/
VoiceAssistant_5.1.py
1427 lines (1220 loc) · 61.6 KB
/
VoiceAssistant_5.1.py
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#time.sleep(30)
import os, time, math, re, json, pyttsx3, requests, pvporcupine, struct, pyaudio, datetime, tiktoken
import speech_recognition as sr
from colorama import init, Fore, Back, Style
from urllib.request import urlopen
from googleapiclient.discovery import build
from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
from google.cloud import texttospeech
try:
# For Raspberry Pi
import Adafruit_DHT
from luma.led_matrix.device import max7219
from luma.core.interface.serial import spi, noop
from luma.core.render import canvas
MAX7219Lib = True
except:
print("No MAX7219 or Adafruit packages found, using settings to not use 8x8 matrix display")
MAX7219Lib = False
try:
import vlc # For Raspberry Pi
print("VLC package found (this is best for Raspberry Pi's)")
vlcLib = True
except:
print("No VLC package found (this is fine if you have Pygame installed)")
vlcLib = False
try:
import pygame # For Windows
pygameLib = True
print("Pygame package found (this is best for Windows)")
except:
print("No Pygame package found (this is fine if you have VLC installed)")
pygameLib = False
try:
from google.cloud import speech
except:
print("No Google Cloud package found (offline STT will be used instead)")
googleSTT = False
from CustomSettings import assistantSpeechOn, offlineTTS, textInput, keepOnListening, listenTime, sumHistoryTime, messageLimit, startPrompt, summarizePrompt
from CustomSettings import wakeUpWords, STABILITY, SIMILARITY_BOOST, VOICE_ID, animationFPS, googleSTT, swedish, english, maxToolsPerPrompt, openAIdelay
from CustomSettings import googleTTS_name, googleTTS_gender, swedishStartPrompt, wakeWordOn, wakeSpeaker, speakerSleepTime, RaspberryPi, devMode
from CustomSettings import overrideMemPrompt, sweOverrideMemPrompt, MAX7219Lib
from apiKeys import openai_api_key, porcupineAccessKey, XI_API_KEY, googleCustomSearchAPI, googleSearchEngineID, GOOGLE_JSON_CREDENTIALS, wolframAlphaAppID
import openai
openai.api_key = openai_api_key
os.environ["WOLFRAM_ALPHA_APPID"] = wolframAlphaAppID
wolfram = WolframAlphaAPIWrapper()
if RaspberryPi:
try:
from ctypes import *
ERROR_HANDLER_FUNC = CFUNCTYPE(None, c_char_p, c_int, c_char_p, c_int, c_char_p) # Define our error handler type
def py_error_handler(filename, line, function, err, fmt):
pass
c_error_handler = ERROR_HANDLER_FUNC(py_error_handler)
asound = cdll.LoadLibrary('libasound.so')
asound.snd_lib_error_set_handler(c_error_handler) # Set error handler
print("Libasound module found (Libasound error handling will be used)")
except:
print("No Libasound module found (No Libasound error handling will be used)")
RaspberryPi = False
# Porcupine (Wake-word recognizer)
porcupine = pvporcupine.create(
access_key=porcupineAccessKey,
keywords=wakeUpWords # Wake-up-words
)
scriptDir = os.path.dirname(os.path.abspath(__file__))
longTermMemoryPath = os.path.join(scriptDir, "longTermMemory.json")
textToSpeechFilePath = os.path.join(scriptDir, "textToSpeech.mp3")
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = os.path.join(scriptDir, GOOGLE_JSON_CREDENTIALS)
# ElevenLabs
OUTPUT_PATH = "textToSpeech.mp3"
# IP info, location
IPinfoURL='http://ipinfo.io/json'
IPinfoResponse=urlopen(IPinfoURL)
IPinfoData=json.load(IPinfoResponse)
city = IPinfoData['city']
postal = IPinfoData['postal']
timezone = IPinfoData['timezone']
country = IPinfoData['country']
if country == "SE": country = "Sweden"
if MAX7219Lib == True:
from mutagen.mp3 import MP3
from images import loading_frames, talk_frames, think_frame
# Define pins for MAX7219
serial = spi(port=0, device=0, gpio=noop())
device = max7219(serial, cascaded=1)
# Define pin for DHT11
dht11_pin = 4
sensor = Adafruit_DHT.DHT11
animationSPF = 1/animationFPS
# Offline text-to-speech
tts = pyttsx3.init()
tts.startLoop(False)
# PyAudio
pa = None
audio_stream = None
# Speech-to-text
stt = sr.Recognizer()
# Initialize pygame
if (pygameLib == True) and (vlcLib == False):
pygame.init()
pygame.mixer.init()
init(autoreset=True) # Initializes Colorama (print colored text)
hasSummarized = True
lastOpenAIresponse = time.time() - openAIdelay
start_time = time.time()
lastSoundTime = 0
history = []
messages = []
messageCount = 0
totAnswerTokens = 0
messageTokens = 0
kwargs = {}
totalCost = 0
rstMemStage = 0
saveMemStage = 0
memRstChoice = ''
math_functions = {
"sin": math.sin,
"cos": math.cos,
"tan": math.tan,
"asin": math.asin,
"acos": math.acos,
"atan": math.atan,
"exp": math.exp,
"log": math.log,
"log10": math.log10,
"sqrt": math.sqrt,
"ceil": math.ceil,
"floor": math.floor,
"pi": math.pi,
"e": math.e
}
# Infinite main loop
def main():
# Listen for wake-word
if wakeWordOn:
pcm = audio_stream.read(porcupine.frame_length)
pcm = struct.unpack_from("h" * porcupine.frame_length, pcm)
keyword_index = porcupine.process(pcm)
if (wakeWordOn == False) or (textInput == True): keyword_index = 1 # Trigger a virtual "wake-word detected", to go into the loop instantly
global start_time
global hasSummarized
global lastSoundTime
if keyword_index >= 0: # If wake-word detected
if wakeWordOn and (textInput == False): print(Style.NORMAL+Fore.WHITE+"Wake-word detected")
if MAX7219Lib == True: animate(loading_frames, animationSPF) # Visualize assistant wake-up on matrix display
start_time = time.time()
while time.time() - start_time < listenTime: # Listen for certain amount of seconds
if textInput == True:
userPrompt = input(Style.BRIGHT + Fore.GREEN + 'User: '+ Style.NORMAL)
else:
if googleSTT == True:
userPrompt = googleSpeechToText()
else:
userPrompt = speechToText() # Listen to speech from user
closeAudioStream() # Stop listening (to not trigger speech when assistant is talking)
if userPrompt is not None or userPrompt != '':
if textInput == False: print(Style.BRIGHT+Fore.GREEN+"User: "+Style.NORMAL+userPrompt)
if userPrompt == None or userPrompt == '': # If there was no speech
break # Break out of loop, and restart
else: # If there was speech
if MAX7219Lib == True: still(think_frame, 0) # Visualize thinking on matrix display
toolAnswer = useTool(userPrompt)
answer(userPrompt, toolAnswer)
if wakeSpeaker and assistantSpeechOn: lastSoundTime = time.time()
hasSummarized = False
start_time = time.time()
if keepOnListening == False: break # Stop listening, if keepOnListening is set to false by user
else: openAudioStream() # Start listning again
# Restart
if MAX7219Lib == True: animate(reversed(loading_frames), animationSPF) # Visualize assistant sleep on matrix display
openAudioStream()
if wakeWordOn and (textInput == False): print(Style.NORMAL+Fore.WHITE+"Waiting for wake-word")
if time.time() - start_time > sumHistoryTime: # if (time.time() - start_time > sumHistoryTime) and (hasSummarized == False) # TEST
print(Fore.YELLOW+str(sumHistoryTime)+" seconds has passed since last conversation")
summarizeHistory()
if wakeWordOn and (textInput == False): print("Waiting for wake-word")
if wakeSpeaker and assistantSpeechOn and (time.time() - lastSoundTime > speakerSleepTime):
beep()
lastSoundTime = time.time()
def youSure(memory, sureBool=True):
global overrideMemPrompt
global sweOverrideMemPrompt
if english == True:
if 'SAVE' in memory: # Ask to save memory
toolAnswer = '[PRIO]You must ask the user, in your next message, for approval if you understood correctly that the short term memory (current conversation) of this device is preferred to be saved to the long term memory.'
else: # Ask to delete memory
if 'BOTH' in memory: text="BOTH the long term memory AND short term memory of this device are"
elif 'LONG TERM' in memory: text="the long term memory of this device is"
elif 'SHORT TERM' in memory: text="the short term memory of this device is"
if sureBool == False:
toolAnswer = "[PRIO]"+overrideMemPrompt+f"You must ask the user again, in your next message, for approval if you understood correctly that {text} preferred to be deleted. Ask if the user meant to say yes or no."
else:
toolAnswer = "[PRIO]"+overrideMemPrompt+f'You must ask the user, in your next message, for approval if you understood correctly that {text} preferred to be deleted. Ask if the user meant to say yes or no.'
elif english == False:
if 'SAVE' in memory: # Ask to save memory
toolAnswer = '[PRIO]Du har en falsk konversation med en användare. Låtsas att du kan spara minne på den här enheten. Du MÅSTE be användaren, i ditt nästa meddelande, om godkännande om du förstått rätt att korttidsminnet (aktuell konversation) för denna enhet föredras att sparas i långtidsminnet.'
else: # Ask to delete memory
if 'BOTH' in memory: text="BÅDE långtidsminnet OCH korttidsminnet för denna enhet"
elif 'LONG TERM' in memory: text="den här enhetens långtidsminne"
elif 'SHORT TERM' in memory: text="korttidsminnet för denna enhet"
if sureBool == False:
toolAnswer = "[PRIO]"+sweOverrideMemPrompt+f"Du måste be användaren igen, i ditt nästa meddelande, om godkännande om du förstått rätt att {text} föredras att bli raderat. Be användaren att svara ja eller nej."
else:
toolAnswer = "[PRIO]"+sweOverrideMemPrompt+f'Du måste be användaren, i ditt nästa meddelande, om godkännande om du förstått rätt att {text} föredras att bli raderat. Be användaren att svara ja eller nej.'
return toolAnswer
def userApprove(userPrompt):
approveMsgs = [{"role": "user", "content": userPrompt}]
approveFuncList = [
{
"name": "yes_or_no",
"description": 'Determine if the user said “yes” or “no” in any way (in Swedish or English). You must be certain, otherwise choose "UNSURE".',
"parameters": {
"type": "object",
"properties": {
"decision": {
"type": "string",
"enum": ["YES", "NO","UNSURE"]
},
},
"required": ["decision"]
}
}
]
approveResponse=generateResponse(approveMsgs, approveFuncList, 0.2, {"name": "yes_or_no"})
if approveResponse.get("function_call"): # Check if it wanted to use a function
functionArgs = json.loads(approveResponse["function_call"]["arguments"])
approveDecision = functionArgs.get("decision")
else:
approveDecision = 'UNSURE'
return approveDecision
def useTool(userPrompt):
global maxToolsPerPrompt
useTools = True
stringToolAnswers = ''
toolsUsedNum = 0
toolAnswers = []
toolMsgs = []
appendPrevSummations(toolMsgs)
toolMsgs.extend(history)
toolMsgs.append({'role':'user','content':userPrompt})
toolStartPrompt = (
"You can use the following tools to answer the user's latest message:\n"
+'{WOLFRAM ALPHA} Provides answers to mathematics, science, technology, society and culture, and weather (location-based).\n'
+'{GOOGLE SEARCH} Can search for anything. Best used for up-to-date information.\n'
+("{TEMP SENSOR} Gives current room temperature.\n" if MAX7219Lib else "")
+'{SIMPLE CALCULATOR} Answers simple calculations.\n'
+'{LOCATION} Provides current device location.\n'
+'{TIME & DATE} Access the current date and time.\n'
+"{SAVE MEMORY} ONLY if the user CLEARLY conveyed to want to save, remember or summarize current conversation to the device's long-term-memory.\n" # +"{SAVE MEMORY} ONLY if the user CLEARLY conveyed to want to save or summarize current conversation to the device's long-term-memory.\n"
+"{RESET MEMORY} If the user wants to reset/delete/clear the device's memory/database/messages, or are deciding on what memory to reset/delete.\n\n"
+"To use a tool, write {} with its name in braces. If you can answer without a tool, write {YES}.\n\n"
+'Examples:\n'
+'Weather info: {LOCATION}, {WOLFRAM ALPHA}\n'
+'Greeting user: {YES}\n'
+"Obama's age to the power of 2: {TIME & DATE}, {WOLFRAM ALPHA}\n"
+"Pewdiepie's height subtracted by Marzia's height: {GOOGLE SEARCH}, {SIMPLE CALCULATOR}\n"
+"Holidays today: {TIME & DATE}, {GOOGLE SEARCH}\n"
+"Memory deletion or clear database: {RESET MEMORY}\n"
+"Recall or remember info from long-term memory: {YES}\n"
+'Unsure/unnecessary: {YES}\n\n'
+'Choose a tool now within {}, nothing else.'
)
toolMsgs.append({'role':'system','content':toolStartPrompt})
response1=generateResponse(toolMsgs, temp=0.2, max_tokens=8) # Choose a tool
global devMode
if devMode: print("response1:",response1)
# Extract the text in braces
match1 = re.search(r'\{(.+?)\}', response1)
if match1: response1 = match1.group(1)
toolMsgs.append({'role':'assistant','content':'{'+response1+'}'})
while useTools:
if 'SAVE MEMORY' in response1:
print(Fore.YELLOW+"Assistant used memory save tool:")
global saveMemStage
if saveMemStage == 1: # Did the user approve?
decision=userApprove(userPrompt)
if decision == 'YES':
print(Fore.YELLOW+"User accepted: Short-term memory will be saved shortly")
summarizeOutput = summarizeHistory(userPrompt)
toolAnswer = f'[PRIO]'+summarizeOutput
saveMemStage = 0
elif decision == 'NO':
print(Fore.YELLOW+"User canceled: Short-term memory will not be saved")
toolAnswer = ''
saveMemStage = 0
else: # UNSURE
toolAnswer=youSure('SAVE')
print(Fore.YELLOW+"Checking if the target is correct again...")
elif saveMemStage == 0: # Ask if the user is sure
print(Fore.YELLOW+"Target: save short-term memory")
toolAnswer=youSure('SAVE')
print(Fore.YELLOW+"Checking if the target is correct...")
saveMemStage = 1
toolAnswers.append(toolAnswer)
break # Use no more tools
else:
saveMemStage = 0
if 'RESET MEMORY' in response1:
print(Fore.YELLOW+"Assistant used memory reset tool:")
global rstMemStage
global memRstChoice
global overrideMemPrompt
global sweOverrideMemPrompt
if rstMemStage == 2: # Did the user approve?
decision=userApprove(userPrompt)
if decision == 'YES':
print(Fore.YELLOW+"User accepted deletion: Memory will be removed shortly")
if english == True:
if 'LONG TERM' in memRstChoice:
text = 'long'
mem = text
elif 'SHORT TERM' in memRstChoice:
text = 'short'
mem = text
elif 'BOTH' in memRstChoice:
text = 'both long term and short'
mem = 'both'
toolAnswer = f'[PRIO][{mem}]{overrideMemPrompt}You MUST inform the user in you next message that you are deleting {text} term memory from this device'
elif english == False:
if 'LONG TERM' in memRstChoice:
text = 'lång'
mem = 'long'
elif 'SHORT TERM' in memRstChoice:
text = 'kort'
mem = 'short'
elif 'BOTH' in memRstChoice:
text = 'både lång- och kort'
mem = 'both'
toolAnswer = f'[PRIO][{mem}]{sweOverrideMemPrompt}Du måste informera användaren i ditt nästa meddelande att du raderar {text}tidsminnet från denna enheten'
rstMemStage = 0
memRstChoice = ''
elif decision == 'NO':
print(Fore.YELLOW+"User canceled deletion: Memory will not be removed")
toolAnswer = ''
rstMemStage = 0
memRstChoice = ''
else: # User didn't specify yes or no, decision == 'UNSURE'
toolAnswer=youSure(memRstChoice, False)
print(Fore.YELLOW+"Checking if the target is correct, again...")
elif rstMemStage == 0: # Check what memory should be deleted
print(Fore.YELLOW+"Checking what memory should be deleted...")
rstMemMsgs = []
rstMemMsgs.extend(history)
rstMemMsgs.append({'role':'user','content':userPrompt})
memRstFuncDesc = (
'Determine what memory the user wants to reset/delete from this device (in Swedish or English). '
+'Or is it wanted at all?\n\n'
+'LONG TERM: Older conversations before this one\n'
+'SHORT TERM: The current conversation\n'
+'BOTH: Both long term and short term\n'
+'NOTHING: The user does not want to reset/delete anything\n'
+'UNSURE: If the user has not specified, or only said to delete “memory” or “minnet”\n\n'
+'Choice examples:\n'
+'”Långtidsminnet” = LONG TERM\n'
+'”Korttidsminnet” = SHORT TERM\n'
+'”Vill inte” / ”inget av dem” = NOTHING\n\n'
+'You must be certain, otherwise choose “UNSURE”.'
)
memRstFunc = [
{
"name": "memory_reset_choice",
"description": memRstFuncDesc,
"parameters": {
"type": "object",
"properties": {
"choice": {
"type": "string",
"enum": ["LONG TERM", "SHORT TERM","BOTH","NOTHING","UNSURE"]
},
},
"required": ["choice"]
}
}
]
memRstResponse=generateResponse(rstMemMsgs, memRstFunc, 0.2, {"name": "memory_reset_choice"})
if memRstResponse.get("function_call"): # Check if it wanted to use a function
functionArgs = json.loads(memRstResponse["function_call"]["arguments"])
memRstChoice = functionArgs.get("choice")
else:
memRstChoice = 'UNSURE'
rstMemMsgs.append({'role':'assistant','content':'{'+memRstChoice+'}'})
if 'LONG TERM' in memRstChoice or 'SHORT TERM' in memRstChoice or 'BOTH' in memRstChoice: # We know what memory should be deleted
rstMemStage = 1 # Go to the next stage
elif 'NOTHING' in memRstChoice:
print(Fore.YELLOW+"Nothing will be deleted.")
toolAnswer = ''
rstMemStage = 0
memRstChoice = ''
else: # It doesn't know
print(Fore.YELLOW+"Assistant doesn't know, but will ask...")
toolAnswer = (
'[PRIO]'
+'You must ask the user, in your next message, to specify what memory on this device that is preferred to be deleted. '
+'Long term memory, short term memory, or both?\n\n'
+'Long term memory: Older conversations before this one\n'
+'Short term memory: The current conversation'
)
if rstMemStage == 1: # We know what memory should be deleted, now ask if the user is sure to do this
if 'LONG TERM' in memRstChoice:
print(Fore.YELLOW+"Targeting: Long-term memory")
toolAnswer=youSure('LONG TERM')
elif 'SHORT TERM' in memRstChoice:
print(Fore.YELLOW+"Targeting: Short-term memory")
toolAnswer=youSure('SHORT TERM')
elif 'BOTH' in memRstChoice:
print(Fore.YELLOW+"Targeting: Both long-term and short-term memory")
toolAnswer=youSure('BOTH')
print(Fore.YELLOW+"Checking if the target is correct...")
rstMemStage = 2
toolAnswers.append(toolAnswer)
break # Use no more tools
else:
rstMemStage = 0
memRstChoice = ''
if 'WOLFRAM ALPHA' in response1:
wolframStartPrompt = ("Enter a question for Wolfram Alpha within {}.\n\n"
+"Examples:\n"
+"'sqrt(2)+5^3' -> {sqrt(2)+5^3}"
+"'weather Luleå Sweden' -> {weather Luleå Sweden}\n"
+"'Obama's age to the power of two' -> {(Obama's age in years)^2}\n\n"
+"Make sure to only include the question within the curly braces {}.")
toolMsgs.append({'role':'system','content':wolframStartPrompt})
response2=generateResponse(toolMsgs)
# Extract the response in braces
match2 = re.search(r'\{(.+?)\}', response2)
if match2: response2 = match2.group(1)
toolMsgs.append({'role':'assistant','content':'{'+response2+'}'})
try:
answer = wolfram.run(response2)
toolAnswer = 'Assistant used Wolfram Alpha tool:\nQuestion: {}\n{}'.format(response2, answer)
toolMsgs.append({'role':'system','content':'Wolfram Alpha answer:\n{}'.format(answer)})
except:
toolAnswer = 'Assistant tried to use Wolfram Alpha tool, but a problem occured'
toolMsgs.append({'role':'system','content':toolAnswer})
elif 'GOOGLE SEARCH' in response1:
googleStartPrompt = "Enter a web search engine query for Google Search within {}. For example, to search for 'how old is Obama' type {Obama age}, and Google will find results for you. Now write the query in curly braces {}." # Make sure to only include the question within the curly braces {}.
toolMsgs.append({'role':'system','content':googleStartPrompt})
query=generateResponse(toolMsgs)
# Extract the response in braces
match2 = re.search(r'\{(.+?)\}', query)
if match2: query = match2.group(1)
toolMsgs.append({'role':'assistant','content':'{'+query+'}'})
try:
service = build("customsearch", "v1", developerKey=googleCustomSearchAPI) # Init google search engine
response = (service.cse().list(q=query,cx=googleSearchEngineID,).execute()) # Call google search api
results = response['items']
answer = ""
for result in results:
answer += "Link: " + result['link'] + "\n"
answer += "Title: " + result['title'] + "\n"
answer += "Content: " + result['snippet'] + "\n\n"
toolAnswer = 'Assistant used Google Search tool:\n\nQuery: {}\n\nSearch results:\n\n{}'.format(query, answer)
toolMsgs.append({'role':'system','content':'Google Search results:\n\n{}'.format(answer)})
except:
toolAnswer = 'Assistant tried to use Google Search tool, but a problem occured'
toolMsgs.append({'role':'system','content':toolAnswer})
elif 'SIMPLE CALCULATOR' in response1:
calcStartPrompt = "Enter an equation for me to calculate within {}. For example, to calculate 'sqrt(2)+5^3', type {sqrt(2)+5^3} and I will calculate it for you. Make sure to only include the equation within the curly braces {}."
toolMsgs.append({'role':'system','content':calcStartPrompt})
response2=generateResponse(toolMsgs)
# Extract the response in braces
match2 = re.search(r'\{(.+?)\}', response2)
if match2: response2 = match2.group(1)
toolMsgs.append({'role':'assistant','content':'{'+response2+'}'})
evalCalc = response2.replace("^", "**")
try:
answer = eval(evalCalc, math_functions)
toolAnswer = 'Assistant used calculator tool: {} = {}'.format(response2, answer)
toolMsgs.append({'role':'system','content':'Calculator answer: {}'.format(answer)})
except:
toolAnswer = 'Assistant tried to use calculator tool, but a problem occured'
toolMsgs.append({'role':'system','content':toolAnswer})
elif 'TIME & DATE' in response1:
answer = datetime.datetime.now().strftime('%H:%M:%S %Y-%m-%d')
toolAnswer = 'Assistant used time & date tool: {}'.format(answer)
toolMsgs.append({'role':'system','content':'Time & date answer: {}'.format(answer)})
elif 'LOCATION' in response1:
answer = f"City: {city}, Country: {country}, Postal: {postal}, Timezone: {timezone}"
toolAnswer = 'Assistant used location tool:\n{}'.format(answer)
toolMsgs.append({'role':'system','content':'Location answer:\n{}'.format(answer)})
elif ('TEMP SENSOR' in response1) and (MAX7219Lib == True):
answer = getTemp() + " degrees Celsius"
toolAnswer = 'Assistant used temperature sensor: The temperature in this room is {}'.format(answer)
toolMsgs.append({'role':'system','content':'Temp sensor results:\nTemp in this room is {}'.format(answer)})
else:
toolAnswer = ''
if toolAnswer != '':
print(Fore.YELLOW+toolAnswer)
toolAnswers.append(toolAnswer)
else: useTools = False
toolsUsedNum += 1
if (toolsUsedNum >= maxToolsPerPrompt): useTools = False
if useTools:
toolEndPrompt = (
'If you are certain that you can give a good, relevant and accurate answer to the user’s question without using another tool, write {YES}, nothing else. '
+'To choose a tool, write {} with its name in braces. For example, to use Google Search, write {GOOGLE SEARCH}. '
+'It can be good to use a tool to gather information, and then use another to calculate or search for anything based on the previous info. '
+'Now you MUST choose with {} only, nothing else.'
)
toolMsgs.append({'role':'system','content':toolEndPrompt})
response3=generateResponse(toolMsgs) # Choose another tool, or end loop
# Extract the text in braces
match3 = re.search(r'\{(.+?)\}', response3)
if match3: response3 = match3.group(1)
toolMsgs.append({'role':'assistant','content':'{'+response3+'}'})
if ('YES' in response3): useTools = False
else: response1 = response3
if toolAnswer != '': stringToolAnswers = '\n\n'.join(toolAnswers)
useTools = True
toolsUsedNum = 0
toolAnswers = []
toolMsgs = []
return stringToolAnswers
def detectLanguage(text):
print(Fore.YELLOW+"Detecting language...")
lanMsgs = [{'role':'user','content':text}]
lanFuncList = [
{
"name": "english_or_swedish",
"description": "Based on the user message, determine if it is English or Swedish",
"parameters": {
"type": "object",
"properties": {
"decision": {
"type": "string",
"enum": ["ENGLISH", "SWEDISH"]
},
},
"required": ["decision"]
}
}
]
lanResponse=generateResponse(lanMsgs, lanFuncList, 0.2, {"name": "english_or_swedish"})
if lanResponse.get("function_call"): # Check if it wanted to use a function
functionArgs = json.loads(lanResponse["function_call"]["arguments"])
lanDecision = functionArgs.get("decision")
else:
lanDecision = 'ENGLISH'
if lanDecision == "SWEDISH": return "sv"
else: return "en"
def beep():
os.system('play -nq -t alsa synth 0.34 sine 30 vol 0.05')
def textToSpeech(text, language):
print("Generating text-to-speech...")
if language == "sv": # Swedish
client = texttospeech.TextToSpeechClient()
input_text = texttospeech.SynthesisInput(text=text)
if googleTTS_gender == "MALE": ssml_gender=texttospeech.SsmlVoiceGender.MALE
else: ssml_gender=texttospeech.SsmlVoiceGender.FEMALE
voice = texttospeech.VoiceSelectionParams(
language_code="sv-SE",
name=googleTTS_name,
ssml_gender=ssml_gender,
)
audio_config = texttospeech.AudioConfig(
audio_encoding=texttospeech.AudioEncoding.MP3
)
response = client.synthesize_speech(
request={"input": input_text, "voice": voice, "audio_config": audio_config}
)
with open(textToSpeechFilePath, "wb") as out:
out.write(response.audio_content)
else: # English
headers = {
"Accept": "audio/mpeg",
"xi-api-key": XI_API_KEY,
"Content-Type": "application/json"
}
data = {
"text": text,
"voice_settings": {
"stability": STABILITY,
"similarity_boost": SIMILARITY_BOOST
}
}
try:
if vlcLib == True: # For Raspberry Pi
response = requests.post(f"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}", json=data, headers=headers)
with open(textToSpeechFilePath, "wb") as f:
f.write(response.content)
elif pygameLib == True: # For Windows
response = requests.post(f"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}/stream", json=data, headers=headers, stream=True)
with open(textToSpeechFilePath, 'wb') as f:
for chunk in response.iter_content(chunk_size=1024):
if chunk:
f.write(chunk)
except Exception as e: # There was an error
print(Style.BRIGHT+Fore.RED+f"ElevenLabs Error:\n{e}")
print("Using offline text-to-speech instead...")
offlineTextToSpeech(text)
return None
# Play sound
if vlcLib == True: # For Raspberry Pi
if wakeSpeaker:
beep() # Beep to wake up the speaker, if it turns off automatically
time.sleep(0.3)
instance = vlc.Instance() # create instance of VLC
player = instance.media_player_new() # create new MediaPlayer object
media = instance.media_new(textToSpeechFilePath) # load the audio file
player.set_media(media) # set the media for the player
def on_end_reached(event):
player.stop()
instance.release()
# register the callback function with the event manager
event_manager = player.event_manager()
event_manager.event_attach(vlc.EventType.MediaPlayerEndReached, on_end_reached)
player.audio_set_volume(100) # Set the volume to 100%
player.play() # play the media
elif pygameLib == True: # For Windows
if language == "sv": # Swedish
sound = pygame.mixer.Sound(textToSpeechFilePath) # Load the sound file
duration = sound.get_length() * 1000 # Convert to milliseconds
channel = sound.play()
pygame.time.wait(int(duration)) # Waiting until finished talking
channel.stop()
else: # English
pygame.mixer.music.load(textToSpeechFilePath)
pygame.mixer.music.play()
while pygame.mixer.music.get_busy():
pygame.time.Clock().tick(10)
# Put stuff here that should run while playing speech
pygame.mixer.music.stop()
pygame.quit()
pygame.init()
pygame.mixer.init()
return textToSpeechFilePath
def offlineTextToSpeech(text):
tts.say(text)
tts.iterate()
def generateResponse(openAImsgs, funcList=None, temp=None, functionCall=None, stream=None, max_tokens=None):
global kwargs
kwargs = {}
if funcList is not None:
kwargs['model'] = "gpt-3.5-turbo-0613"
kwargs['functions'] = funcList
else:
kwargs['model'] = "gpt-3.5-turbo"
kwargs['messages'] = openAImsgs
if temp is not None:
kwargs['temperature'] = temp
if functionCall is not None:
kwargs['function_call'] = functionCall
if stream is not None:
kwargs['stream'] = stream
if max_tokens is not None:
kwargs['max_tokens'] = max_tokens
promptTokens = num_tokens_from_messages(openAImsgs, kwargs['model'])
if promptTokens > 3500:
print(Fore.RED+"OpenAI: Using 16k model instead of 4k, which is more expensive (too many tokens for 4k model)")
if funcList is not None:
kwargs['model'] = "gpt-3.5-turbo-16k-0613"
else:
kwargs['model'] = "gpt-3.5-turbo-16k"
if devMode:
if "gpt-3.5-turbo" in kwargs['model'] and "16k" not in kwargs['model']:
global totalCost
totalCost+=0.0015*(promptTokens/1000)
print(Fore.LIGHTMAGENTA_EX+"Approx. cost:",totalCost,"$")
# Wait until we can send another request to OpenAI without exceeding the max request rate
global lastOpenAIresponse
if (openAIdelay > 0) and ((openAIdelay - (time.time() - lastOpenAIresponse)) > 0):
print("Waiting", round(openAIdelay - (time.time() - lastOpenAIresponse)), "seconds... Free OpenAI account settings on.")
time.sleep(openAIdelay - (time.time() - lastOpenAIresponse))
openaiError = False
attempts = 3
retryTime = 5 # seconds
for attempt in range(attempts):
if openaiError:
print(Fore.RED+f"Retrying in {retryTime} seconds...")
time.sleep(retryTime)
try:
# Call OpenAI's API to generate a response
response = openai.ChatCompletion.create(
**kwargs
)
if (openAIdelay > 0): lastOpenAIresponse = time.time()
if funcList: # Functions
aiResponse = response['choices'][0]['message']
elif stream: # Streamed assistant answers
aiResponse = response
else: # Usual responses (some tools)
aiResponse = response['choices'][0]['message']['content']
if devMode:
print("openAImsgs:\n",openAImsgs)
print("aiResponse:\n",aiResponse)
except openai.error.Timeout as e:
#Handle timeout error, e.g. retry or log
print(Style.BRIGHT+Fore.RED+f"OpenAI API request timed out: {e}")
openaiError = True
except openai.error.APIError as e:
#Handle API error, e.g. retry or log
print(Style.BRIGHT+Fore.RED+f"OpenAI API returned an API Error: {e}")
openaiError = True
except openai.error.APIConnectionError as e:
#Handle connection error, e.g. check network or log
print(Style.BRIGHT+Fore.RED+f"OpenAI API request failed to connect: {e}")
openaiError = True
except openai.error.InvalidRequestError as e:
#Handle invalid request error, e.g. validate parameters or log
print(Style.BRIGHT+Fore.RED+f"OpenAI API request was invalid: {e}")
print(Style.BRIGHT+Fore.RED+"Messages sent to OpenAI:\n",openAImsgs)
openaiError = True
except openai.error.AuthenticationError as e:
#Handle authentication error, e.g. check credentials or log
print(Style.BRIGHT+Fore.RED+f"OpenAI API request was not authorized: {e}")
openaiError = True
except openai.error.PermissionError as e:
#Handle permission error, e.g. check scope or log
print(Style.BRIGHT+Fore.RED+f"OpenAI API request was not permitted: {e}")
openaiError = True
except openai.error.RateLimitError as e:
#Handle rate limit error, e.g. pace requests or log
print(Style.BRIGHT+Fore.RED+f"OpenAI API request hit rate limit: {e}")
openaiError = True
except openai.error.ServiceUnavailableError as e:
print(Style.BRIGHT+Fore.RED+f"OpenAI API service is unavailable: {e}")
openaiError = True
except Exception as e:
print(Style.BRIGHT+Fore.RED+f"OpenAI Error:\n{e}")
openaiError = True
else: # The try block ran without raising an exception
openaiError = False
break
else: # We failed all the retry attempts - deal with the consequences
print(Style.BRIGHT+Fore.RED+f"Could not generate a response from OpenAI even after {attempts} retry attempts. Please try again later or consider looking in to it.\nShort-term memory was deleted, to start from scratch.")
chatReset()
openaiError = True
if openaiError: aiResponse = ''
return aiResponse
def num_tokens_from_string(string: str, encoding_name: str) -> int:
encoding = tiktoken.encoding_for_model(encoding_name)
num_tokens = len(encoding.encode(string))
return num_tokens
def num_tokens_from_messages(messages, model="gpt-3.5-turbo"):
"""Return the number of tokens used by a list of messages."""
try:
encoding = tiktoken.encoding_for_model(model)
except KeyError:
print("Warning: model not found. Using cl100k_base encoding.")
encoding = tiktoken.get_encoding("cl100k_base")
if model in {
"gpt-3.5-turbo-0613",
"gpt-3.5-turbo-16k-0613",
"gpt-3.5-turbo-16k",
"gpt-4-0314",
"gpt-4-32k-0314",
"gpt-4-0613",
"gpt-4-32k-0613",
}:
tokens_per_message = 3
tokens_per_name = 1
elif model == "gpt-3.5-turbo-0301":
tokens_per_message = 4 # every message follows <|start|>{role/name}\n{content}<|end|>\n
tokens_per_name = -1 # if there's a name, the role is omitted
elif "gpt-3.5-turbo" in model:
return num_tokens_from_messages(messages, model="gpt-3.5-turbo-0613")
elif "gpt-4" in model:
print("Warning: gpt-4 may update over time. Returning num tokens assuming gpt-4-0613.")
return num_tokens_from_messages(messages, model="gpt-4-0613")
else:
raise NotImplementedError(
f"""num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens."""
)
num_tokens = 0
for message in messages:
num_tokens += tokens_per_message
for key, value in message.items():
num_tokens += len(encoding.encode(value))
if key == "name":
num_tokens += tokens_per_name
num_tokens += 3 # every reply is primed with <|start|>assistant<|message|>
return num_tokens
def chatReset():
global messages
global history
global messageCount
messages.clear()
history.clear()
messageCount = 0
messageTokens = 0
def longTermMemoryReset():
with open(longTermMemoryPath, "r") as f:
data = json.load(f)
if "summations" in data:
with open(longTermMemoryPath, "w") as f:
json.dump({}, f)
def appendPrevSummations(memoryList):
# Load the JSON file
with open(longTermMemoryPath, "r") as f:
data = json.load(f)
# Extract previous summations from the JSON file and append them to memoryList
if "summations" in data: # If there are any summations
for summation in data["summations"]:
memoryList.append({"role": "system", "content": "Device memory from earlier conversations: "+summation})
def shortenSummation(summation):
pattern = re.compile(r'•(.*?)\n')
matches = pattern.findall(summation)
shorterSummation = '\n'.join(matches)
return shorterSummation
def summationStringToList(summationString, newOrOld):
namePattern = re.compile(r"User's name: (.*?)\n")
nameList = namePattern.findall(summationString)
if newOrOld == "OLD":
infoNtimePattern = re.compile(r"Info timestamps:\n(.+?)\n\n", re.DOTALL)
infoNtimeList = infoNtimePattern.findall(summationString)
for name in range(len(nameList)):
infoNtimeList[name] += "\n"
fullInfoList = []
if newOrOld == "NEW": summationString += "\n\n"
for name in range(len(nameList)):
if newOrOld == "OLD":
infoPattern = re.compile(r"\n-(.+?)(?=\n)", re.DOTALL)
infoList = infoPattern.findall(infoNtimeList[name])
exctractedInfoList = '\n'.join(infoList)
fullInfoList.append(exctractedInfoList)
elif newOrOld == "NEW":
infoPattern = re.compile(r"User's name: {}\n(.+?)\n\n".format(nameList[name]), re.DOTALL)
infoMatch = infoPattern.search(summationString)
if infoMatch:
fullInfoList.append(infoMatch.group(1))
extractedSummationList = []
for name in range(len(nameList)):
extractedSummationList.append(
{
"User's name": nameList[name],
"Info": fullInfoList[name]
}
)
if newOrOld == "OLD":
return extractedSummationList, nameList
elif newOrOld == "NEW":
return extractedSummationList
def summarizeHistory(userPrompt=None):
global messages
global history
print(Fore.YELLOW+"Transferring short-term-memory to long-term-memory...")
with open(longTermMemoryPath, "r") as f:
data = json.load(f)
# WARNING! longTermMemory.json must only contain one list item
# Extract previous summations from the JSON file and append them to memoryList
if "summations" in data: # If there are any summations
oldSummation = data["summations"][0]
if devMode: print("oldSummation:\n\n",oldSummation)
oldSummationLists=summationStringToList(oldSummation, "OLD")
oldSummationList=oldSummationLists[0]
oldNameList=oldSummationLists[1]
if devMode:
print("oldSummationList:",oldSummationList)
print("oldNameList:",oldNameList)
summarizeOutput = "ERROR when trying to save short-term memory to long-term memory"
summarizeError = False
attempts = 2
for attempt in range(attempts):