tools.py

from langchain.tools import Tool
import requests
import os
from PIL import Image
import io
import base64
from langchain_community.tools import DuckDuckGoSearchRun
from typing import Optional
import json
import PyPDF2
import tempfile

# Initialize web search tool
search_tool = DuckDuckGoSearchRun()

def web_search_tool_func(query: str) -> str:
    """Searches the web for information using DuckDuckGo."""
    try:
        results = search_tool.run(query)
        return results
    except Exception as e:
        return f"Web search failed: {str(e)}"

web_search_tool = Tool(
    name="web_search",
    func=web_search_tool_func,
    description="Searches the web for current information. Use this for factual questions, recent events, or when you need to find information not in your training data."
)

def file_download_tool_func(task_id: str) -> str:
    """Downloads a file associated with a GAIA task ID."""
    try:
        api_url = "https://agents-course-unit4-scoring.hf.space"
        file_url = f"{api_url}/files/{task_id}"
        
        response = requests.get(file_url, timeout=30)
        response.raise_for_status()
        
        # Save to temporary file
        with tempfile.NamedTemporaryFile(delete=False, suffix=".tmp") as temp_file:
            temp_file.write(response.content)
            temp_path = temp_file.name
        
        # Try to determine file type and process accordingly
        content_type = response.headers.get('content-type', '').lower()
        
        if 'image' in content_type:
            return f"Image file downloaded to {temp_path}. Use image_analysis_tool to analyze it."
        elif 'pdf' in content_type:
            return process_pdf_file(temp_path)
        elif 'text' in content_type:
            with open(temp_path, 'r', encoding='utf-8') as f:
                content = f.read()
            os.unlink(temp_path)  # Clean up
            return f"Text file content:\n{content}"
        else:
            return f"File downloaded to {temp_path}. Content type: {content_type}"
            
    except Exception as e:
        return f"Failed to download file for task {task_id}: {str(e)}"

def process_pdf_file(file_path: str) -> str:
    """Process a PDF file and extract text content."""
    try:
        with open(file_path, 'rb') as file:
            pdf_reader = PyPDF2.PdfReader(file)
            text_content = ""
            
            for page_num in range(len(pdf_reader.pages)):
                page = pdf_reader.pages[page_num]
                text_content += f"\n--- Page {page_num + 1} ---\n"
                text_content += page.extract_text()
        
        os.unlink(file_path)  # Clean up
        return f"PDF content extracted:\n{text_content}"
    except Exception as e:
        return f"Failed to process PDF: {str(e)}"

file_download_tool = Tool(
    name="file_download",
    func=file_download_tool_func,
    description="Downloads and processes files associated with GAIA task IDs. Can handle images, PDFs, and text files."
)

def image_analysis_tool_func(image_path_or_description: str) -> str:
    """Analyzes images for GAIA questions. For now, returns a placeholder."""
    # This is a simplified version - in a full implementation, you'd use a vision model
    try:
        if os.path.exists(image_path_or_description):
            # Try to open and get basic info about the image
            with Image.open(image_path_or_description) as img:
                width, height = img.size
                mode = img.mode
                format_info = img.format
                
                # Clean up the temporary file
                os.unlink(image_path_or_description)
                
                return f"Image analyzed: {width}x{height} pixels, mode: {mode}, format: {format_info}. Note: This is a basic analysis. For detailed image content analysis, a vision model would be needed."
        else:
            return f"Image analysis requested for: {image_path_or_description}. Note: Full image analysis requires a vision model integration."
    except Exception as e:
        return f"Image analysis failed: {str(e)}"

image_analysis_tool = Tool(
    name="image_analysis",
    func=image_analysis_tool_func,
    description="Analyzes images to extract information. Use this for questions involving visual content."
)

def calculator_tool_func(expression: str) -> str:
    """Performs mathematical calculations safely."""
    try:
        # Basic safety check - only allow certain characters
        allowed_chars = set('0123456789+-*/().= ')
        if not all(c in allowed_chars for c in expression):
            return f"Invalid characters in expression: {expression}"
        
        # Use eval safely for basic math
        result = eval(expression)
        return f"Calculation result: {expression} = {result}"
    except Exception as e:
        return f"Calculation failed for '{expression}': {str(e)}"

calculator_tool = Tool(
    name="calculator",
    func=calculator_tool_func,
    description="Performs mathematical calculations. Use this for numerical computations and math problems."
)

def text_processor_tool_func(text: str, operation: str = "summarize") -> str:
    """Processes text for various operations like summarization, extraction, etc."""
    try:
        if operation == "summarize":
            # Simple summarization - take first and last sentences if long
            sentences = text.split('.')
            if len(sentences) > 5:
                summary = '. '.join(sentences[:2] + sentences[-2:])
                return f"Text summary: {summary}"
            else:
                return f"Text (short enough to not need summarization): {text}"
        
        elif operation == "extract_numbers":
            import re
            numbers = re.findall(r'\d+(?:\.\d+)?', text)
            return f"Numbers found in text: {numbers}"
        
        elif operation == "extract_dates":
            import re
            # Simple date pattern matching
            date_patterns = [
                r'\d{1,2}/\d{1,2}/\d{4}',  # MM/DD/YYYY
                r'\d{4}-\d{1,2}-\d{1,2}',  # YYYY-MM-DD
                r'\b\w+ \d{1,2}, \d{4}\b'  # Month DD, YYYY
            ]
            dates = []
            for pattern in date_patterns:
                dates.extend(re.findall(pattern, text))
            return f"Dates found in text: {dates}"
        
        else:
            return f"Text processing operation '{operation}' not supported. Available: summarize, extract_numbers, extract_dates"
            
    except Exception as e:
        return f"Text processing failed: {str(e)}"

text_processor_tool = Tool(
    name="text_processor",
    func=text_processor_tool_func,
    description="Processes text for various operations like summarization, number extraction, date extraction. Specify operation as second parameter."
)

# List of all tools for easy import
agent_tools = [
    web_search_tool,
    file_download_tool,
    image_analysis_tool,
    calculator_tool,
    text_processor_tool
]