app.py

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

Automatically generated by Colaboratory.

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

# !pip install gradio

# !pip install -U sentence-transformers

# !pip install datasets


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


from greg_funcs import get_llm_response

"""# import models"""

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

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

"""# import datasets"""
dataset = load_dataset("gfhayworth/hack_policy", split='train')

mypassages = list(dataset.to_pandas()['psg'])

dataset_embed = load_dataset("gfhayworth/hack_policy_embed", split='train')

dataset_embed_pd = dataset_embed.to_pandas()
mycorpus_embeddings = torch_tensor(dataset_embed_pd.values)


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

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

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

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


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


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


@tool
def mygreetings(greeting: str) -> str:
    """Let us do our greetings
    """

    return "how are you?"

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

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

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


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

# demo.launch()

OPENAI_API_KEY = "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"

# AWS keys
aws_access_key_id = "AKIA3JRWKI2EE5ZFN5NZ"
aws_secret_access_key = "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"
aws_region_name = "us-east-1"

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

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

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

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

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

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

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

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

# Pertains to WHISPER functionality
WHISPER_DETECT_LANG = "Detect language"


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


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

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

# example questions
EXAMPLES = ["What is the name of the plan described by this summary of benefits?",
                      "How much is the monthly premium?",
                      "What would my copay be for the emergency room?",
                      "Tell me about go365 please",
                      "what is the special preferred pharmacy called"]
AUTHORS = """
        <p>This application, developed by <b>Greg Hayworth, Srikanth Tangelloju, Lincoln Snyder, Michal Piekarczyk, and Xingde Jiang</b>, 
        demonstrates a conversational agent implemented with OpenAI GPT-3.5 and LangChain. 
        When necessary, it leverages tools for complex math, searching the internet, and accessing news and weather.
        Uses talking heads from <a href='https://exh.ai/'>Ex-Human</a>.
        For faster inference without waiting in queue, you may duplicate the space.
        </p>"""
# UNCOMMENT TO USE WHISPER


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


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

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

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

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

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

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

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

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

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

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

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

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

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

    return generated_text


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

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

    return chain, express_chain


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

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


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

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

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

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

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

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

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

        if error_msg:
            hidden_text += error_msg

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

    return output, hidden_text


class ChatWrapper:

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

    def __call__(
            self, api_key: str, inp: str, history: Optional[Tuple[str, str]], chain: Optional[ConversationChain],
            trace_chain: bool, speak_text: bool, talking_head: bool, monologue: bool, express_chain: Optional[LLMChain],
            num_words, formality, anticipation_level, joy_level, trust_level,
            fear_level, surprise_level, sadness_level, disgust_level, anger_level,
            lang_level, translate_to, literary_style
    ):
        """Execute the chat functionality."""
        self.lock.acquire()



        # import ipdb; ipdb.set_trace()
        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)
            talking_head = False
            print("monologue: ", monologue)
            history = history or []
            # If chain is None, that is because no API key was provided.
            output = AUTH_ERR_MSG
            hidden_text = output


            output = get_llm_response(inp)

            """
            if chain:
                # Set OpenAI key
                import openai
                openai.api_key = OPENAI_API_KEY
                # openai.api_key = api_key
                if not monologue:
                    output, hidden_text = run_chain(
                        chain, inp, capture_hidden_text=trace_chain)
                else:
                    output, hidden_text = inp, None
            """
            print("original output", output)
            print("using these knobs:",
                  (
                      formality, anticipation_level, joy_level,
                      trust_level,
                      fear_level, surprise_level, sadness_level, disgust_level, anger_level,
                      lang_level, translate_to, literary_style
                      )

                  )

            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)

            print("transformed output", output)

            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):
    print(f"words_to_speak: {words_to_speak}")
    print(f"polly_language: {polly_language}")
    print(f"VOICE_GENDER: {VOICE_GENDER}")
    polly_client = boto3.Session(
        aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"],
        aws_secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"],
        region_name=os.environ["AWS_DEFAULT_REGION"]
    ).client('polly')

    voice_id, language_code, engine = POLLY_VOICE_DATA.get_voice(
        polly_language, VOICE_GENDER)
    # voice_id, language_code, engine = polly_voice_data.get_voice('English (US)', 'Female')
    # print('English (US)', 'Female', voice_id, language_code, engine)
    # English (US) Female Joanna en-US neural

    if not voice_id:
        voice_id = "Joanna"
        language_code = "en-US"
        engine = NEURAL_ENGINE
    # print(f'voice_id: {voice_id}')
    # print(f'language_code: {language_code}')
    # print(f'engine: {engine}')

    response = polly_client.synthesize_speech(
        Text=words_to_speak,
        OutputFormat='mp3',
        VoiceId=voice_id,
        LanguageCode=language_code,
        Engine=engine
    )

    print('-'*10)
    print(f'response: {response}')
    print('-'*10)

    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_vid_file.value['name']
    html_video = f'<video width={width} height={width} autoplay muted loop><source src={temp_file_url} type="video/mp4" poster="humancare.jpg"></video>'
    return html_video


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

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

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

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


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


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

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


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


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

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

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

    with gr.Tab("Chat"):
        with gr.Row():
            with gr.Column():
                gr.HTML(
                    """<b><center>GPT + Whisper + Polly + Personalized + Multi-lingual + Empathetic + Agent: Human Care Humana.</center></b>
                    <p><center>New feature in <b>Translate to</b>: Choose <b>Language level</b> (e.g. for conversation practice or explain like I'm five)</center></p>""")

            openai_api_key_textbox = gr.Textbox(placeholder="Paste your OpenAI API key (sk-...)",
                                                show_label=False, lines=1, type='password')
        with gr.Row():
            with gr.Column(scale=1, min_width=TALKING_HEAD_WIDTH, visible=True):
                speak_text_cb = gr.Checkbox(label="Enable speech", value=True)
                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_vid_file = gr.File(LOOPING_TALKING_HEAD, visible=False)
                # tmp_file_url = "/file=" + tmp_vid_file.value['name']
                # htm_video = create_html_video(
                #     LOOPING_TALKING_HEAD, TALKING_HEAD_WIDTH)
                # video_html = gr.HTML(htm_video)
                video_html = gr.HTML("<pre></pre>")

                # 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>'
                # htm_audio = f'<audio autoplay><source src={tmp_aud_file_url} type="audio/mp3"></audio>'

                # htm_audio = f'<audio autoplay><source src="/file=audios/temfile.mp3" type="audio/mp3"></audio>'
                audio_html = gr.HTML(htm_audio)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    gr.HTML(AUTHORS)

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

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

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

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

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

block.launch(debug=True)