tools.py

from langchain_core.tools import tool
import datetime
import requests
import openai
import os
import tempfile
from urllib.parse import urlparse
from openai import OpenAI

@tool
def current_date(_) -> str :
    """ Returns the current date in YYYY-MM-DD format """
    return datetime.datetime.now().strftime("%Y-%m-%d")

@tool
def day_of_week(_) -> str :
    """ Returns the current day of the week (e.g., Monday, Tuesday) """
    return datetime.datetime.now().strftime("%A")

@tool
def days_until(date_str: str) -> str :
    """ Returns the number of days from today until a given date (input format: YYYY-MM-DD) """
    try:
        future_date = datetime.datetime.strptime(date_str, "%Y-%m-%d").date()
        today = datetime.date.today()

        delta_days = (future_date - today).days
        return f"{delta_days} days until {date_str}"
    except Exception as e:
        return f"Error parsing date: {str(e)}"

datetime_tools = [current_date, day_of_week, days_until]

@tool
def transcribe_audio(audio_file: str, file_extension: str) -> str:
    """ Transcribes an audio file to text

    Args:
        audio_file (str): local file path to the audio file (.mp3, .m4a, etc.)
        file_extension (str): file extension of the audio, e.g. mp3
    
    Returns:
        str: The transcribed text from the audio.
    """
    
    try:
        response = requests.get(audio_file)  # download the audio_file
        response.raise_for_status()  # check if the http request was successful
        
        # clean file extension and save to disk
        file_extension = file_extension.replace('.','')
        filename = f'tmp.{file_extension}'
        with open(filename, 'wb') as file:  # opens a new file for writing with a name like, e.g. tmp.mp3
            file.write(response.content)    # write(w) the binary(b) contents (audio file) to disk
        
        # transcribe audio with OpenAI Whisper
        client = OpenAI()
        
        # read(r) the audio file from disk in binary(b) mode "rb"; the "with" block ensures the file is automatically closed afterward
        with open(filename, "rb") as audio_content:
            transcription = client.audio.transcriptions.create(
                model="whisper-1",
                file=audio_content
        )
        return transcription.text

    except Exception as e:
        return f"transcribe_audio failed: {e}"