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text-audio-video

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michal

hmm

172cdd5 over 1 year ago

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	# -- coding: utf-8 --
	"""wiki_chat.ipynb

	Automatically generated by Colaboratory.

	Original file is located at
	https://colab.research.google.com/drive/1P5rJeCXRSsDJw_1ksnHmodH6ng2Ot5NW
	"""

	# !pip install gradio

	# !pip install -U sentence-transformers

	# !pip install datasets


	from azure_utils import AzureVoiceData
	from polly_utils import PollyVoiceData, NEURAL_ENGINE
	from langchain.prompts import PromptTemplate
	from openai.error import AuthenticationError, InvalidRequestError, RateLimitError
	import re
	import sys
	from io import StringIO
	from threading import Lock
	from langchain.llms import OpenAI
	from langchain.chains.conversation.memory import ConversationBufferMemory
	from langchain.agents import tool, load_tools, initialize_agent
	from langchain.agents import Tool
	from langchain import ConversationChain, LLMChain
	import whisper
	import warnings
	import boto3
	import datetime
	from typing import Optional, Tuple
	from contextlib import closing
	# Console to variable
	import io
	import requests
	import os
	import gradio as gr
	from sentence_transformers import SentenceTransformer, CrossEncoder, util
	from torch import tensor as torch_tensor
	from datasets import load_dataset

	"""# import models"""

	bi_encoder = SentenceTransformer(
	'sentence-transformers/multi-qa-MiniLM-L6-cos-v1')
	bi_encoder.max_seq_length = 256 # Truncate long passages to 256 tokens

	# The bi-encoder will retrieve top_k documents. We use a cross-encoder, to re-rank the results list to improve the quality
	cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')

	"""# import datasets"""
	dataset = load_dataset("gfhayworth/wiki_mini", split='train')
	mypassages = list(dataset.to_pandas()['psg'])

	dataset_embed = load_dataset("gfhayworth/wiki_mini_embed", split='train')
	dataset_embed_pd = dataset_embed.to_pandas()
	mycorpus_embeddings = torch_tensor(dataset_embed_pd.values)


	def search(query, top_k=20, top_n=1):
	question_embedding = bi_encoder.encode(query, convert_to_tensor=True)
	question_embedding = question_embedding # .cuda()
	hits = util.semantic_search(
	question_embedding, mycorpus_embeddings, top_k=top_k)
	hits = hits[0] # Get the hits for the first query

	##### Re-Ranking #####
	cross_inp = [[query, mypassages[hit['corpus_id']]] for hit in hits]
	cross_scores = cross_encoder.predict(cross_inp)

	# Sort results by the cross-encoder scores
	for idx in range(len(cross_scores)):
	hits[idx]['cross-score'] = cross_scores[idx]

	hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True)
	predictions = hits[:top_n]
	return predictions
	# for hit in hits[0:3]:
	# print("\t{:.3f}\t{}".format(hit['cross-score'], mypassages[hit['corpus_id']].replace("\n", " ")))


	def get_text(qry):
	predictions = search(qry)
	prediction_text = []
	for hit in predictions:
	prediction_text.append("{}".format(mypassages[hit['corpus_id']]))
	return prediction_text


	@tool
	def mysearch(query: str) -> str:
	"""Query our own datasets.
	"""
	rslt = get_text(query)
	return '\n'.join(rslt)

	# mysearch("who is the best rapper in the world?")


	# """# chat example"""
	# def chat(message, history):
	# history = history or []
	# message = message.lower()

	# responses = get_text(message)
	# for response in responses:
	# history.append((message, response))
	# return history, history


	# with gr.Blocks(css=CSS) as demo:
	# history_state = gr.State()
	# gr.Markdown('# WikiBot')
	# title = 'Wikipedia Chatbot'
	# description = 'chatbot with search on Wikipedia'
	# with gr.Row():
	# chatbot = gr.Chatbot()
	# with gr.Row():
	# message = gr.Textbox(label='Input your question here:',
	# placeholder='How many countries are in Europe?',
	# lines=1)
	# submit = gr.Button(value='Send',
	# variant='secondary').style(full_width=False)
	# submit.click(chat,
	# inputs=[message, history_state],
	# outputs=[chatbot, history_state])
	# gr.Examples(
	# examples=["How many countries are in Europe?",
	# "Was Roman Emperor Constantine I a Christian?",
	# "Who is the best rapper in the world?"],
	# inputs=message
	# )

	# demo.launch()

	# OPENAI_API_KEY = "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"
	# NOTE using locally

	# AWS keys
	aws_access_key_id = "AKIA3JRWKI2EOCFNK3FT"
	aws_secret_access_key = "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"
	aws_region_name = "us-east-2"

	os.environ["AWS_ACCESS_KEY_ID"] = aws_access_key_id
	os.environ["AWS_SECRET_ACCESS_KEY"] = aws_secret_access_key
	os.environ["AWS_DEFAULT_REGION"] = aws_region_name



	# exhumana api key
	# todo: may need to pay to get one
	os.environ['EXHUMAN_API_KEY'] = ''

	# news, tmdb keys
	os.environ["NEWS_API_KEY"] = ''
	os.environ["TMDB_BEARER_TOKEN"] = ''

	news_api_key = os.environ["NEWS_API_KEY"]
	tmdb_bearer_token = os.environ["TMDB_BEARER_TOKEN"]

	TOOLS_LIST = ['serpapi', 'wolfram-alpha', 'pal-math', 'pal-colored-objects', 'news-api', 'tmdb-api',
	'open-meteo-api'] # 'google-search'
	# TOOLS_DEFAULT_LIST = ['mysearch', 'serpapi', 'pal-math']
	TOOLS_DEFAULT_LIST = [mysearch]
	BUG_FOUND_MSG = "Congratulations, you've found a bug in this application!"
	AUTH_ERR_MSG = "Please paste your OpenAI key from openai.com to use this application. It is not necessary to hit a button or key after pasting it."
	MAX_TOKENS = 512
	TEMPERATURE = 0

	LOOPING_TALKING_HEAD = "videos/humancare.mp4"
	TALKING_HEAD_WIDTH = "192"
	MAX_TALKING_HEAD_TEXT_LENGTH = 155

	# Pertains to Express-inator functionality
	NUM_WORDS_DEFAULT = 0
	MAX_WORDS = 400
	FORMALITY_DEFAULT = "N/A"
	TEMPERATURE_DEFAULT = 0.5
	EMOTION_DEFAULT = "N/A"
	LANG_LEVEL_DEFAULT = "N/A"
	TRANSLATE_TO_DEFAULT = "N/A"
	LITERARY_STYLE_DEFAULT = "N/A"
	PROMPT_TEMPLATE = PromptTemplate(
	input_variables=["original_words", "num_words", "formality",
	"emotions", "lang_level", "translate_to", "literary_style"],
	template="Restate {num_words}{formality}{emotions}{lang_level}{translate_to}{literary_style}the following: \n{original_words}\n",
	)

	POLLY_VOICE_DATA = PollyVoiceData()
	AZURE_VOICE_DATA = AzureVoiceData()
	VOICE_GENDER = 'Female' # "Male"

	# Pertains to WHISPER functionality
	WHISPER_DETECT_LANG = "Detect language"


	# UNCOMMENT TO USE WHISPER
	warnings.filterwarnings("ignore")
	WHISPER_MODEL = whisper.load_model("tiny")
	print("WHISPER_MODEL", WHISPER_MODEL)


	# gradio settings
	# css
	CSS = ".gradio-container {background-color: lightgray}"

	# placeholder for chat text input
	PLACEHOLDER = "What is my plan benefit?",

	# example questions
	EXAMPLES = ["How many people live in Canada?",
	"What is 2 to the 30th power?",
	"If x+y=10 and x-y=4, what are x and y?",
	"How much did it rain in SF today?",
	"Get me information about the movie 'Avatar'",
	"What are the top tech headlines in the US?",
	"On the desk, you see two blue booklets, two purple booklets, and two yellow pairs of sunglasses - "
	"if I remove all the pairs of sunglasses from the desk, how many purple items remain on it?"]
	AUTHORS = """
	<p>This application, developed by Greg Hayworth, Srikanth Tangelloju, Lincoln Snyder, Michal Piekarczyk, and Xingde Jiang,
	demonstrates a conversational agent implemented with OpenAI GPT-3.5 and LangChain.
	When necessary, it leverages tools for complex math, searching the internet, and accessing news and weather.
	Uses talking heads from <a href='https://exh.ai/'>Ex-Human</a>.
	For faster inference without waiting in queue, you may duplicate the space.
	</p>"""
	# UNCOMMENT TO USE WHISPER


	def transcribe(aud_inp, whisper_lang):
	if aud_inp is None:
	return ""
	aud = whisper.load_audio(aud_inp)
	aud = whisper.pad_or_trim(aud)
	mel = whisper.log_mel_spectrogram(aud).to(WHISPER_MODEL.device)
	_, probs = WHISPER_MODEL.detect_language(mel)
	options = whisper.DecodingOptions(fp16=False)
	if whisper_lang != WHISPER_DETECT_LANG:
	whisper_lang_code = POLLY_VOICE_DATA.get_whisper_lang_code(
	whisper_lang)
	options = whisper.DecodingOptions(
	fp16=False, language=whisper_lang_code)
	result = whisper.decode(WHISPER_MODEL, mel, options)
	print("result.text", result.text)
	result_text = ""
	if result and result.text:
	result_text = result.text
	return result_text


	# Pertains to Express-inator functionality
	def transform_text(desc, express_chain, num_words, formality,
	anticipation_level, joy_level, trust_level,
	fear_level, surprise_level, sadness_level, disgust_level, anger_level,
	lang_level, translate_to, literary_style):
	num_words_prompt = ""
	if num_words and int(num_words) != 0:
	num_words_prompt = "using up to " + str(num_words) + " words, "

	# Change some arguments to lower case
	formality = formality.lower()
	anticipation_level = anticipation_level.lower()
	joy_level = joy_level.lower()
	trust_level = trust_level.lower()
	fear_level = fear_level.lower()
	surprise_level = surprise_level.lower()
	sadness_level = sadness_level.lower()
	disgust_level = disgust_level.lower()
	anger_level = anger_level.lower()

	formality_str = ""
	if formality != "n/a":
	formality_str = "in a " + formality + " manner, "

	# put all emotions into a list
	emotions = []
	if anticipation_level != "n/a":
	emotions.append(anticipation_level)
	if joy_level != "n/a":
	emotions.append(joy_level)
	if trust_level != "n/a":
	emotions.append(trust_level)
	if fear_level != "n/a":
	emotions.append(fear_level)
	if surprise_level != "n/a":
	emotions.append(surprise_level)
	if sadness_level != "n/a":
	emotions.append(sadness_level)
	if disgust_level != "n/a":
	emotions.append(disgust_level)
	if anger_level != "n/a":
	emotions.append(anger_level)

	emotions_str = ""
	if len(emotions) > 0:
	if len(emotions) == 1:
	emotions_str = "with emotion of " + emotions[0] + ", "
	else:
	emotions_str = "with emotions of " + \
	", ".join(emotions[:-1]) + " and " + emotions[-1] + ", "

	lang_level_str = ""
	if lang_level != LANG_LEVEL_DEFAULT:
	lang_level_str = "at a " + lang_level + \
	" level, " if translate_to == TRANSLATE_TO_DEFAULT else ""

	translate_to_str = ""
	if translate_to != TRANSLATE_TO_DEFAULT:
	translate_to_str = "translated to " + \
	("" if lang_level == TRANSLATE_TO_DEFAULT else lang_level +
	" level ") + translate_to + ", "

	literary_style_str = ""
	if literary_style != LITERARY_STYLE_DEFAULT:
	if literary_style == "Prose":
	literary_style_str = "as prose, "
	elif literary_style == "Summary":
	literary_style_str = "as a summary, "
	elif literary_style == "Outline":
	literary_style_str = "as an outline numbers and lower case letters, "
	elif literary_style == "Bullets":
	literary_style_str = "as bullet points using bullets, "
	elif literary_style == "Poetry":
	literary_style_str = "as a poem, "
	elif literary_style == "Haiku":
	literary_style_str = "as a haiku, "
	elif literary_style == "Limerick":
	literary_style_str = "as a limerick, "
	elif literary_style == "Joke":
	literary_style_str = "as a very funny joke with a setup and punchline, "
	elif literary_style == "Knock-knock":
	literary_style_str = "as a very funny knock-knock joke, "

	formatted_prompt = PROMPT_TEMPLATE.format(
	original_words=desc,
	num_words=num_words_prompt,
	formality=formality_str,
	emotions=emotions_str,
	lang_level=lang_level_str,
	translate_to=translate_to_str,
	literary_style=literary_style_str
	)

	trans_instr = num_words_prompt + formality_str + emotions_str + \
	lang_level_str + translate_to_str + literary_style_str
	if express_chain and len(trans_instr.strip()) > 0:
	generated_text = express_chain.run(
	{'original_words': desc, 'num_words': num_words_prompt, 'formality': formality_str,
	'emotions': emotions_str, 'lang_level': lang_level_str, 'translate_to': translate_to_str,
	'literary_style': literary_style_str}).strip()
	else:
	print("Not transforming text")
	generated_text = desc

	# replace all newlines with <br> in generated_text
	generated_text = generated_text.replace("\n", "\n\n")

	prompt_plus_generated = "GPT prompt: " + \
	formatted_prompt + "\n\n" + generated_text

	print("\n==== date/time: " + str(datetime.datetime.now() -
	datetime.timedelta(hours=5)) + " ====")
	print("prompt_plus_generated: " + prompt_plus_generated)

	return generated_text


	def load_chain(tools_list, llm):
	chain = None
	express_chain = None
	if llm:
	print("\ntools_list", tools_list)
	tool_names = tools_list
	# tools = load_tools(tool_names, llm=llm, news_api_key=news_api_key,
	# tmdb_bearer_token=tmdb_bearer_token)
	# tools = load_tools(tool_names, llm=llm)
	tools = [mysearch]

	memory = ConversationBufferMemory(memory_key="chat_history")

	chain = initialize_agent(
	tools, llm, agent="conversational-react-description", verbose=True, memory=memory)
	express_chain = LLMChain(llm=llm, prompt=PROMPT_TEMPLATE, verbose=True)

	return chain, express_chain


	def set_openai_api_key(api_key):
	"""Set the api key and return chain.
	If no api_key, then None is returned.
	"""
	# if api_key and api_key.startswith("sk-") and len(api_key) > 50:
	# # os.environ["OPENAI_API_KEY"] = api_key
	# os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
	# llm = OpenAI(temperature=TEMPERATURE, max_tokens=MAX_TOKENS)
	# chain, express_chain = load_chain(TOOLS_DEFAULT_LIST, llm)
	# os.environ["OPENAI_API_KEY"] = ""
	# return chain, express_chain, llm
	# return None, None, None

	os.environ["OPENAI_API_KEY"] = api_key
	llm = OpenAI(temperature=TEMPERATURE, max_tokens=MAX_TOKENS)
	chain, express_chain = load_chain(TOOLS_DEFAULT_LIST, llm)

	# NOTE hmm this looks weird!
	# os.environ["OPENAI_API_KEY"] = ""
	return chain, express_chain, llm


	def run_chain(chain, inp, capture_hidden_text):
	output = ""
	hidden_text = None
	if capture_hidden_text:
	error_msg = None
	tmp = sys.stdout
	hidden_text_io = StringIO()
	sys.stdout = hidden_text_io

	try:
	output = chain.run(input=inp)
	except AuthenticationError as ae:
	print("run_chain oops, in capture_hidden_text")
	print(repr(ae))
	error_msg = AUTH_ERR_MSG
	except RateLimitError as rle:
	error_msg = "\n\nRateLimitError: " + str(rle)
	except ValueError as ve:
	error_msg = "\n\nValueError: " + str(ve)
	except InvalidRequestError as ire:
	error_msg = "\n\nInvalidRequestError: " + str(ire)
	except Exception as e:
	error_msg = "\n\n" + BUG_FOUND_MSG + ":\n\n" + str(e)

	sys.stdout = tmp
	hidden_text = hidden_text_io.getvalue()

	# remove escape characters from hidden_text
	hidden_text = re.sub(r'\x1b[^m]*m', '', hidden_text)

	# remove "Entering new AgentExecutor chain..." from hidden_text
	hidden_text = re.sub(
	r"Entering new AgentExecutor chain...\n", "", hidden_text)

	# remove "Finished chain." from hidden_text
	hidden_text = re.sub(r"Finished chain.", "", hidden_text)

	# Add newline after "Thought:" "Action:" "Observation:" "Input:" and "AI:"
	hidden_text = re.sub(r"Thought:", "\n\nThought:", hidden_text)
	hidden_text = re.sub(r"Action:", "\n\nAction:", hidden_text)
	hidden_text = re.sub(r"Observation:", "\n\nObservation:", hidden_text)
	hidden_text = re.sub(r"Input:", "\n\nInput:", hidden_text)
	hidden_text = re.sub(r"AI:", "\n\nAI:", hidden_text)

	if error_msg:
	hidden_text += error_msg

	print("hidden_text: ", hidden_text)
	else:
	try:
	output = chain.run(input=inp)
	except AuthenticationError as ae:
	print("run_chain oops, in else")
	print(repr(ae))
	output = AUTH_ERR_MSG
	except RateLimitError as rle:
	output = "\n\nRateLimitError: " + str(rle)
	except ValueError as ve:
	output = "\n\nValueError: " + str(ve)
	except InvalidRequestError as ire:
	output = "\n\nInvalidRequestError: " + str(ire)
	except Exception as e:
	output = "\n\n" + BUG_FOUND_MSG + ":\n\n" + str(e)

	return output, hidden_text


	class ChatWrapper:

	def __init__(self):
	self.lock = Lock()

	def __call__(
	self, api_key: str, inp: str, history: Optional[Tuple[str, str]], chain: Optional[ConversationChain],
	trace_chain: bool, speak_text: bool, talking_head: bool, monologue: bool, express_chain: Optional[LLMChain],
	num_words, formality, anticipation_level, joy_level, trust_level,
	fear_level, surprise_level, sadness_level, disgust_level, anger_level,
	lang_level, translate_to, literary_style
	):
	"""Execute the chat functionality."""
	self.lock.acquire()
	try:
	print("\n==== date/time: " + str(datetime.datetime.now()) + " ====")
	print("inp: " + inp)
	print("trace_chain: ", trace_chain)
	print("speak_text: ", speak_text)
	print("talking_head: ", talking_head)
	print("monologue: ", monologue)
	history = history or []
	# If chain is None, that is because no API key was provided.
	output = AUTH_ERR_MSG
	hidden_text = output

	if chain:
	# Set OpenAI key
	import openai
	openai.api_key = os.environ["OPENAI_API_KEY"] # OPENAI_API_KEY
	# openai.api_key = api_key
	if not monologue:
	output, hidden_text = run_chain(
	chain, inp, capture_hidden_text=trace_chain)
	else:
	output, hidden_text = inp, None

	output = transform_text(output, express_chain, num_words, formality, anticipation_level, joy_level,
	trust_level,
	fear_level, surprise_level, sadness_level, disgust_level, anger_level,
	lang_level, translate_to, literary_style)

	text_to_display = output
	if trace_chain:
	text_to_display = hidden_text + "\n\n" + output
	history.append((inp, text_to_display))

	html_video, temp_file, html_audio, temp_aud_file = None, None, None, None
	if speak_text:
	if talking_head:
	if len(output) <= MAX_TALKING_HEAD_TEXT_LENGTH:
	html_video, temp_file = do_html_video_speak(
	output, translate_to)
	else:
	temp_file = LOOPING_TALKING_HEAD
	html_video = create_html_video(
	temp_file, TALKING_HEAD_WIDTH)
	html_audio, temp_aud_file = do_html_audio_speak(
	output, translate_to)
	else:
	html_audio, temp_aud_file = do_html_audio_speak(
	output, translate_to)
	else:
	if talking_head:
	temp_file = LOOPING_TALKING_HEAD
	html_video = create_html_video(
	temp_file, TALKING_HEAD_WIDTH)
	else:
	# html_audio, temp_aud_file = do_html_audio_speak(output, translate_to)
	# html_video = create_html_video(temp_file, "128")
	pass

	except Exception as e:
	raise e
	finally:
	self.lock.release()
	return history, history, html_video, temp_file, html_audio, temp_aud_file, ""
	# return history, history, html_audio, temp_aud_file, ""


	chat = ChatWrapper()


	def do_html_audio_speak(words_to_speak, polly_language):
	polly_client = boto3.Session(
	aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"],
	aws_secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"],
	region_name=os.environ["AWS_DEFAULT_REGION"]
	).client('polly')

	voice_id, language_code, engine = POLLY_VOICE_DATA.get_voice(
	polly_language, VOICE_GENDER)
	if not voice_id:
	voice_id = "Joanna"
	# voice_id = "Matthew"
	language_code = "en-US"
	engine = NEURAL_ENGINE
	response = polly_client.synthesize_speech(
	Text=words_to_speak,
	OutputFormat='mp3',
	VoiceId=voice_id,
	LanguageCode=language_code,
	Engine=engine
	)

	html_audio = '<pre>no audio</pre>'

	# Save the audio stream returned by Amazon Polly on Lambda's temp directory
	if "AudioStream" in response:
	with closing(response["AudioStream"]) as stream:
	# output = os.path.join("/tmp/", "speech.mp3")

	try:
	with open('audios/tempfile.mp3', 'wb') as f:
	f.write(stream.read())
	temp_aud_file = gr.File("audios/tempfile.mp3")
	temp_aud_file_url = "/file=" + temp_aud_file.value['name']
	html_audio = f'<audio autoplay><source src={temp_aud_file_url} type="audio/mp3"></audio>'
	except IOError as error:
	# Could not write to file, exit gracefully
	print(error)
	return None, None
	else:
	# The response didn't contain audio data, exit gracefully
	print("Could not stream audio")
	return None, None

	return html_audio, "audios/tempfile.mp3"


	def create_html_video(file_name, width):
	temp_file_url = "/file=" + tmp_file.value['name']
	html_video = f'<video width={width} height={width} autoplay muted loop><source src={temp_file_url} type="video/mp4" poster="humancare.jpg"></video>'
	return html_video


	def do_html_video_speak(words_to_speak, azure_language):
	azure_voice = AZURE_VOICE_DATA.get_voice(azure_language, VOICE_GENDER)
	if not azure_voice:
	azure_voice = "en-US-ChristopherNeural"

	headers = {"Authorization": f"Bearer {os.environ['EXHUMAN_API_KEY']}"}
	body = {
	'bot_name': 'humancare',
	'bot_response': words_to_speak,
	'azure_voice': azure_voice,
	'azure_style': 'friendly',
	'animation_pipeline': 'high_speed',
	}
	api_endpoint = "https://api.exh.ai/animations/v1/generate_lipsync"
	res = requests.post(api_endpoint, json=body, headers=headers)
	print("res.status_code: ", res.status_code)

	html_video = '<pre>no video</pre>'
	if isinstance(res.content, bytes):
	response_stream = io.BytesIO(res.content)
	print("len(res.content)): ", len(res.content))

	with open('videos/tempfile.mp4', 'wb') as f:
	f.write(response_stream.read())
	temp_file = gr.File("videos/tempfile.mp4")
	temp_file_url = "/file=" + temp_file.value['name']
	html_video = f'<video width={TALKING_HEAD_WIDTH} height={TALKING_HEAD_WIDTH} autoplay><source src={temp_file_url} type="video/mp4" poster="humancare.jpg"></video>'
	else:
	print('video url unknown')
	return html_video, "videos/tempfile.mp4"


	def update_selected_tools(widget, state, llm):
	if widget:
	state = widget
	chain, express_chain = load_chain(state, llm)
	return state, llm, chain, express_chain


	def update_talking_head(widget, state):
	if widget:
	state = widget

	video_html_talking_head = create_html_video(
	LOOPING_TALKING_HEAD, TALKING_HEAD_WIDTH)
	return state, video_html_talking_head
	else:
	# return state, create_html_video(LOOPING_TALKING_HEAD, "32")
	return None, "<pre></pre>"


	def update_foo(widget, state):
	if widget:
	state = widget
	return state


	with gr.Blocks(css=CSS) as block:
	llm_state = gr.State()
	history_state = gr.State()
	chain_state = gr.State()
	express_chain_state = gr.State()
	tools_list_state = gr.State(TOOLS_DEFAULT_LIST)
	trace_chain_state = gr.State(False)
	speak_text_state = gr.State(False)
	talking_head_state = gr.State(True)
	# Takes the input and repeats it back to the user, optionally transforming it.
	monologue_state = gr.State(False)

	# Pertains to Express-inator functionality
	num_words_state = gr.State(NUM_WORDS_DEFAULT)
	formality_state = gr.State(FORMALITY_DEFAULT)
	anticipation_level_state = gr.State(EMOTION_DEFAULT)
	joy_level_state = gr.State(EMOTION_DEFAULT)
	trust_level_state = gr.State(EMOTION_DEFAULT)
	fear_level_state = gr.State(EMOTION_DEFAULT)
	surprise_level_state = gr.State(EMOTION_DEFAULT)
	sadness_level_state = gr.State(EMOTION_DEFAULT)
	disgust_level_state = gr.State(EMOTION_DEFAULT)
	anger_level_state = gr.State(EMOTION_DEFAULT)
	lang_level_state = gr.State(LANG_LEVEL_DEFAULT)
	translate_to_state = gr.State(TRANSLATE_TO_DEFAULT)
	literary_style_state = gr.State(LITERARY_STYLE_DEFAULT)

	# Pertains to WHISPER functionality
	whisper_lang_state = gr.State(WHISPER_DETECT_LANG)

	with gr.Tab("Chat"):
	with gr.Row():
	with gr.Column():
	gr.HTML(
	"""<b><center>GPT + WolframAlpha + Whisper</center></b>
	<p><center>New feature in <b>Translate to</b>: Choose <b>Language level</b> (e.g. for conversation practice or explain like I'm five)</center></p>""")

	openai_api_key_textbox = gr.Textbox(placeholder="Paste your OpenAI API key (sk-...)",
	show_label=False, lines=1, type='password')
	with gr.Row():
	with gr.Column(scale=1, min_width=TALKING_HEAD_WIDTH, visible=True):
	speak_text_cb = gr.Checkbox(label="Enable speech", value=False)
	speak_text_cb.change(update_foo, inputs=[speak_text_cb, speak_text_state],
	outputs=[speak_text_state])

	my_file = gr.File(label="Upload a file",
	type="file", visible=False)
	tmp_file = gr.File(LOOPING_TALKING_HEAD, visible=False)
	# tmp_file_url = "/file=" + tmp_file.value['name']
	htm_video = create_html_video(
	LOOPING_TALKING_HEAD, TALKING_HEAD_WIDTH)
	video_html = gr.HTML(htm_video)

	# my_aud_file = gr.File(label="Audio file", type="file", visible=True)
	tmp_aud_file = gr.File("audios/tempfile.mp3", visible=False)
	tmp_aud_file_url = "/file=" + tmp_aud_file.value['name']
	htm_audio = f'<audio><source src={tmp_aud_file_url} type="audio/mp3"></audio>'
	audio_html = gr.HTML(htm_audio)

	with gr.Column(scale=7):
	chatbot = gr.Chatbot()

	with gr.Row():
	message = gr.Textbox(label="What's on your mind??",
	placeholder=PLACEHOLDER,
	lines=1)
	submit = gr.Button(value="Send", variant="secondary").style(
	full_width=False)

	# UNCOMMENT TO USE WHISPER
	with gr.Row():
	audio_comp = gr.Microphone(source="microphone", type="filepath", label="Just say it!",
	interactive=True, streaming=False)
	audio_comp.change(transcribe, inputs=[
	audio_comp, whisper_lang_state], outputs=[message])

	gr.Examples(
	examples=EXAMPLES,
	inputs=message
	)

	with gr.Tab("Settings"):
	tools_cb_group = gr.CheckboxGroup(label="Tools:", choices=TOOLS_LIST,
	value=TOOLS_DEFAULT_LIST)
	tools_cb_group.change(update_selected_tools,
	inputs=[tools_cb_group,
	tools_list_state, llm_state],
	outputs=[tools_list_state, llm_state, chain_state, express_chain_state])

	trace_chain_cb = gr.Checkbox(
	label="Show reasoning chain in chat bubble", value=False)
	trace_chain_cb.change(update_foo, inputs=[trace_chain_cb, trace_chain_state],
	outputs=[trace_chain_state])

	# speak_text_cb = gr.Checkbox(label="Speak text from agent", value=False)
	# speak_text_cb.change(update_foo, inputs=[speak_text_cb, speak_text_state],
	# outputs=[speak_text_state])

	talking_head_cb = gr.Checkbox(label="Show talking head", value=True)
	talking_head_cb.change(update_talking_head, inputs=[talking_head_cb, talking_head_state],
	outputs=[talking_head_state, video_html])

	monologue_cb = gr.Checkbox(label="Babel fish mode (translate/restate what you enter, no conversational agent)",
	value=False)
	monologue_cb.change(update_foo, inputs=[monologue_cb, monologue_state],
	outputs=[monologue_state])

	with gr.Tab("Whisper STT"):
	whisper_lang_radio = gr.Radio(label="Whisper speech-to-text language:", choices=[
	WHISPER_DETECT_LANG, "Arabic", "Arabic (Gulf)", "Catalan", "Chinese (Cantonese)", "Chinese (Mandarin)",
	"Danish", "Dutch", "English (Australian)", "English (British)", "English (Indian)", "English (New Zealand)",
	"English (South African)", "English (US)", "English (Welsh)", "Finnish", "French", "French (Canadian)",
	"German", "German (Austrian)", "Georgian", "Hindi", "Icelandic", "Indonesian", "Italian", "Japanese",
	"Korean", "Norwegian", "Polish",
	"Portuguese (Brazilian)", "Portuguese (European)", "Romanian", "Russian", "Spanish (European)",
	"Spanish (Mexican)", "Spanish (US)", "Swedish", "Turkish", "Ukrainian", "Welsh"],
	value=WHISPER_DETECT_LANG)

	whisper_lang_radio.change(update_foo,
	inputs=[whisper_lang_radio,
	whisper_lang_state],
	outputs=[whisper_lang_state])

	with gr.Tab("Translate to"):
	lang_level_radio = gr.Radio(label="Language level:", choices=[
	LANG_LEVEL_DEFAULT, "1st grade", "2nd grade", "3rd grade", "4th grade", "5th grade", "6th grade",
	"7th grade", "8th grade", "9th grade", "10th grade", "11th grade", "12th grade", "University"],
	value=LANG_LEVEL_DEFAULT)
	lang_level_radio.change(update_foo, inputs=[lang_level_radio, lang_level_state],
	outputs=[lang_level_state])

	translate_to_radio = gr.Radio(label="Language:", choices=[
	TRANSLATE_TO_DEFAULT, "Arabic", "Arabic (Gulf)", "Catalan", "Chinese (Cantonese)", "Chinese (Mandarin)",
	"Danish", "Dutch", "English (Australian)", "English (British)", "English (Indian)", "English (New Zealand)",
	"English (South African)", "English (US)", "English (Welsh)", "Finnish", "French", "French (Canadian)",
	"German", "German (Austrian)", "Georgian", "Hindi", "Icelandic", "Indonesian", "Italian", "Japanese",
	"Korean", "Norwegian", "Polish",
	"Portuguese (Brazilian)", "Portuguese (European)", "Romanian", "Russian", "Spanish (European)",
	"Spanish (Mexican)", "Spanish (US)", "Swedish", "Turkish", "Ukrainian", "Welsh",
	"emojis", "Gen Z slang", "how the stereotypical Karen would say it", "Klingon",
	"Pirate", "Strange Planet expospeak technical talk", "Yoda"],
	value=TRANSLATE_TO_DEFAULT)

	translate_to_radio.change(update_foo,
	inputs=[translate_to_radio,
	translate_to_state],
	outputs=[translate_to_state])

	with gr.Tab("Formality"):
	formality_radio = gr.Radio(label="Formality:",
	choices=[FORMALITY_DEFAULT,
	"Casual", "Polite", "Honorific"],
	value=FORMALITY_DEFAULT)
	formality_radio.change(update_foo,
	inputs=[formality_radio, formality_state],
	outputs=[formality_state])

	with gr.Tab("Lit style"):
	literary_style_radio = gr.Radio(label="Literary style:", choices=[
	LITERARY_STYLE_DEFAULT, "Prose", "Summary", "Outline", "Bullets", "Poetry", "Haiku", "Limerick", "Joke",
	"Knock-knock"],
	value=LITERARY_STYLE_DEFAULT)

	literary_style_radio.change(update_foo,
	inputs=[literary_style_radio,
	literary_style_state],
	outputs=[literary_style_state])

	with gr.Tab("Emotions"):
	anticipation_level_radio = gr.Radio(label="Anticipation level:",
	choices=[
	EMOTION_DEFAULT, "Interest", "Anticipation", "Vigilance"],
	value=EMOTION_DEFAULT)
	anticipation_level_radio.change(update_foo,
	inputs=[anticipation_level_radio,
	anticipation_level_state],
	outputs=[anticipation_level_state])

	joy_level_radio = gr.Radio(label="Joy level:",
	choices=[EMOTION_DEFAULT,
	"Serenity", "Joy", "Ecstasy"],
	value=EMOTION_DEFAULT)
	joy_level_radio.change(update_foo,
	inputs=[joy_level_radio, joy_level_state],
	outputs=[joy_level_state])

	trust_level_radio = gr.Radio(label="Trust level:",
	choices=[
	EMOTION_DEFAULT, "Acceptance", "Trust", "Admiration"],
	value=EMOTION_DEFAULT)
	trust_level_radio.change(update_foo,
	inputs=[trust_level_radio, trust_level_state],
	outputs=[trust_level_state])

	fear_level_radio = gr.Radio(label="Fear level:",
	choices=[EMOTION_DEFAULT,
	"Apprehension", "Fear", "Terror"],
	value=EMOTION_DEFAULT)
	fear_level_radio.change(update_foo,
	inputs=[fear_level_radio, fear_level_state],
	outputs=[fear_level_state])

	surprise_level_radio = gr.Radio(label="Surprise level:",
	choices=[
	EMOTION_DEFAULT, "Distraction", "Surprise", "Amazement"],
	value=EMOTION_DEFAULT)
	surprise_level_radio.change(update_foo,
	inputs=[surprise_level_radio,
	surprise_level_state],
	outputs=[surprise_level_state])

	sadness_level_radio = gr.Radio(label="Sadness level:",
	choices=[
	EMOTION_DEFAULT, "Pensiveness", "Sadness", "Grief"],
	value=EMOTION_DEFAULT)
	sadness_level_radio.change(update_foo,
	inputs=[sadness_level_radio,
	sadness_level_state],
	outputs=[sadness_level_state])

	disgust_level_radio = gr.Radio(label="Disgust level:",
	choices=[EMOTION_DEFAULT,
	"Boredom", "Disgust", "Loathing"],
	value=EMOTION_DEFAULT)
	disgust_level_radio.change(update_foo,
	inputs=[disgust_level_radio,
	disgust_level_state],
	outputs=[disgust_level_state])

	anger_level_radio = gr.Radio(label="Anger level:",
	choices=[EMOTION_DEFAULT,
	"Annoyance", "Anger", "Rage"],
	value=EMOTION_DEFAULT)
	anger_level_radio.change(update_foo,
	inputs=[anger_level_radio, anger_level_state],
	outputs=[anger_level_state])

	with gr.Tab("Max words"):
	num_words_slider = gr.Slider(label="Max number of words to generate (0 for don't care)",
	value=NUM_WORDS_DEFAULT, minimum=0, maximum=MAX_WORDS, step=10)
	num_words_slider.change(update_foo,
	inputs=[num_words_slider, num_words_state],
	outputs=[num_words_state])

	gr.HTML(AUTHORS)

	# gr.HTML("""
	# <form action="https://www.paypal.com/donate" method="post" target="_blank">
	# <input type="hidden" name="business" value="AK8BVNALBXSPQ" />
	# <input type="hidden" name="no_recurring" value="0" />
	# <input type="hidden" name="item_name" value="Please consider helping to defray the cost of APIs such as SerpAPI and WolframAlpha that this app uses." />
	# <input type="hidden" name="currency_code" value="USD" />
	# <input type="image" src="https://www.paypalobjects.com/en_US/i/btn/btn_donate_LG.gif" border="0" name="submit" title="PayPal - The safer, easier way to pay online!" alt="Donate with PayPal button" />
	# <img alt="" border="0" src="https://www.paypal.com/en_US/i/scr/pixel.gif" width="1" height="1" />
	# </form>
	# """)

	gr.HTML("""<center>
	<a href="https://huggingface.co/spaces/gfhayworth/hack_qa?duplicate=true">
	<img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
	Powered by <a href='https://github.com/hwchase17/langchain'>LangChain 🦜️🔗</a>
	</center>""")

	message.submit(chat, inputs=[openai_api_key_textbox, message, history_state, chain_state, trace_chain_state,
	speak_text_state, talking_head_state, monologue_state,
	express_chain_state, num_words_state, formality_state,
	anticipation_level_state, joy_level_state, trust_level_state, fear_level_state,
	surprise_level_state, sadness_level_state, disgust_level_state, anger_level_state,
	lang_level_state, translate_to_state, literary_style_state],
	outputs=[chatbot, history_state, video_html, my_file, audio_html, tmp_aud_file, message])
	# outputs=[chatbot, history_state, audio_html, tmp_aud_file, message])

	submit.click(chat, inputs=[openai_api_key_textbox, message, history_state, chain_state, trace_chain_state,
	speak_text_state, talking_head_state, monologue_state,
	express_chain_state, num_words_state, formality_state,
	anticipation_level_state, joy_level_state, trust_level_state, fear_level_state,
	surprise_level_state, sadness_level_state, disgust_level_state, anger_level_state,
	lang_level_state, translate_to_state, literary_style_state],
	outputs=[chatbot, history_state, video_html, my_file, audio_html, tmp_aud_file, message])
	# outputs=[chatbot, history_state, audio_html, tmp_aud_file, message])

	openai_api_key_textbox.change(set_openai_api_key,
	inputs=[openai_api_key_textbox],
	outputs=[chain_state, express_chain_state, llm_state])

	block.launch(debug=True)