, domain: Optional[str] = None) -> str:
+        """Perform the actual search request."""
+        try:
+            # Construct search URL
+            base_url = "https://html.duckduckgo.com/html"
+            params = {"q": query}
+            if domain:
+                params["q"] += f" site:{domain}"
+
+            # Make request with increased timeout
+            response = requests.get(base_url, params=params, timeout=10)
+            response.raise_for_status()
+
+            if response.status_code == 202:
+                raise Exception("202 Ratelimit")
+
+            # Extract search results
+            results = []
+            soup = BeautifulSoup(response.text, 'html.parser')
+            for result in soup.find_all('div', {'class': 'result'}):
+                title = result.find('a', {'class': 'result__a'})
+                snippet = result.find('a', {'class': 'result__snippet'})
+                if title and snippet:
+                    results.append({
+                        'title': title.get_text(),
+                        'snippet': snippet.get_text(),
+                        'url': title.get('href')
+                    })
+
+            # Format results
+            formatted_results = []
+            for r in results[:10]:  # Limit to top 5 results
+                formatted_results.append(f"[{r['title']}]({r['url']})\n{r['snippet']}\n")
+
+            return "## Search Results\n\n" + "\n".join(formatted_results)
+
+        except requests.RequestException as e:
+            raise Exception(f"Search request failed: {str(e)}")
+
+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."
+
+
+def download_file_from_url(url: str, filename: Optional[str] = None) -> str:
+    """
+    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:
+        # 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)}"
+
+
+def extract_text_from_image(image_path: str) -> str:
+    """
+    Extract text from an image using pytesseract (if available).
+    
+    Args:
+        image_path: Path to the image file
+        
+    Returns:
+        Extracted text or error message
+    """
+    try:
+        # Try to import pytesseract
+        import pytesseract
+        from PIL import Image
+        
+        # Open the image
+        image = Image.open(image_path)
+        
+        # Extract text
+        text = pytesseract.image_to_string(image)
+        
+        return f"Extracted text from image:\n\n{text}"
+    except ImportError:
+        return "Error: pytesseract is not installed. Please install it with 'pip install pytesseract' and ensure Tesseract OCR is installed on your system."
+    except Exception as e:
+        return f"Error extracting text from image: {str(e)}"
+
+
+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)}"
+
+@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)}"
+
+class GeminiAgent:
+    def __init__(self, api_key: str, model_name: str = "gemini-2.0-flash"):
+        # Suppress warnings
+        import warnings
+        warnings.filterwarnings("ignore", category=UserWarning)
+        warnings.filterwarnings("ignore", category=DeprecationWarning)
+        warnings.filterwarnings("ignore", message=".*will be deprecated.*")
+        warnings.filterwarnings("ignore", "LangChain.*")
+        
+        self.api_key = api_key
+        self.model_name = model_name
+        
+        # Configure Gemini
+        genai.configure(api_key=api_key)
+        
+        # Initialize the LLM
+        self.llm = self._setup_llm()
+        
+        # Setup tools
+        self.tools = [
+            SmolagentToolWrapper(WikipediaSearchTool()),
+            Tool(
+                name="analyze_video",
+                func=self._analyze_video,
+                description="Analyze YouTube video content directly"
+            ),
+            Tool(
+                name="analyze_image",
+                func=self._analyze_image,
+                description="Analyze image content"
+            ),
+            Tool(
+                name="analyze_table",
+                func=self._analyze_table,
+                description="Analyze table or matrix data"
+            ),
+            Tool(
+                name="analyze_list",
+                func=self._analyze_list,
+                description="Analyze and categorize list items"
+            ),
+            Tool(
+                name="web_search",
+                func=self._web_search,
+                description="Search the web for information"
+            )
+        ]
+        
+        # Setup memory
+        self.memory = ConversationBufferMemory(
+            memory_key="chat_history",
+            return_messages=True
+        )
+        
+        # Initialize agent
+        self.agent = self._setup_agent()
+       
+
+    def run(self, query: str) -> str:
+        """Run the agent on a query with incremental retries."""
+        max_retries = 3
+        base_sleep = 1  # Start with 1 second sleep
+        
+        for attempt in range(max_retries):
+            try:
+
+                # If no match found in answer bank, use the agent
+                response = self.agent.run(query)
+                return response
+
+            except Exception as e:
+                sleep_time = base_sleep * (attempt + 1)  # Incremental sleep: 1s, 2s, 3s
+                if attempt < max_retries - 1:
+                    print(f"Attempt {attempt + 1} failed. Retrying in {sleep_time} seconds...")
+                    time.sleep(sleep_time)
+                    continue
+                return f"Error processing query after {max_retries} attempts: {str(e)}"
+
+        print("Agent processed all queries!")
+
+    def _clean_response(self, response: str) -> str:
+        """Clean up the response from the agent."""
+        # Remove any tool invocation artifacts
+        cleaned = re.sub(r'> Entering new AgentExecutor chain...|> Finished chain.', '', response)
+        cleaned = re.sub(r'Thought:.*?Action:.*?Action Input:.*?Observation:.*?\n', '', cleaned, flags=re.DOTALL)
+        return cleaned.strip()
+
+    def run_interactive(self):
+        print("AI Assistant Ready! (Type 'exit' to quit)")
+        
+        while True:
+            query = input("You: ").strip()
+            if query.lower() == 'exit':
+                print("Goodbye!")
+                break
+            
+            print("Assistant:", self.run(query))
+
+    def _web_search(self, query: str, domain: Optional[str] = None) -> str:
+        """Perform web search with rate limiting and retries."""
+        try:
+            # Use DuckDuckGo API wrapper for more reliable results
+            search = DuckDuckGoSearchAPIWrapper(max_results=5)
+            results = search.run(f"{query} {f'site:{domain}' if domain else ''}")
+            
+            if not results or results.strip() == "":
+                return "No search results found."
+                
+            return results
+
+        except Exception as e:
+            return f"Search error: {str(e)}"
+
+    def _analyze_video(self, url: str) -> str:
+        """Analyze video content using Gemini's video understanding capabilities."""
+        try:
+            # Validate URL
+            parsed_url = urlparse(url)
+            if not all([parsed_url.scheme, parsed_url.netloc]):
+                return "Please provide a valid video URL with http:// or https:// prefix."
+            
+            # Check if it's a YouTube URL
+            if 'youtube.com' not in url and 'youtu.be' not in url:
+                return "Only YouTube videos are supported at this time."
+
+            try:
+                # Configure yt-dlp with minimal extraction
+                ydl_opts = {
+                    'quiet': True,
+                    'no_warnings': True,
+                    'extract_flat': True,
+                    'no_playlist': True,
+                    'youtube_include_dash_manifest': False
+                }
+
+                with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+                    try:
+                        # Try basic info extraction
+                        info = ydl.extract_info(url, download=False, process=False)
+                        if not info:
+                            return "Could not extract video information."
+
+                        title = info.get('title', 'Unknown')
+                        description = info.get('description', '')
+                        
+                        # Create a detailed prompt with available metadata
+                        prompt = f"""Please analyze this YouTube video:
+Title: {title}
+URL: {url}
+Description: {description}
+
+Please provide a detailed analysis focusing on:
+1. Main topic and key points from the title and description
+2. Expected visual elements and scenes
+3. Overall message or purpose
+4. Target audience"""
+
+                        # Use the LLM with proper message format
+                        messages = [HumanMessage(content=prompt)]
+                        response = self.llm.invoke(messages)
+                        return response.content if hasattr(response, 'content') else str(response)
+
+                    except Exception as e:
+                        if 'Sign in to confirm' in str(e):
+                            return "This video requires age verification or sign-in. Please provide a different video URL."
+                        return f"Error accessing video: {str(e)}"
+
+            except Exception as e:
+                return f"Error extracting video info: {str(e)}"
+
+        except Exception as e:
+            return f"Error analyzing video: {str(e)}"
+
+    def _analyze_table(self, table_data: str) -> str:
+        """Analyze table or matrix data."""
+        try:
+            if not table_data or not isinstance(table_data, str):
+                return "Please provide valid table data for analysis."
+
+            prompt = f"""Please analyze this table:
+
+{table_data}
+
+Provide a detailed analysis including:
+1. Structure and format
+2. Key patterns or relationships
+3. Notable findings
+4. Any mathematical properties (if applicable)"""
+
+            messages = [HumanMessage(content=prompt)]
+            response = self.llm.invoke(messages)
+            return response.content if hasattr(response, 'content') else str(response)
+
+        except Exception as e:
+            return f"Error analyzing table: {str(e)}"
+
+    def _analyze_image(self, image_data: str) -> str:
+        """Analyze image content."""
+        try:
+            if not image_data or not isinstance(image_data, str):
+                return "Please provide a valid image for analysis."
+
+            prompt = f"""Please analyze this image:
+
+{image_data}
+
+Focus on:
+1. Visual elements and objects
+2. Colors and composition
+3. Text or numbers (if present)
+4. Overall context and meaning"""
+
+            messages = [HumanMessage(content=prompt)]
+            response = self.llm.invoke(messages)
+            return response.content if hasattr(response, 'content') else str(response)
+
+        except Exception as e:
+            return f"Error analyzing image: {str(e)}"
+
+    def _analyze_list(self, list_data: str) -> str:
+        """Analyze and categorize list items."""
+        if not list_data:
+            return "No list data provided."
+        try:
+            items = [x.strip() for x in list_data.split(',')]
+            if not items:
+                return "Please provide a comma-separated list of items."
+            # Add list analysis logic here
+            return "Please provide the list items for analysis."
+        except Exception as e:
+            return f"Error analyzing list: {str(e)}"
+
+    def _setup_llm(self):
+        """Set up the language model."""
+        # Set up model with video capabilities
+        generation_config = {
+            "temperature": 0.0,
+            "max_output_tokens": 2000,
+            "candidate_count": 1,
+        }
+        
+        safety_settings = {
+            HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+            HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+            HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+            HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+        }
+        
+        return ChatGoogleGenerativeAI(
+            model="gemini-2.0-flash",
+            google_api_key=self.api_key,
+            temperature=0,
+            max_output_tokens=2000,
+            generation_config=generation_config,
+            safety_settings=safety_settings,
+            system_message=SystemMessage(content=(
+                "You are a precise AI assistant that helps users find information and analyze content. "
+                "You can directly understand and analyze YouTube videos, images, and other content. "
+                "When analyzing videos, focus on relevant details like dialogue, text, and key visual elements. "
+                "For lists, tables, and structured data, ensure proper formatting and organization. "
+                "If you need additional context, clearly explain what is needed."
+            ))
+        )
+        
+    def _setup_agent(self) -> AgentExecutor:
+        """Set up the agent with tools and system message."""
+        
+        # Define the system message template
+        PREFIX = """You are a helpful AI assistant that can use various tools to answer questions and analyze content. You have access to tools for web search, Wikipedia lookup, and multimedia analysis.
+
+TOOLS:
+------
+You have access to the following tools:"""
+
+        FORMAT_INSTRUCTIONS = """To use a tool, use the following format:
+
+Thought: Do I need to use a tool? Yes
+Action: the action to take, should be one of [{tool_names}]
+Action Input: the input to the action
+Observation: the result of the action
+
+When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:
+
+Thought: Do I need to use a tool? No
+Final Answer: [your response here]
+
+Begin! Remember to ALWAYS include 'Thought:', 'Action:', 'Action Input:', and 'Final Answer:' in your responses."""
+
+        SUFFIX = """Previous conversation history:
+{chat_history}
+
+New question: {input}
+{agent_scratchpad}"""
+
+        # Create the base agent
+        agent = ConversationalAgent.from_llm_and_tools(
+            llm=self.llm,
+            tools=self.tools,
+            prefix=PREFIX,
+            format_instructions=FORMAT_INSTRUCTIONS,
+            suffix=SUFFIX,
+            input_variables=["input", "chat_history", "agent_scratchpad", "tool_names"],
+            handle_parsing_errors=True
+        )
+
+        # Initialize agent executor with custom output handling
+        return AgentExecutor.from_agent_and_tools(
+            agent=agent,
+            tools=self.tools,
+            memory=self.memory,
+            max_iterations=5,
+            verbose=True,
+            handle_parsing_errors=True,
+            return_only_outputs=True  # This ensures we only get the final output
+        )
+
+@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)}"
+
+@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)}"

main_agent.py

@@ -0,0 +1,492 @@
+from smolagents import (
+    CodeAgent, 
+    DuckDuckGoSearchTool, 
+    HfApiModel, 
+    LiteLLMModel,
+    OpenAIServerModel,
+    PythonInterpreterTool,
+    tool,
+    InferenceClientModel
+)
+from typing import List, Dict, Any, Optional
+import os
+import tempfile
+import re
+import json
+import requests
+from urllib.parse import urlparse
+
+@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:
+    """
+    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:
+        # 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)}"
+
+@tool
+def extract_text_from_image(image_path: str) -> str:
+    """
+    Extract text from an image using pytesseract (if available).
+    
+    Args:
+        image_path: Path to the image file
+        
+    Returns:
+        Extracted text or error message
+    """
+    try:
+        # Try to import pytesseract
+        import pytesseract
+        from PIL import Image
+        
+        # Open the image
+        image = Image.open(image_path)
+        
+        # Extract text
+        text = pytesseract.image_to_string(image)
+        
+        return f"Extracted text from image:\n\n{text}"
+    except ImportError:
+        return "Error: pytesseract is not installed. Please install it with 'pip install pytesseract' and ensure Tesseract OCR is installed on your system."
+    except Exception as e:
+        return f"Error extracting text from image: {str(e)}"
+
+@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)}"
+
+@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)}"
+
+class GAIAAgent:
+    def __init__(
+        self, 
+        model_type: str = "HfApiModel", 
+        model_id: Optional[str] = None,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        temperature: float = 0.2,
+        executor_type: str = "local",  # Changed from use_e2b to executor_type
+        additional_imports: List[str] = None,
+        additional_tools: List[Any] = None,
+        system_prompt: Optional[str] = None,  # We'll still accept this parameter but not use it directly
+        verbose: bool = False,
+        provider: Optional[str] = None,  # Add provider for InferenceClientModel
+        timeout: Optional[int] = None   # Add timeout for InferenceClientModel
+    ):
+        """
+        Initialize a GAIAAgent with specified configuration
+        
+        Args:
+            model_type: Type of model to use (HfApiModel, LiteLLMModel, OpenAIServerModel, InferenceClientModel)
+            model_id: ID of the model to use
+            api_key: API key for the model provider
+            api_base: Base URL for API calls
+            temperature: Temperature for text generation
+            executor_type: Type of executor for code execution ('local' or 'e2b')
+            additional_imports: Additional Python modules to allow importing
+            additional_tools: Additional tools to provide to the agent
+            system_prompt: Custom system prompt to use (not directly used, kept for backward compatibility)
+            verbose: Enable verbose logging
+            provider: Provider for InferenceClientModel (e.g., "hf-inference")
+            timeout: Timeout in seconds for API calls
+        """
+        # Set verbosity
+        self.verbose = verbose
+        self.system_prompt = system_prompt  # Store for potential future use
+        
+        # Initialize model based on configuration
+        if model_type == "HfApiModel":
+            if api_key is None:
+                api_key = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+                if not api_key:
+                    raise ValueError("No Hugging Face token provided. Please set HUGGINGFACEHUB_API_TOKEN environment variable or pass api_key parameter.")
+            
+            if self.verbose:
+                print(f"Using Hugging Face token: {api_key[:5]}...")
+                
+            self.model = HfApiModel(
+                model_id=model_id or "meta-llama/Llama-3-70B-Instruct",
+                token=api_key,
+                temperature=temperature
+            )
+        elif model_type == "InferenceClientModel":
+            if api_key is None:
+                api_key = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+                if not api_key:
+                    raise ValueError("No Hugging Face token provided. Please set HUGGINGFACEHUB_API_TOKEN environment variable or pass api_key parameter.")
+            
+            if self.verbose:
+                print(f"Using Hugging Face token: {api_key[:5]}...")
+                
+            self.model = InferenceClientModel(
+                model_id=model_id or "meta-llama/Llama-3-70B-Instruct",
+                provider=provider or "hf-inference",
+                token=api_key,
+                timeout=timeout or 120,
+                temperature=temperature
+            )
+        elif model_type == "LiteLLMModel":
+            from smolagents import LiteLLMModel
+            self.model = LiteLLMModel(
+                model_id=model_id or "gpt-4o",
+                api_key=api_key or os.getenv("OPENAI_API_KEY"),
+                temperature=temperature
+            )
+        elif model_type == "OpenAIServerModel":
+            # Check for xAI API key and base URL first
+            xai_api_key = os.getenv("XAI_API_KEY")
+            xai_api_base = os.getenv("XAI_API_BASE")
+            
+            # If xAI credentials are available, use them
+            if xai_api_key and api_key is None:
+                api_key = xai_api_key
+                if self.verbose:
+                    print(f"Using xAI API key: {api_key[:5]}...")
+            
+            # If no API key specified, fall back to OPENAI_API_KEY
+            if api_key is None:
+                api_key = os.getenv("OPENAI_API_KEY")
+                if not api_key:
+                    raise ValueError("No OpenAI API key provided. Please set OPENAI_API_KEY or XAI_API_KEY environment variable or pass api_key parameter.")
+            
+            # If xAI API base is available and no api_base is provided, use it
+            if xai_api_base and api_base is None:
+                api_base = xai_api_base
+                if self.verbose:
+                    print(f"Using xAI API base URL: {api_base}")
+            
+            # If no API base specified but environment variable available, use it
+            if api_base is None:
+                api_base = os.getenv("AGENT_API_BASE")
+                if api_base and self.verbose:
+                    print(f"Using API base from AGENT_API_BASE: {api_base}")
+            
+            self.model = OpenAIServerModel(
+                model_id=model_id or "gpt-4o",
+                api_key=api_key,
+                api_base=api_base,
+                temperature=temperature
+            )
+        else:
+            raise ValueError(f"Unknown model type: {model_type}")
+        
+        if self.verbose:
+            print(f"Initialized model: {model_type} - {model_id}")
+        
+        # Initialize default tools
+        self.tools = [
+            DuckDuckGoSearchTool(),
+            PythonInterpreterTool(),
+            save_and_read_file,
+            download_file_from_url,
+            analyze_csv_file,
+            analyze_excel_file
+        ]
+        
+        # Add extract_text_from_image if PIL and pytesseract are available
+        try:
+            import pytesseract
+            from PIL import Image
+            self.tools.append(extract_text_from_image)
+            if self.verbose:
+                print("Added image processing tool")
+        except ImportError:
+            if self.verbose:
+                print("Image processing libraries not available")
+        
+        # Add any additional tools
+        if additional_tools:
+            self.tools.extend(additional_tools)
+            
+        if self.verbose:
+            print(f"Initialized with {len(self.tools)} tools")
+        
+        # Setup imports allowed
+        self.imports = ["pandas", "numpy", "datetime", "json", "re", "math", "os", "requests", "csv", "urllib"]
+        if additional_imports:
+            self.imports.extend(additional_imports)
+            
+        # Initialize the CodeAgent
+        executor_kwargs = {}
+        if executor_type == "e2b":
+            try:
+                # Try to import e2b dependencies to check if they're available
+                from e2b_code_interpreter import Sandbox
+                if self.verbose:
+                    print("Using e2b executor")
+            except ImportError:
+                if self.verbose:
+                    print("e2b dependencies not found, falling back to local executor")
+                executor_type = "local"  # Fallback to local if e2b is not available
+        
+        self.agent = CodeAgent(
+            tools=self.tools,
+            model=self.model,
+            additional_authorized_imports=self.imports,
+            executor_type=executor_type,
+            executor_kwargs=executor_kwargs,
+            verbosity_level=2 if self.verbose else 0
+        )
+        
+        if self.verbose:
+            print("Agent initialized and ready")
+    
+    def answer_question(self, question: str, task_file_path: Optional[str] = None) -> str:
+        """
+        Process a GAIA benchmark question and return the answer
+        
+        Args:
+            question: The question to answer
+            task_file_path: Optional path to a file associated with the question
+            
+        Returns:
+            The answer to the question
+        """
+        try:
+            if self.verbose:
+                print(f"Processing question: {question}")
+                if task_file_path:
+                    print(f"With associated file: {task_file_path}")
+            
+            # Create a context with file information if available
+            context = question
+            file_content = None
+            
+            # If there's a file, read it and include its content in the context
+            if task_file_path:
+                try:
+                    with open(task_file_path, 'r') as f:
+                        file_content = f.read()
+                    
+                    # Determine file type from extension
+                    import os
+                    file_ext = os.path.splitext(task_file_path)[1].lower()
+                    
+                    context = f"""
+Question: {question}
+
+This question has an associated file. Here is the file content:
+
+```{file_ext}
+{file_content}
+```
+
+Analyze the file content above to answer the question.
+"""
+                except Exception as file_e:
+                    context = f"""
+Question: {question}
+
+This question has an associated file at path: {task_file_path}
+However, there was an error reading the file: {file_e}
+You can still try to answer the question based on the information provided.
+"""
+            
+            # Check for special cases that need specific formatting
+            # Reversed text questions
+            if question.startswith(".") or ".rewsna eht sa" in question:
+                context = f"""
+This question appears to be in reversed text. Here's the reversed version:
+{question[::-1]}
+
+Now answer the question above. Remember to format your answer exactly as requested.
+"""
+            
+            # Add a prompt to ensure precise answers
+            full_prompt = f"""{context}
+
+When answering, provide ONLY the precise answer requested. 
+Do not include explanations, steps, reasoning, or additional text.
+Be direct and specific. GAIA benchmark requires exact matching answers.
+For example, if asked "What is the capital of France?", respond simply with "Paris".
+"""
+            
+            # Run the agent with the question
+            answer = self.agent.run(full_prompt)
+            
+            # Clean up the answer to ensure it's in the expected format
+            # Remove common prefixes that models often add
+            answer = self._clean_answer(answer)
+            
+            if self.verbose:
+                print(f"Generated answer: {answer}")
+                
+            return answer
+        except Exception as e:
+            error_msg = f"Error answering question: {e}"
+            if self.verbose:
+                print(error_msg)
+            return error_msg
+    
+    def _clean_answer(self, answer: any) -> str:
+        """
+        Clean up the answer to remove common prefixes and formatting
+        that models often add but that can cause exact match failures.
+        
+        Args:
+            answer: The raw answer from the model
+            
+        Returns:
+            The cleaned answer as a string
+        """
+        # 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
+                # Check if it's an integer value in float form (e.g., 12.0)
+                if answer.is_integer():
+                    formatted_answer = str(int(answer))
+                else:
+                    # For currency values that might need formatting
+                    if abs(answer) >= 1000:
+                        formatted_answer = f"${answer:,.2f}"
+                    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.startswith(prefix):
+                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