import tempfile
import requests
import os

#from time import sleep
from dotenv import load_dotenv
#from urllib.parse import urlparse
from typing import Optional, List
import yt_dlp
import wikipedia

from smolagents import tool

#from google.genai import types

from PIL import Image
#from google import genai
#from dotenv import load_dotenv
#from model_provider import create_react_model, create_vision_model
#import imageio

load_dotenv(override=True)


@tool
def read_file(filepath: str ) -> str:
    """
    Used to read the content of a file.  Returns the content as a string.
    Will only work for text-based files, such as .txt files or code files.
    Do not use for audio or visual files. 
    
    Args:
        filepath (str): The path to the file to be read.
    Returns:
        str: Content of the file as a string.
    """
    try:
        with open(filepath, 'r', encoding='utf-8') as file:
            content = file.read()
        print(content)
        return content
    except FileNotFoundError:
        print(f"File not found: {filepath}")
    except IOError as e:
        print(f"Error reading 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'"
    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.  
    To use this file you need to have saved it in a location and pass that location to the function.
    The download_file_from_url tool will save it by name to tempfile.gettempdir()
    
    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.
    To use this file you need to have saved it in a location and pass that location to the function.
    The download_file_from_url tool will save it by name to tempfile.gettempdir()
    
    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)}"

import whisper

@tool
def youtube_transcribe(url: str) -> str:
    """
    Transcribes a YouTube video.  Use when you need to process the audio from a YouTube video into Text.
    Args:
        url: Url of the YouTube video
    """
    model_size: str = "base"
    # Load model
    model = whisper.load_model(model_size)
    with tempfile.TemporaryDirectory() as tmpdir:
        # Download audio
        ydl_opts = {
            'format': 'bestaudio/best',
            'outtmpl': os.path.join(tmpdir, 'audio.%(ext)s'),
            'quiet': True,
            'noplaylist': True,
            'postprocessors': [{
                'key': 'FFmpegExtractAudio',
                'preferredcodec': 'wav',
                'preferredquality': '192',
            }],
            'force_ipv4': True,
        }
        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
            info = ydl.extract_info(url, download=True)

        audio_path = next((os.path.join(tmpdir, f) for f in os.listdir(tmpdir) if f.endswith('.wav')), None)
        if not audio_path:
            raise RuntimeError("Failed to find audio")

        # Transcribe
        result = model.transcribe(audio_path)
        return result['text']

@tool
def transcribe_audio(audio_file_path: str) -> str:
    """
    Transcribes an audio file.  Use when you need to process audio data.
    DO NOT use this tool for YouTube video; use the youtube_transcribe tool to process audio data from YouTube.
    Use this tool when you have an audio file in .mp3, .wav, .aac, .ogg, .flac, .m4a, .alac or .wma
    Args:
        audio_file_path: Filepath to the audio file (str)
    """
    model_size: str = "small"
    # Load model
    model = whisper.load_model(model_size)
    result = model.transcribe(audio_file_path)
    return result['text']


@tool
def wikipedia_search(query: str) -> dict:
    """
    Search Wikipedia for a given query and return the first 10 results with summaries.
    
    Args:
        query: The search term or topic.
    Returns:
        A dictionary with a 'wiki_results' key containing formatted Wikipedia summaries.
    """
    wikipedia.set_lang("en")
    try:
        results = wikipedia.search(query, results=10)
        summaries = []
        for title in results:
            try:
                summary = wikipedia.summary(title, sentences=2)
                summaries.append(f"## {title}\n{summary}")
            except wikipedia.exceptions.DisambiguationError as e:
                summaries.append(f"## {title}\nDisambiguation required. Example options: {e.options[:3]}")
            except wikipedia.exceptions.PageError:
                summaries.append(f"## {title}\nPage not found.")
        
        formatted = "\n\n---\n\n".join(summaries)
        return {"wiki_results": formatted}
    
    except Exception as e:
        return {"wiki_results": f"Error during Wikipedia search: {str(e)}"}
    
    
#Mathematical tools
@tool
def multiply(a: float, b: float) -> float:
    """Multiply two numbers.
    Args:
        a: first number
        b: second number
    Returns: 
        Multiplication result
    """
    return a * b


@tool
def add(a: float, b: float) -> float:
    """Add two numbers.
    Args:
        a: first number
        b: second number
    Returns: 
        Addition result
    """
    return a + b


@tool
def subtract(a: float, b: float) -> float:
    """Subtract two numbers.
    Args:
        a: first number
        b: second number
    Returns: 
        Subtraction result
    """
    return a - b


@tool
def divide(a: float, b: float) -> float:
    """Divide two numbers.
    Args:
        a: first number
        b: second number
    Returns: 
        Division result
    """
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b


@tool
def modulus(a: int, b: int) -> int:
    """Get the modulus of two numbers.
    Args:
        a: first number
        b: second number
    Returns:
        Modulus result
    """
    return a % b


@tool
def convert_units(value: float, from_unit: str, to_unit: str) -> float:
    """
    Converts a value from one unit to another.

    Args:
        value: The numerical value to convert.
        from_unit: The original unit (e.g. 'miles', 'kg', 'celsius').
        to_unit: The target unit (e.g. 'kilometers', 'lb', 'fahrenheit').

    Supported conversions:
        - miles <-> kilometers
        - kilograms <-> pounds
        - celsius <-> fahrenheit

    Returns:
        The converted value result.
    """
    conversions = {
        ("miles", "kilometers"): lambda v: v * 1.60934,
        ("kilometers", "miles"): lambda v: v / 1.60934,
        ("kilograms", "pounds"): lambda v: v * 2.20462,
        ("pounds", "kilograms"): lambda v: v / 2.20462,
        ("celsius", "fahrenheit"): lambda v: (v * 9/5) + 32,
        ("fahrenheit", "celsius"): lambda v: (v - 32) * 5/9,
    }

    key = (from_unit.lower(), to_unit.lower())
    if key not in conversions:
        raise ValueError(f"Conversion from {from_unit} to {to_unit} not supported.")

    return conversions[key](value)