import io
import os
import tempfile
from typing import Optional
from urllib.parse import urlparse
import uuid
import pandas as pd
import contextlib
from langchain_core.tools import tool
import requests
from PIL import Image
import pytesseract
from transformers import pipeline


@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 (str): the path to the Excel file.
        query (str): Question about the data
    """
    try:
        # 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 Exception as e:
        return f"Error analyzing Excel file: {str(e)}"



# Load ASR pipeline once at module level (for efficiency)
asr_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=-1)

@tool
def transcribe_audio(file_path: str, query: str = "") -> str:
    """
    Transcribes speech from an audio file (e.g., .mp3 or .wav).
    
    Args:
        file_path (str): Path to the audio file.
        query (str): (Optional) Ignored; present to support LangChain tool schema.

    Returns:
        str: Transcribed text from the audio.
    """
    try:
        print(f"Transcribing: {file_path}")
        result = asr_pipeline(file_path)
        transcript = result["text"]
        return transcript.strip() if transcript.strip() else "No speech detected."
    except Exception as e:
        return f"Error transcribing audio: {str(e)}"



@tool
def execute_python_code(code: str) -> str:
    """
    Executes a Python code string and returns the output or error.
    Args:
        code (str): The Python code to execute.
    Returns:
        str: The output or error message.
    """
    local_vars = {}
    stdout = io.StringIO()
    try:
        with contextlib.redirect_stdout(stdout):
            exec(code, {}, local_vars)
        output = stdout.getvalue()
        if output.strip():
            return output.strip()
        # If code defines a variable named 'result', return its value
        if "result" in local_vars:
            return str(local_vars["result"])
        return "Code executed successfully, but produced no output."
    except Exception as e:
        return f"Error executing code: {e}"



@tool
def save_and_read_file(content: str, filename: Optional[str] = None) -> str:
    """
    Save content to a file and return the path.
    Args:
        content (str): the content to save to the file
        filename (str, optional): the name of the file. If not provided, a random name file will be created.
    """
    temp_dir = tempfile.gettempdir()
    if filename is None:
        temp_file = tempfile.NamedTemporaryFile(delete=False, dir=temp_dir)
        filepath = temp_file.name
    else:
        filepath = os.path.join(temp_dir, filename)

    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 (str): the URL of the file to download.
        filename (str, optional): the name of the file. If not provided, a random name file will be created.
    """
    try:
        # Parse URL to get filename if not provided
        if not filename:
            path = urlparse(url).path
            filename = os.path.basename(path)
            if not filename:
                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 read this file to process its contents."
    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 OCR library pytesseract (if available).
    Args:
        image_path (str): the path to the image file.
    """
    try:
        # Open the image
        image = Image.open(image_path)

        # Extract text from the image
        text = pytesseract.image_to_string(image)

        return f"Extracted text from image:\n\n{text}"
    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 (str): the path to the CSV file.
        query (str): Question about the data
    """
    try:
        # 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 Exception as e:
        return f"Error analyzing CSV file: {str(e)}"