getting there

app.py

@@ -2,586 +2,31 @@ 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 = "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",
-)
-
-FORCE_TRANSLATE_DEFAULT = True
-USE_GPT4_DEFAULT = False
-
-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
-
+from config.config import TALKING_HEAD_WIDTH, TOOLS_DEFAULT_LIST, NUM_WORDS_DEFAULT, FORMALITY_DEFAULT, EMOTION_DEFAULT, \
+    LANG_LEVEL_DEFAULT, TRANSLATE_TO_DEFAULT, LITERARY_STYLE_DEFAULT, LOOPING_TALKING_HEAD, TOOLS_LIST, MAX_WORDS
+from utilities.audio import FORCE_TRANSLATE_DEFAULT, USE_GPT4_DEFAULT, transcribe, WHISPER_DETECT_LANG
+from utilities.audio import POLLY_VOICE_DATA, AZURE_VOICE_DATA
+from utilities.html_stuff import create_html_video, update_talking_head
+from utilities.polly_utils import NEURAL_ENGINE
+from utilities.reset_memory import ChatWrapper, reset_memory
+from utilities.set_openai_api_key import set_openai_api_key
+from utilities.update_things import update_foo, update_selected_tools, update_use_embeddings, \
+    update_embeddings
 
 # 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()
@@ -639,10 +84,10 @@ with gr.Blocks(css=".gradio-container {background-color: lightgray}") as block:
                                      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)
+                html_video = create_html_video(LOOPING_TALKING_HEAD, TALKING_HEAD_WIDTH)
+                video_html = gr.HTML(html_video)
 
                 # my_aud_file = gr.File(label="Audio file", type="file", visible=True)
                 tmp_aud_file = gr.File("audios/tempfile.mp3", visible=False)
@@ -683,20 +128,20 @@ with gr.Blocks(css=".gradio-container {background-color: lightgray}") as block:
         )
 
     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])
+        tools_checkbox_group = gr.CheckboxGroup(label="Tools:", choices=TOOLS_LIST,
+                                                value=TOOLS_DEFAULT_LIST)
+        tools_checkbox_group.change(update_selected_tools,
+                                    inputs=[tools_checkbox_group, tools_list_state, llm_state],
+                                    outputs=[tools_list_state, llm_state, chain_state, express_chain_state])
+
+        trace_chain_checkbox = gr.Checkbox(label="Show reasoning chain in chat bubble", value=False)
+        trace_chain_checkbox.change(update_foo, inputs=[trace_chain_checkbox, trace_chain_state],
+                                    outputs=[trace_chain_state])
+
+        force_translate_checkbox = gr.Checkbox(label="Force translation to selected Output Language",
+                                               value=FORCE_TRANSLATE_DEFAULT)
+        force_translate_checkbox.change(update_foo, inputs=[force_translate_checkbox, 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],

config/config.py

@@ -0,0 +1,36 @@
+import os
+
+from langchain import PromptTemplate
+
+TALKING_HEAD_WIDTH = 192
+
+# Console to variable
+# Pertains to question answering functionality
+
+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"
+
+MAX_TALKING_HEAD_TEXT_LENGTH = 155
+
+# Pertains to Express-inator functionality
+NUM_WORDS_DEFAULT = 0
+MAX_WORDS = 4000
+FORMALITY_DEFAULT = "N/A"
+TEMPERATURE_DEFAULT = 0
+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",
+)
+

utilities/audio.py

@@ -0,0 +1,60 @@
+import warnings
+
+import whisper
+
+from utilities.azure_utils import AzureVoiceData
+from utilities.polly_utils import PollyVoiceData
+
+FORCE_TRANSLATE_DEFAULT = True
+USE_GPT4_DEFAULT = True
+
+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("large")
+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
+
+
+# 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
+

utilities/html_stuff.py

@@ -0,0 +1,111 @@
+import io
+import os
+from contextlib import closing
+
+import boto3
+import gradio as gr
+
+import requests
+
+from config.config import TALKING_HEAD_WIDTH, LOOPING_TALKING_HEAD
+from utilities.audio import AZURE_VOICE_DATA, POLLY_VOICE_DATA
+from utilities.polly_utils import NEURAL_ENGINE
+
+
+def create_html_video(file_name, width):
+    tmp_file = gr.File(LOOPING_TALKING_HEAD, visible=False)
+    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 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 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 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"

utilities/load_chain.py

@@ -0,0 +1,29 @@
+import os
+
+from langchain import LLMChain
+from langchain.agents import load_tools, initialize_agent
+from langchain.memory import ConversationBufferMemory
+
+from config.config import PROMPT_TEMPLATE
+
+
+def load_chain(tools_list, llm):
+    chain = None
+    express_chain = None
+    memory = None
+    if llm:
+        print("\ntools_list", tools_list)
+        tool_names = tools_list
+
+        news_api_key = os.environ["NEWS_API_KEY"]
+        tmdb_bearer_token = os.environ["TMDB_BEARER_TOKEN"]
+        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

utilities/reset_memory.py

@@ -0,0 +1,99 @@
+import datetime
+from threading import Lock
+from typing import Tuple, Optional
+
+from langchain import ConversationChain, LLMChain
+
+from config.config import MAX_TALKING_HEAD_TEXT_LENGTH, LOOPING_TALKING_HEAD, TALKING_HEAD_WIDTH
+from utilities.html_stuff import do_html_video_speak, create_html_video, do_html_audio_speak
+from utilities.transform_text import transform_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, ""
+

utilities/run_chain.py

@@ -0,0 +1,73 @@
+import datetime
+import re
+import sys
+from io import StringIO
+
+from openai import InvalidRequestError
+from openai.error import RateLimitError, AuthenticationError
+
+from config.config import BUG_FOUND_MSG, AUTH_ERR_MSG
+
+
+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

utilities/set_openai_api_key.py

@@ -0,0 +1,48 @@
+import os
+from datetime import datetime
+
+from langchain import OpenAI
+from langchain.chains import load_chain
+from langchain.chains.question_answering import load_qa_chain
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.llms import OpenAIChat
+
+from config.config import MAX_TOKENS, TOOLS_DEFAULT_LIST
+
+
+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.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.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.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

utilities/transform_text.py

@@ -0,0 +1,120 @@
+# Pertains to Express-inator functionality
+import datetime
+
+from config.config import PROMPT_TEMPLATE, TRANSLATE_TO_DEFAULT, LANG_LEVEL_DEFAULT, LITERARY_STYLE_DEFAULT
+
+
+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

utilities/update_things.py

@@ -0,0 +1,39 @@
+from langchain import FAISS
+from langchain.text_splitter import CharacterTextSplitter
+
+
+from utilities.load_chain import load_chain
+
+
+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_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
+