Update agent.py

agent.py

@@ -25,169 +25,7 @@ from langchain_google_genai import ChatGoogleGenerativeAI
 
 #load_dotenv()
 google_api_key = os.environ["GOOGLE_API_KEY"]
-#@@@@@@@@@@@@@@@@@/////////////////////////////////////////////////
-@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)}"
-
-
-
-
-
-#@@@@@@@@@@@@@@@@@@///////////////////////////////////////////////
 @tool
 def add(a: int, b: int) -> int:
   """ Add a and b """
@@ -266,8 +104,7 @@ If you are asked for a comma separated list, apply the above rules depending of
 system_message = SystemMessage(content=system_prompt)
 
 tools = [
-    add,subtract,multiply,divide,web_search,wikipedia_search,arxiv_search,
-    save_and_read_file,download_file_from_url,extract_text_from_image,analyze_csv_file,analyze_excel_file
+    add,subtract,multiply,divide,web_search,wikipedia_search,arxiv_search
 ]