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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 <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 | |
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 = '<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="Masahiro.png"></video>' | |
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 = '<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="Masahiro.png"></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, 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, "<pre></pre>" | |
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( | |
"""<b><center>GPT + WolframAlpha + Whisper</center></b> | |
<p><center>Hit Enter after pasting your OpenAI API key.</center></p> | |
<i><center>If you have GPT-4 access, optionally select it in Settings tab.</center></i>""") | |
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><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="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(""" | |
# <p>This application, developed by <a href='https://www.linkedin.com/in/javafxpert/'>James L. Weaver</a>, | |
# 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>""") | |
# 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/JavaFXpert/Chat-GPT-LangChain?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, | |
qa_chain_state, docsearch_state, use_embeddings_state, | |
force_translate_state], | |
outputs=[chatbot, history_state, video_html, my_file, 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, | |
qa_chain_state, docsearch_state, use_embeddings_state, | |
force_translate_state], | |
outputs=[chatbot, history_state, video_html, my_file, audio_html, tmp_aud_file, message]) | |
openai_api_key_textbox.change(set_openai_api_key, | |
inputs=[openai_api_key_textbox, use_gpt4_state], | |
outputs=[chain_state, express_chain_state, llm_state, embeddings_state, | |
qa_chain_state, memory_state, use_gpt4_state]) | |
openai_api_key_textbox.submit(set_openai_api_key, | |
inputs=[openai_api_key_textbox, use_gpt4_state], | |
outputs=[chain_state, express_chain_state, llm_state, embeddings_state, | |
qa_chain_state, memory_state, use_gpt4_state]) | |
block.launch(debug=True) |