tiny-rawr

import requests
from dotenv import load_dotenv
import os
import csv
from conversation_ids import conversation_ids
from bs4 import BeautifulSoup
from concurrent.futures import ThreadPoolExecutor, as_completed

load_dotenv()

tiny-rawr

import asyncio
from bleak import BleakScanner, BleakClient
import subprocess
from abc import ABC, abstractmethod

# Base class for your lights (enables polymorphism)
# So each light responds to same command but implements differently
class BLELight(ABC):
    def __init__(self, address, characteristic_uuid):
        self.address = address

tiny-rawr

# pip install youtube-transcript-api

from youtube_transcript_api import YouTubeTranscriptApi

def get_video_transcript(video_id="wk89rJpaj6w"):
    transcript_with_timestamps = YouTubeTranscriptApi.get_transcript(video_id)
    transcript = ' '.join([t['text'] for t in transcript_with_timestamps])
    return transcript

print(get_video_transcript())

tiny-rawr

def get_airtable_data(view_name=None, table_name="Members"):
    print("getting members")
    access_token = st.secrets["airtable"]["personal_access_token"]
    url, headers = f"https://api.airtable.com/v0/appnc2IWGpsHNfTvt/{table_name}", {"Authorization": f"Bearer {access_token}"}
    params = {} if view_name is None else {"view": view_name}
    data = []
    while True:
        res = requests.get(url, headers=headers, params=params).json()
        data += res.get('records', [])
        if 'offset' not in res: break

tiny-rawr

import React, { useEffect, useState } from 'react';
import mixpanel from 'mixpanel-browser';

const ThoughtChecker = () => {
    const [textEntered, setTextEntered] = useState(false);
    const [journalText, setJournalText] = useState('');
    const [defaultJournalEntry, setDefaultJournalEntry] = useState(''); // Set your default journal entry here
    const sessionID = ''; // Set your session ID here

    useEffect(() => {

tiny-rawr

import requests
import json

def generate_talking_avatar(image_filepath, audio_filepath):
    api_key = "<YOUR_API_KEY>"

    files = [
        ("input_face", open(image_filepath, "rb")),
        ("input_audio", open(audio_filepath, "rb")),
    ]

tiny-rawr

from openai import OpenAI

def choose_voice(gender="female")
  gender = gender.lower()
  voice = "shimmer" # gender neutral
  
  if gender == "female":
    voice = "nova"
   elif gender == "male":
    voice = "onyx"

tiny-rawr

from openai import OpenAI

def respond_to_message(message, character_description):
    api_key = st.session_state.api_key
    client = OpenAI(api_key = api_key)

    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": f"You are a helpful assistant. Respond to the following message in 1-3 sentences. Also, this is a description of the character you are in case you are asked: {character_description}"},

tiny-rawr

from openai import OpenAI

def create_custom_character(character_description):

  client = OpenAI(api_key=api_key)

  response = client.images.generate(
    model="dall-e-3",
    prompt=f"Please create the following 3D character in the style of Pixar Animation Studio with a blurred sunlight background: {character_description}. There should be no text in the image.",
    size="1024x1024",

tiny-rawr

def categorise_cognitive_distortions(quotes):
    client = OpenAI()
    client.api_key = st.session_state.api_key

    conversation = [
        {"role": "system",
         "content": "You categorise cognitive distortions and explain why they are an example of that cognitive distortion"},
        {"role": "user", "content": str(quotes)},
    ]
    response = client.chat.completions.create(