import io
import os
import ssl
from contextlib import closing
from typing import Optional, Tuple
import datetime
import boto3
import gradio as gr
import requests
# UNCOMMENT TO USE WHISPER
import warnings
import whisper
from langchain import ConversationChain, LLMChain
from langchain.agents import load_tools, initialize_agent
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.llms import OpenAI, OpenAIChat
from threading import Lock
# Console to variable
from io import StringIO
import sys
import re
from openai.error import AuthenticationError, InvalidRequestError, RateLimitError
# Pertains to Express-inator functionality
from langchain.prompts import PromptTemplate
from polly_utils import PollyVoiceData, NEURAL_ENGINE
from azure_utils import AzureVoiceData
# Pertains to question answering functionality
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores.faiss import FAISS
from langchain.docstore.document import Document
from langchain.chains.question_answering import load_qa_chain
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'] # 'google-search','news-api','tmdb-api','open-meteo-api'
TOOLS_DEFAULT_LIST = ['serpapi']
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."
AUTH_ERR_MSG = "Please paste your OpenAI key from openai.com to use this application. "
MAX_TOKENS = 512
LOOPING_TALKING_HEAD = "videos/Masahiro.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 = "University"
TRANSLATE_TO_DEFAULT = "Chinese (Mandarin)"
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",
)
FORCE_TRANSLATE_DEFAULT = True
USE_GPT4_DEFAULT = False
POLLY_VOICE_DATA = PollyVoiceData()
AZURE_VOICE_DATA = AzureVoiceData()
# Pertains to WHISPER functionality
WHISPER_DETECT_LANG = "Chinese (Mandarin)"
# UNCOMMENT TO USE WHISPER
warnings.filterwarnings("ignore")
WHISPER_MODEL = whisper.load_model("tiny")
print("WHISPER_MODEL", WHISPER_MODEL)
# 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()
if whisper_lang != WHISPER_DETECT_LANG:
whisper_lang_code = POLLY_VOICE_DATA.get_whisper_lang_code(whisper_lang)
options = whisper.DecodingOptions(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
# Temporarily address Wolfram Alpha SSL certificate issue
ssl._create_default_https_context = ssl._create_unverified_context
# TEMPORARY FOR TESTING
def transcribe_dummy(aud_inp_tb, whisper_lang):
if aud_inp_tb 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()
# options = whisper.DecodingOptions(language="ja")
# result = whisper.decode(WHISPER_MODEL, mel, options)
result_text = "Whisper will detect language"
if whisper_lang != WHISPER_DETECT_LANG:
whisper_lang_code = POLLY_VOICE_DATA.get_whisper_lang_code(whisper_lang)
result_text = f"Whisper will use lang code: {whisper_lang_code}"
print("result_text", result_text)
return aud_inp_tb
# 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, force_translate):
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 level that a person in " + lang_level + " can easily comprehend, " if translate_to == TRANSLATE_TO_DEFAULT else ""
translate_to_str = ""
if translate_to != TRANSLATE_TO_DEFAULT and (force_translate or lang_level != LANG_LEVEL_DEFAULT):
translate_to_str = "translated to " + translate_to + (
"" if lang_level == LANG_LEVEL_DEFAULT else " at a level that a person in " + lang_level + " can easily comprehend") + ", "
literary_style_str = ""
if literary_style != LITERARY_STYLE_DEFAULT:
if literary_style == "Prose":
literary_style_str = "as prose, "
if literary_style == "Story":
literary_style_str = "as a story, "
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 == "Rap":
literary_style_str = "as a rap, "
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, "
elif literary_style == "FAQ":
literary_style_str = "as a FAQ with several questions and answers, "
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 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
memory = 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)
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, memory
def set_openai_api_key(api_key, use_gpt4):
"""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
print("\n\n ++++++++++++++ Setting OpenAI API key ++++++++++++++ \n\n")
print(str(datetime.datetime.now()) + ": Before OpenAI, OPENAI_API_KEY length: " + str(
len(os.environ["OPENAI_API_KEY"])))
if use_gpt4:
llm = OpenAIChat(temperature=0, max_tokens=MAX_TOKENS, model_name="gpt-4")
print("Trying to use llm OpenAIChat with gpt-4")
else:
print("Trying to use llm OpenAI with text-davinci-003")
llm = OpenAI(temperature=0, max_tokens=MAX_TOKENS, model_name="text-davinci-003")
print(str(datetime.datetime.now()) + ": After OpenAI, OPENAI_API_KEY length: " + str(
len(os.environ["OPENAI_API_KEY"])))
chain, express_chain, memory = load_chain(TOOLS_DEFAULT_LIST, llm)
# Pertains to question answering functionality
embeddings = OpenAIEmbeddings()
if use_gpt4:
qa_chain = load_qa_chain(OpenAIChat(temperature=0, model_name="gpt-4"), chain_type="stuff")
print("Trying to use qa_chain OpenAIChat with gpt-4")
else:
print("Trying to use qa_chain OpenAI with text-davinci-003")
qa_chain = OpenAI(temperature=0, max_tokens=MAX_TOKENS, model_name="text-davinci-003")
print(str(datetime.datetime.now()) + ": After load_chain, OPENAI_API_KEY length: " + str(
len(os.environ["OPENAI_API_KEY"])))
os.environ["OPENAI_API_KEY"] = ""
return chain, express_chain, llm, embeddings, qa_chain, memory, use_gpt4
return None, None, None, None, None, None, None
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:
error_msg = AUTH_ERR_MSG + str(datetime.datetime.now()) + ". " + str(ae)
print("error_msg", error_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:
output = AUTH_ERR_MSG + str(datetime.datetime.now()) + ". " + str(ae)
print("output", output)
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
def reset_memory(history, memory):
# memory.clear()
history = []
return history, history, memory
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, qa_chain, docsearch, use_embeddings, force_translate
):
"""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 = "Please paste your OpenAI key from openai.com to use this app. " + str(datetime.datetime.now())
hidden_text = output
if chain:
# Set OpenAI key
import openai
openai.api_key = api_key
if not monologue:
if use_embeddings:
if inp and inp.strip() != "":
if docsearch:
docs = docsearch.similarity_search(inp)
output = str(qa_chain.run(input_documents=docs, question=inp))
else:
output, hidden_text = "Please supply some text in the the Embeddings tab.", None
else:
output, hidden_text = "What's on your mind?", None
else:
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, force_translate)
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, "Female")
voice_id, language_code, engine = POLLY_VOICE_DATA.get_voice(polly_language, "Male")
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 = '
no audio
'
# 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''
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''
return html_video
def do_html_video_speak(words_to_speak, azure_language):
azure_voice = AZURE_VOICE_DATA.get_voice(azure_language, "Male")
if not azure_voice:
azure_voice = "en-US-ChristopherNeural"
headers = {"Authorization": f"Bearer {os.environ['EXHUMAN_API_KEY']}"}
body = {
'bot_name': 'Masahiro',
'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 = '
no video
'
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''
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, memory = 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, ""
def update_foo(widget, state):
if widget:
state = widget
return state
# Pertains to question answering functionality
def update_embeddings(embeddings_text, embeddings, qa_chain):
if embeddings_text:
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_text(embeddings_text)
docsearch = FAISS.from_texts(texts, embeddings)
print("Embeddings updated")
return docsearch
# Pertains to question answering functionality
def update_use_embeddings(widget, state):
if widget:
state = widget
return state
with gr.Blocks(css=".gradio-container {background-color: lightgray}") 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(False)
monologue_state = gr.State(False) # Takes the input and repeats it back to the user, optionally transforming it.
force_translate_state = gr.State(FORCE_TRANSLATE_DEFAULT) #
memory_state = gr.State()
# 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)
# Pertains to question answering functionality
embeddings_state = gr.State()
qa_chain_state = gr.State()
docsearch_state = gr.State()
use_embeddings_state = gr.State(False)
use_gpt4_state = gr.State(USE_GPT4_DEFAULT)
with gr.Tab("Chat"):
with gr.Row():
with gr.Column():
gr.HTML(
"""
GPT + WolframAlpha + Whisper
Hit Enter after pasting your OpenAI API key.
If you have GPT-4 access, optionally select it in Settings tab.
""")
openai_api_key_textbox = gr.Textbox(placeholder="Paste your OpenAI API key (sk-...) and hit Enter",
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_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="What's the answer to life, the universe, and everything?",
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])
# TEMPORARY FOR TESTING
# with gr.Row():
# audio_comp_tb = gr.Textbox(label="Just say it!", lines=1)
# audio_comp_tb.submit(transcribe_dummy, inputs=[audio_comp_tb, whisper_lang_state], outputs=[message])
gr.Examples(
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?"],
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])
# force_translate_cb = gr.Checkbox(label="Force translation to selected Output Language",
# value=FORCE_TRANSLATE_DEFAULT)
# force_translate_cb.change(update_foo, inputs=[force_translate_cb, force_translate_state],
# outputs=[force_translate_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])
# use_gpt4_cb = gr.Checkbox(label="Use GPT-4 (experimental) if your OpenAI API has access to it",
# value=USE_GPT4_DEFAULT)
# use_gpt4_cb.change(set_openai_api_key,
# inputs=[openai_api_key_textbox, use_gpt4_cb],
# outputs=[chain_state, express_chain_state, llm_state, embeddings_state,
# qa_chain_state, memory_state, use_gpt4_state])
# reset_btn = gr.Button(value="Reset chat", variant="secondary").style(full_width=False)
# reset_btn.click(reset_memory, inputs=[history_state, memory_state],
# outputs=[chatbot, history_state, memory_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("Output Language"):
# 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", "Neanderthal",
# "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", "Story", "Summary", "Outline", "Bullets", "Poetry", "Haiku", "Limerick",
# "Rap",
# "Joke", "Knock-knock", "FAQ"],
# 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])
# with gr.Tab("Embeddings"):
# embeddings_text_box = gr.Textbox(label="Enter text for embeddings and hit Create:",
# lines=20)
# with gr.Row():
# use_embeddings_cb = gr.Checkbox(label="Use embeddings", value=False)
# use_embeddings_cb.change(update_use_embeddings, inputs=[use_embeddings_cb, use_embeddings_state],
# outputs=[use_embeddings_state])
# embeddings_text_submit = gr.Button(value="Create", variant="secondary").style(full_width=False)
# embeddings_text_submit.click(update_embeddings,
# inputs=[embeddings_text_box, embeddings_state, qa_chain_state],
# outputs=[docsearch_state])
# gr.HTML("""
#
This application, developed by James L. Weaver,
# 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 Ex-Human.
# For faster inference without waiting in queue, you may duplicate the space.
#