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| from langchain.tools import tool | |
| import datetime | |
| import requests | |
| import openai | |
| import os | |
| import tempfile | |
| from urllib.parse import urlparse | |
| from openai import OpenAI | |
| def current_date(_) -> str : | |
| """ Returns the current date in YYYY-MM-DD format """ | |
| return datetime.datetime.now().strftime("%Y-%m-%d") | |
| def day_of_week(_) -> str : | |
| """ Returns the current day of the week (e.g., Monday, Tuesday) """ | |
| return datetime.datetime.now().strftime("%A") | |
| 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] | |
| def transcribe_audio(audio_input: str) -> str: | |
| """ | |
| Transcribes an audio file from a local file or a URL | |
| Args: | |
| audio_input (str): A local file path or a direct URL to the audio file (.mp3, .m4a, etc.) | |
| Returns: | |
| str: The transcribed text from the audio. | |
| """ | |
| try: | |
| # Detects if audio_input is a URL | |
| is_url = audio_input.startswith("http://") or audio_input.startswith("https://") | |
| if is_url: | |
| parsed = urlparse(audio_input) # breaks down the URL into components (scheme, netloc, path, params, etc.) | |
| extension = os.path.splitext(parsed.path)[1] or ".mp3" # get the actual file extension from the URL path or define it to .mp3 if no extension is found | |
| # Download to temporary file | |
| with tempfile.NamedTemporaryFile(suffix=extension, delete=False) as tmp_file: # creates a temporary file | |
| response = requests.get(audio_input) # downloads the content | |
| response.raise_for_status() # checks if the http request was successful | |
| tmp_file.write(response.content) # saves the file to disk | |
| file_path = tmp_file.name | |
| else: | |
| file_path = audio_input | |
| # Transcribing audio using OpenAI Whisper | |
| client = OpenAI() | |
| with open(file_path, "rb") as audio_file: # opens the audio file from disk in binary mode (rb); the "with" block ensures the file is automatically closed afterward | |
| transcription = client.audio.transcriptions.create( | |
| model="whisper-1", | |
| file=audio_file | |
| ) | |
| return transcription.text | |
| except Exception as e: | |
| return f"Transcription failed: {e}" |