Spaces:
Runtime error
Runtime error
File size: 38,063 Bytes
283538e 1e99095 283538e 3aa7119 338f37a 283538e dcc369d 283538e 6cf1b6c 283538e 6cf1b6c 283538e 4c33aab 6ee747d 4c33aab 6ee747d 283538e 125926e 6cf1b6c 6ee747d 283538e 6cf1b6c 283538e 67d6308 283538e 6ee747d 283538e 224d655 283538e 6cf1b6c 283538e 6cf1b6c 283538e 4c33aab 125926e 283538e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 |
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
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"]
os.environ["OPENAI_API_KEY"] = "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"
TOOLS_LIST = ['serpapi', 'wolfram-alpha', 'pal-math', 'pal-colored-objects'] #'google-search','news-api','tmdb-api','open-meteo-api'
TOOLS_DEFAULT_LIST = ['serpapi', 'pal-math']
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 = "Vui lòng nhập OpenAI key from openai.com to use this application. "
MAX_TOKENS = 3200
LOOPING_TALKING_HEAD = "videos/eae4686734545a20f095e6d5a7afa98a.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()
# 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)
# 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):
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, "
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():
os.environ["OPENAI_API_KEY"] = "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"
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"])))
llm = OpenAI(temperature=0, max_tokens=MAX_TOKENS)
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()
qa_chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
print(str(datetime.datetime.now()) + ": After load_chain, OPENAI_API_KEY length: " + str(
len(os.environ["OPENAI_API_KEY"])))
return chain, express_chain, llm, embeddings, qa_chain, memory
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
):
"""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)
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(True)
monologue_state = gr.State(False) # Takes the input and repeats it back to the user, optionally transforming it.
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)
with gr.Tab("Chat"):
with gr.Row():
with gr.Column():
gr.HTML(
"""<b><center>GPT + WolframAlpha + Whisper</center></b>
<p><center>New feature: <b>Embeddings</b></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="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])
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])
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("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", "Neanderthal",
"Pirate", "Strange Planet expospeak technical talk", "Yoda", "Vietnamese (Vietnam)"],
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])
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],
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,
qa_chain_state, docsearch_state, use_embeddings_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, outputs=[chain_state, express_chain_state, llm_state, embeddings_state, qa_chain_state, memory_state])
# openai_api_key_textbox = "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"
block.launch(debug=True)
|