import os
import tempfile

import requests
from google import genai
from google.genai import types
from smolagents import tool


@tool
def download_file_of_task_id(task_id: str, file_name: str) -> str:
    """
    Download a file associated with a specific task ID and save it to a temporary location.
    Args:
        task_id (str): The unique identifier of the task associated with the file to download.
        file_name (str): The name to assign to the downloaded file.
    Returns:
        str: Path to the downloaded file or an error message if the download fails.
    """

    try:
        # Create temporary file
        temp_dir = tempfile.gettempdir()
        filepath = os.path.join(temp_dir, file_name)

        # Download the file
        response = requests.get(f"https://agents-course-unit4-scoring.hf.space/files/{task_id}",
                                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 filepath
    except Exception as e:
        return f"Error downloading file: {e!s}"


@tool
def analyze_audio_file(path_file_audio: str, query: str) -> str:
    """
    Analyzes an MP3 audio file to answer a specific query.
    Args:
        path_file_audio (str): Path to the MP3 audio file to be analyzed.
        query (str): Question or query to analyze the content of the audio file.
    Returns:
        str: The result of the analysis of audio.
    """

    client = genai.Client(api_key=os.getenv("GOOGLE_API_KEY"))

    myfile = client.files.upload(file=path_file_audio)

    response = client.models.generate_content(
        model=os.getenv("GOOGLE_MODEL_ID"),
        contents=[f"Carefully analyze the audio to answer the question correctly.\n\n The question is {query}",
                  myfile]
    )

    return response.text


@tool
def analyze_youtube_video(url_youtube_video: str, query: str) -> str:
    """
    Analyzes a YouTube video using the provided query.
    Args:
        url_youtube_video (str): URL of the YouTube video to analyze.
        query (str): Query or question to analyze the content of the video.
    Returns:
        str: Result of the video analysis.
    """

    client = genai.Client(api_key=os.getenv("GOOGLE_API_KEY"))

    response = client.models.generate_content(
        model=f"models/{os.getenv('GOOGLE_MODEL_ID')}",
        contents=types.Content(
            parts=[
                types.Part(
                    file_data=types.FileData(file_uri=url_youtube_video)
                ),
                types.Part(text=f"Carefully analyze each frame of the video to answer the question correctly.\n\n The question is {query}")
            ]
        )
    )

    return response.text


@tool
def analyze_image_file(path_file_image: str, query: str) -> str:
    """
    Analyzes an image file to answer a specific query.
    Args:
        path_file_image (str): Path to the image file to be analyzed.
        query (str): Question or query to analyze the content of the image file.
    Returns:
        str: The result of the analysis of audio.
    """

    client = genai.Client(api_key=os.getenv("GOOGLE_API_KEY"))

    myfile = client.files.upload(file=path_file_image)

    response = client.models.generate_content(
        model=os.getenv('GOOGLE_MODEL_ID'),
        contents=[myfile,
                  f"Carefully analyze the image file and think to answer the question correctly.\n\n The question is {query}"]
    )

    return response.text


@tool
def analyze_xlsx_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: {e!s}"