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 = 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("tiny") print("WHISPER_MODEL", WHISPER_MODEL) ARC = """

When multiple lines of business are selected or already bound - we need to follow the ""most restrictive"" guideline.

Example: If Auto Liability (AL) is bound already and we are processing an endorsement, we need to make sure the driver has 2+ years driving experience (even if only 1+ is needed for the other coverages like APD, etc)

Coverage	Driver Age	Experience
Auto Liability (AL)	25 to 65 years old	2+ years
APD, MTC, TGL, NTL	23 to 70 years old	2+ years experience for New Ventures (0 years in business) 2+ years for accounts in business 1+ years

Cover Whale insures trucks that are at Class 3 weight or heavier.

Our system optimizes the finance terms based on the account characteristics. We have several plans approved by our premium finance partners.

Public auto operations are not within our guidelines and are considered ineligible.

Operations transporting fine arts are not within our guidelines and are considered ineligible.

No, we don't currently offer surplus line stacking.

It depends if we financed or if they financed it. On the accounts Cover Whale has financed we return it to finance company. On accounts financed with third parties, we return credits to third parties.

We are not currently offering hired or non-owned.

PA is not a state where you can decline PIP coverage.

Box trucks or straight trucks are acceptable risks for automobile liability (AL) insurance provided they are not used for "last mile" deliveries to the final destination.

Log in to the Cover Whale Platform and search for the submission in question.
Click on the purple submission number link.
Click Create Endorsement.
Confirm the effective date and click Create Endorsement again.
Make the change needed: Add or remove a vehicle or trailer (only for auto liability: in order to bind we will need a de-lease agreement, termination agreement, bill of sale or signed agreement) add or remove a driver, update named insured, add loss payee, update mailing or terminal address, request different limits of insurance, or change in radius or commodities.
After the change has been made, click Request Quote.
Read and agree to the Terms of Use, Privacy Policy, and Brokerage Agreement and click Request Quote.
You will receive the endorsement quote in an automated e-mail. If the quote and pricing look desirable, click Request to Bind.
The underwriters will review the endorsement. If they have additional questions or require additional documents, they will contact you using the Info Needed button in the Platform. You will receive the correspondence in an automated e-mail. Once the underwriters receive what is needed, they will approve the binder on the endorsement. And just like that, an endorsement can be processed quickly.

We may consider hauling fry ash as long as it is not wet cement nor in a mixing truck, please submit to underwriting for review.

Shuttle service risks are not within our guidelines and are considered ineligible operations.

Click the ""Chat with Us"" option behind your avatar in either the Guru web app or browser extension.

Want a step by step Getting Started guide?
Check out our course here.

In search of a quick tour of the product?
Our product tour should do the trick. Check it out here.

I want to see more informational videos. Got any more?
Sure do! Check out the ""Learn More About Guru"" section on your Dashboard.

Our Help Center will be an evergreen resource for you.
Feel free to bookmark it, pin it, memorize it...whatever you need to be a Guru power user ðŸ˜„

" "

Reasonable growth during the policy term is acceptable.

1 unit per quarter is considered reasonable growth.

" "

NEW HIRE CALLS Playlist

Link calls here.

reviewer feedback

Managers should look for and provide feedback on how the agent:

Showed empathy to the customer
Asked questions to triage the issue
Took steps to remedy the issue
Confirmed that the issue had been resolved

Scorecards will close ~30 days after an initiative.
Reps will be expected to submit two calls in 30 days.
First call > scorecard > implement feedback > second call > scorecard to note improvement

Taxi risks are not currently within our guidelines and are considered ineligible operations.

We do not currently offer admitted cargo.

If they do a premium bearing endorsement after binding the service team will send it to capital so it can be added to their current installment plan.

It should be emailed to the agent but it can also be found in the files section of the submission page

" "

Commissions are paid within 45 to 60 days of the policy being bound, as long as there are no outstanding balances due to Cover Whale.

When we have a California driver with a letter prior to "Y", this means that the driver has had a CDL for more than 3 years, so we do not ask for proof of experience.

Paratransit risks are not within our guidelines at this time and are considered ineligible operations.

Pickup trucks are currently not within our guidelines and are considered ineligible. (under 14,000lbs GVW)

AL $500 safety fee (this is for Orion Dash Cam) + $500 Underwriting fee

Renewals are discounted.

Additional Note: The policy fees for New Jersey will have a different structure due to the state requirement that policy fees may not exceed $250 for AL & APD coverage.

Fees have been updated based on a retailer or wholesaler:

" "

When offering Auto Liability, it is a corporate guideline that Cover Whale finances the policy with one of our Premium Finance partners.

We offer industry best terms including very low down payments and 11 installments. This is required to reduce cancellations and reinstatements, and manage endorsement premiums.

Hired or non-owned trailers are acceptable if coverage is purchased. This doesn't meet UIIA requirements.

Cover Whale would like to provide clear instructions to help our agents and brokers stay organized while completing their monthly reporting on fleet accounts.

Please follow the below steps to make sure your monthly reports are compliant with our carrier guidelines.

Fill out the attached excel spreadsheet. On the Vehicles Schedule tab, please only include the vehicles and trailers being added or deleted. Indicate on column G whether the vehicle or trailer is being added or deleted.
Fill out the next tab, Driver Schedule, the same as above. Please only include the drivers that are being added and deleted.
Please send the updated fleet report to hello@coverwhale.com by the 10th of the following month. Example: Reporting for May is due June 10th.
Once you have received our automated quote with the monthly report and have reviewed it, please go into your submission and click Request to Bind.
Your monthly report will then go to Underwriting to approve the updated vehicle & driver schedule.
Please note, if a fleet report is declined, you may e-mail underwriting@coverwhale.com to review if there is a discrepancy on the reason for the decline.

" "

Crum & Forster - A Paper

Knight Specialty - B ++ Paper

Everspan - A

" "

At this time, we don't offer roadside assistance.

Cover Whale APD policies reflect a towing limit for a covered loss. This coverage can't be used for roadside assistance, a mechanical breakdown, or wear and tear loss. A covered loss must be a sudden, direct, accidental loss leading to damage to the policyholder's unit.

Car or truck rental and leasing firms are not currently within our guidelines and are considered ineligible.

Oversized load risks are not currently within our guidelines and are considered ineligible.

Non-Admitted Surplus Lines

Boat Hauler risks are not within our appetite and considered ineligible operations.

Welcome to {{COMPANY}}!

The broad goals of onboarding are getting to know:

{{COMPANY}} (the product)
{{COMPANY}} (the culture)
{{COMPANY}} (the people).

Customer Experience | Key Elements of our role at {{COMPANY}}

Services, Support & Success:

How we interact with our fellow teammates
How we engage with prospects/customers
Implementation/Onboarding approach
How we scale going forward
Metrics we are tracking

Learn more about all of the above and you'll reach a clearer understanding of what we do as Customer Success and how we do it. Remember that the learning never stops!

Helpful resources include:

Team Org. Chart
Link to Log Into Product
Link to Help Center

" "

Agency bill: Cover Whale bills agency

Currently, Cover Whale is agency bill we do not offer Direct Bill

" ")""" # UNCOMMENT TO USE WHISPER def transcribe(aud_inp, whisper_lang): if aud_inp is None: return "" aud = whisper.load_audio(aud_inp) aud = whisper.pad_or_trim(aud) mel = whisper.log_mel_spectrogram(aud).to(WHISPER_MODEL.device) _, probs = WHISPER_MODEL.detect_language(mel) options = whisper.DecodingOptions() if whisper_lang != WHISPER_DETECT_LANG: whisper_lang_code = POLLY_VOICE_DATA.get_whisper_lang_code(whisper_lang) options = whisper.DecodingOptions(language=whisper_lang_code) result = whisper.decode(WHISPER_MODEL, mel, options) print("result.text", result.text) result_text = "" if result and result.text: result_text = result.text return result_text # Temporarily address Wolfram Alpha SSL certificate issue ssl._create_default_https_context = ssl._create_unverified_context # TEMPORARY FOR TESTING def transcribe_dummy(aud_inp_tb, whisper_lang): if aud_inp_tb is None: return "" # aud = whisper.load_audio(aud_inp) # aud = whisper.pad_or_trim(aud) # mel = whisper.log_mel_spectrogram(aud).to(WHISPER_MODEL.device) # _, probs = WHISPER_MODEL.detect_language(mel) # options = whisper.DecodingOptions() # options = whisper.DecodingOptions(language="ja") # result = whisper.decode(WHISPER_MODEL, mel, options) result_text = "Whisper will detect language" if whisper_lang != WHISPER_DETECT_LANG: whisper_lang_code = POLLY_VOICE_DATA.get_whisper_lang_code(whisper_lang) result_text = f"Whisper will use lang code: {whisper_lang_code}" print("result_text", result_text) return aud_inp_tb # Pertains to Express-inator functionality def transform_text(desc, express_chain, num_words, formality, anticipation_level, joy_level, trust_level, fear_level, surprise_level, sadness_level, disgust_level, anger_level, lang_level, translate_to, literary_style, force_translate): num_words_prompt = "" if num_words and int(num_words) != 0: num_words_prompt = "using up to " + str(num_words) + " words, " # Change some arguments to lower case formality = formality.lower() anticipation_level = anticipation_level.lower() joy_level = joy_level.lower() trust_level = trust_level.lower() fear_level = fear_level.lower() surprise_level = surprise_level.lower() sadness_level = sadness_level.lower() disgust_level = disgust_level.lower() anger_level = anger_level.lower() formality_str = "" if formality != "n/a": formality_str = "in a " + formality + " manner, " # put all emotions into a list emotions = [] if anticipation_level != "n/a": emotions.append(anticipation_level) if joy_level != "n/a": emotions.append(joy_level) if trust_level != "n/a": emotions.append(trust_level) if fear_level != "n/a": emotions.append(fear_level) if surprise_level != "n/a": emotions.append(surprise_level) if sadness_level != "n/a": emotions.append(sadness_level) if disgust_level != "n/a": emotions.append(disgust_level) if anger_level != "n/a": emotions.append(anger_level) emotions_str = "" if len(emotions) > 0: if len(emotions) == 1: emotions_str = "with emotion of " + emotions[0] + ", " else: emotions_str = "with emotions of " + ", ".join(emotions[:-1]) + " and " + emotions[-1] + ", " lang_level_str = "" if lang_level != LANG_LEVEL_DEFAULT: lang_level_str = "at a level that a person in " + lang_level + " can easily comprehend, " if translate_to == TRANSLATE_TO_DEFAULT else "" translate_to_str = "" if translate_to != TRANSLATE_TO_DEFAULT and (force_translate or lang_level != LANG_LEVEL_DEFAULT): translate_to_str = "translated to " + translate_to + ( "" if lang_level == LANG_LEVEL_DEFAULT else " at a level that a person in " + lang_level + " can easily comprehend") + ", " literary_style_str = "" if literary_style != LITERARY_STYLE_DEFAULT: if literary_style == "Prose": literary_style_str = "as prose, " if literary_style == "Story": literary_style_str = "as a story, " elif literary_style == "Summary": literary_style_str = "as a summary, " elif literary_style == "Outline": literary_style_str = "as an outline numbers and lower case letters, " elif literary_style == "Bullets": literary_style_str = "as bullet points using bullets, " elif literary_style == "Poetry": literary_style_str = "as a poem, " elif literary_style == "Haiku": literary_style_str = "as a haiku, " elif literary_style == "Limerick": literary_style_str = "as a limerick, " elif literary_style == "Rap": literary_style_str = "as a rap, " elif literary_style == "Joke": literary_style_str = "as a very funny joke with a setup and punchline, " elif literary_style == "Knock-knock": literary_style_str = "as a very funny knock-knock joke, " elif literary_style == "FAQ": literary_style_str = "as a FAQ with several questions and answers, " formatted_prompt = PROMPT_TEMPLATE.format( original_words=desc, num_words=num_words_prompt, formality=formality_str, emotions=emotions_str, lang_level=lang_level_str, translate_to=translate_to_str, literary_style=literary_style_str ) trans_instr = num_words_prompt + formality_str + emotions_str + lang_level_str + translate_to_str + literary_style_str if express_chain and len(trans_instr.strip()) > 0: generated_text = express_chain.run( {'original_words': desc, 'num_words': num_words_prompt, 'formality': formality_str, 'emotions': emotions_str, 'lang_level': lang_level_str, 'translate_to': translate_to_str, 'literary_style': literary_style_str}).strip() else: print("Not transforming text") generated_text = desc # replace all newlines with
in generated_text generated_text = generated_text.replace("\n", "\n\n") prompt_plus_generated = "GPT prompt: " + formatted_prompt + "\n\n" + generated_text print("\n==== date/time: " + str(datetime.datetime.now() - datetime.timedelta(hours=5)) + " ====") print("prompt_plus_generated: " + prompt_plus_generated) return generated_text def load_chain(tools_list, llm): chain = None express_chain = None memory = None if llm: print("\ntools_list", tools_list) tool_names = tools_list tools = load_tools(tool_names, llm=llm, news_api_key=news_api_key, tmdb_bearer_token=tmdb_bearer_token) memory = ConversationBufferMemory(memory_key="chat_history") chain = initialize_agent(tools, llm, agent="conversational-react-description", verbose=True, memory=memory) express_chain = LLMChain(llm=llm, prompt=PROMPT_TEMPLATE, verbose=True) return chain, express_chain, memory def set_openai_api_key(api_key, use_gpt4): """Set the api key and return chain. If no api_key, then None is returned. """ if api_key and api_key.startswith("sk-") and len(api_key) > 50: #os.environ["OPENAI_API_KEY"] = api_key os.environ["OPENAI_API_KEY"] = os.environ['gptapikey'] 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 openai.api_key = os.environ['gptapikey'] 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 press the 'Activate Embeddings' button in order to load Coverwhale Agent Resource Center knowledge into ChatGPT.", None else: output, hidden_text = "What's on your mind?", None else: output, hidden_text = run_chain(chain, inp, capture_hidden_text=trace_chain) else: output, hidden_text = inp, None output = transform_text(output, express_chain, num_words, formality, anticipation_level, joy_level, trust_level, fear_level, surprise_level, sadness_level, disgust_level, anger_level, lang_level, translate_to, literary_style, force_translate) text_to_display = output if trace_chain: text_to_display = hidden_text + "\n\n" + output history.append((inp, text_to_display)) html_video, temp_file, html_audio, temp_aud_file = None, None, None, None if speak_text: if talking_head: if len(output) <= MAX_TALKING_HEAD_TEXT_LENGTH: html_video, temp_file = do_html_video_speak(output, translate_to) else: temp_file = LOOPING_TALKING_HEAD html_video = create_html_video(temp_file, TALKING_HEAD_WIDTH) html_audio, temp_aud_file = do_html_audio_speak(output, translate_to) else: html_audio, temp_aud_file = do_html_audio_speak(output, translate_to) else: if talking_head: temp_file = LOOPING_TALKING_HEAD html_video = create_html_video(temp_file, TALKING_HEAD_WIDTH) else: # html_audio, temp_aud_file = do_html_audio_speak(output, translate_to) # html_video = create_html_video(temp_file, "128") pass except Exception as e: raise e finally: self.lock.release() return history, history, html_video, temp_file, html_audio, temp_aud_file, "" # return history, history, html_audio, temp_aud_file, "" chat = ChatWrapper() def do_html_audio_speak(words_to_speak, polly_language): polly_client = boto3.Session( aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"], aws_secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"], region_name=os.environ["AWS_DEFAULT_REGION"] ).client('polly') # voice_id, language_code, engine = POLLY_VOICE_DATA.get_voice(polly_language, "Female") voice_id, language_code, engine = POLLY_VOICE_DATA.get_voice(polly_language, "Male") if not voice_id: # voice_id = "Joanna" voice_id = "Matthew" language_code = "en-US" engine = NEURAL_ENGINE response = polly_client.synthesize_speech( Text=words_to_speak, OutputFormat='mp3', VoiceId=voice_id, LanguageCode=language_code, Engine=engine ) html_audio = '

no audio

' # Save the audio stream returned by Amazon Polly on Lambda's temp directory if "AudioStream" in response: with closing(response["AudioStream"]) as stream: # output = os.path.join("/tmp/", "speech.mp3") try: with open('audios/tempfile.mp3', 'wb') as f: f.write(stream.read()) temp_aud_file = gr.File("audios/tempfile.mp3") temp_aud_file_url = "/file=" + temp_aud_file.value['name'] html_audio = f'

' except IOError as error: # Could not write to file, exit gracefully print(error) return None, None else: # The response didn't contain audio data, exit gracefully print("Could not stream audio") return None, None return html_audio, "audios/tempfile.mp3" def create_html_video(file_name, width): temp_file_url = "/file=" + tmp_file.value['name'] html_video = f'

' return html_video def do_html_video_speak(words_to_speak, azure_language): azure_voice = AZURE_VOICE_DATA.get_voice(azure_language, "Male") if not azure_voice: azure_voice = "en-US-ChristopherNeural" headers = {"Authorization": f"Bearer {os.environ['EXHUMAN_API_KEY']}"} body = { 'bot_name': 'Masahiro', 'bot_response': words_to_speak, 'azure_voice': azure_voice, 'azure_style': 'friendly', 'animation_pipeline': 'high_speed', } api_endpoint = "https://api.exh.ai/animations/v1/generate_lipsync" res = requests.post(api_endpoint, json=body, headers=headers) print("res.status_code: ", res.status_code) html_video = '

no video

' if isinstance(res.content, bytes): response_stream = io.BytesIO(res.content) print("len(res.content)): ", len(res.content)) with open('videos/tempfile.mp4', 'wb') as f: f.write(response_stream.read()) temp_file = gr.File("videos/tempfile.mp4") temp_file_url = "/file=" + temp_file.value['name'] html_video = f'

' else: print('video url unknown') return html_video, "videos/tempfile.mp4" def update_selected_tools(widget, state, llm): if widget: state = widget chain, express_chain, memory = load_chain(state, llm) return state, llm, chain, express_chain def update_talking_head(widget, state): if widget: state = widget video_html_talking_head = create_html_video(LOOPING_TALKING_HEAD, TALKING_HEAD_WIDTH) return state, video_html_talking_head else: # return state, create_html_video(LOOPING_TALKING_HEAD, "32") return None, "

" def update_foo(widget, state): if widget: state = widget return state # Pertains to question answering functionality def update_embeddings(embeddings_text, embeddings, qa_chain): if embeddings_text: text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) texts = text_splitter.split_text(embeddings_text) docsearch = FAISS.from_texts(texts, embeddings) print("Embeddings updated") return docsearch # Pertains to question answering functionality def update_use_embeddings(widget, state): if widget: state = widget return state with gr.Blocks(css=".gradio-container {background-color: lightgray}") as block: llm_state = gr.State() history_state = gr.State() chain_state = gr.State() express_chain_state = gr.State() tools_list_state = gr.State(TOOLS_DEFAULT_LIST) trace_chain_state = gr.State(False) speak_text_state = gr.State(False) talking_head_state = gr.State(True) monologue_state = gr.State(False) # Takes the input and repeats it back to the user, optionally transforming it. force_translate_state = gr.State(FORCE_TRANSLATE_DEFAULT) # memory_state = gr.State() # Pertains to Express-inator functionality num_words_state = gr.State(NUM_WORDS_DEFAULT) formality_state = gr.State(FORMALITY_DEFAULT) anticipation_level_state = gr.State(EMOTION_DEFAULT) joy_level_state = gr.State(EMOTION_DEFAULT) trust_level_state = gr.State(EMOTION_DEFAULT) fear_level_state = gr.State(EMOTION_DEFAULT) surprise_level_state = gr.State(EMOTION_DEFAULT) sadness_level_state = gr.State(EMOTION_DEFAULT) disgust_level_state = gr.State(EMOTION_DEFAULT) anger_level_state = gr.State(EMOTION_DEFAULT) lang_level_state = gr.State(LANG_LEVEL_DEFAULT) translate_to_state = gr.State(TRANSLATE_TO_DEFAULT) literary_style_state = gr.State(LITERARY_STYLE_DEFAULT) # Pertains to WHISPER functionality whisper_lang_state = gr.State(WHISPER_DETECT_LANG) # Pertains to question answering functionality embeddings_state = gr.State() qa_chain_state = gr.State() docsearch_state = gr.State() use_embeddings_state = gr.State(True) use_gpt4_state = gr.State(USE_GPT4_DEFAULT) with gr.Tab("Chat"): with gr.Row(): with gr.Column(): gr.HTML( """Cover Whale Agent Resource Center ChatGPT Demo""") #openai_api_key_textbox = gr.Textbox(placeholder="Paste your OpenAI API key (sk-...) and hit Enter", # show_label=False, lines=1, type='password') with gr.Row(): with gr.Column(scale=1, min_width=TALKING_HEAD_WIDTH, visible=False): speak_text_cb = gr.Checkbox(label="Enable speech", value=False) speak_text_cb.change(update_foo, inputs=[speak_text_cb, speak_text_state], outputs=[speak_text_state]) my_file = gr.File(label="Upload a file", type="file", visible=False) tmp_file = gr.File(LOOPING_TALKING_HEAD, visible=False) # tmp_file_url = "/file=" + tmp_file.value['name'] htm_video = create_html_video(LOOPING_TALKING_HEAD, TALKING_HEAD_WIDTH) video_html = gr.HTML(htm_video) # my_aud_file = gr.File(label="Audio file", type="file", visible=True) tmp_aud_file = gr.File("audios/tempfile.mp3", visible=False) tmp_aud_file_url = "/file=" + tmp_aud_file.value['name'] htm_audio = f'

' audio_html = gr.HTML(htm_audio) with gr.Column(scale=7): chatbot = gr.Chatbot() embeddings_text_box = gr.Textbox(value=ARC, visible=False, label="Enter text for embeddings and hit Create:", lines=20) with gr.Row(): openai_api_key_textbox = gr.Textbox(value=os.environ['gptapikey'], placeholder="Paste your OpenAI API key (sk-...) and hit Enter", show_label=False, lines=1, type='password') #openai_api_key_textbox.submit() #api_text_submit = gr.Button(value="Activate Embeddings", variant="secondary").style(full_width=False) #api_text_submit.click(update_embeddings, # inputs=[embeddings_text_box, embeddings_state, qa_chain_state], # outputs=[docsearch_state]) with gr.Row(): use_embeddings_cb = gr.Checkbox(label="Use embeddings", value=True) use_embeddings_cb.change(update_use_embeddings, inputs=[use_embeddings_cb, use_embeddings_state], outputs=[use_embeddings_state]) embeddings_text_submit = gr.Button(value="Activate Embeddings", variant="secondary").style(full_width=False) embeddings_text_submit.click(update_embeddings, inputs=[embeddings_text_box, embeddings_state, qa_chain_state], outputs=[docsearch_state]) with gr.Row(): message = gr.Textbox(label="Ask a question about Cover Whale", placeholder="How do I process an endorsement on the Cover Whale Platform?", lines=1) submit = gr.Button(value="Send", variant="secondary").style(full_width=False) # UNCOMMENT TO USE WHISPER #with gr.Row(): # audio_comp = gr.Microphone(source="microphone", type="filepath", label="Just say it!", # interactive=True, streaming=False) # audio_comp.change(transcribe, inputs=[audio_comp, whisper_lang_state], outputs=[message]) # TEMPORARY FOR TESTING # with gr.Row(): # audio_comp_tb = gr.Textbox(label="Just say it!", lines=1) # audio_comp_tb.submit(transcribe_dummy, inputs=[audio_comp_tb, whisper_lang_state], outputs=[message]) #gr.Examples( # examples=["How many people live in Canada?", # "What is 2 to the 30th power?", # "If x+y=10 and x-y=4, what are x and y?", # "How much did it rain in SF today?", # "Get me information about the movie 'Avatar'", # "What are the top tech headlines in the US?", # "On the desk, you see two blue booklets, two purple booklets, and two yellow pairs of sunglasses - " # "if I remove all the pairs of sunglasses from the desk, how many purple items remain on it?"], # inputs=message #) with gr.Tab("Settings"): tools_cb_group = gr.CheckboxGroup(label="Tools:", choices=TOOLS_LIST, value=TOOLS_DEFAULT_LIST) tools_cb_group.change(update_selected_tools, inputs=[tools_cb_group, tools_list_state, llm_state], outputs=[tools_list_state, llm_state, chain_state, express_chain_state]) trace_chain_cb = gr.Checkbox(label="Show reasoning chain in chat bubble", value=False) trace_chain_cb.change(update_foo, inputs=[trace_chain_cb, trace_chain_state], outputs=[trace_chain_state]) force_translate_cb = gr.Checkbox(label="Force translation to selected Output Language", value=FORCE_TRANSLATE_DEFAULT) force_translate_cb.change(update_foo, inputs=[force_translate_cb, force_translate_state], outputs=[force_translate_state]) # speak_text_cb = gr.Checkbox(label="Speak text from agent", value=False) # speak_text_cb.change(update_foo, inputs=[speak_text_cb, speak_text_state], # outputs=[speak_text_state]) talking_head_cb = gr.Checkbox(label="Show talking head", value=True) talking_head_cb.change(update_talking_head, inputs=[talking_head_cb, talking_head_state], outputs=[talking_head_state, video_html]) monologue_cb = gr.Checkbox(label="Babel fish mode (translate/restate what you enter, no conversational agent)", value=False) monologue_cb.change(update_foo, inputs=[monologue_cb, monologue_state], outputs=[monologue_state]) use_gpt4_cb = gr.Checkbox(label="Use GPT-4 (experimental) if your OpenAI API has access to it", value=USE_GPT4_DEFAULT) use_gpt4_cb.change(set_openai_api_key, inputs=[openai_api_key_textbox, use_gpt4_cb], outputs=[chain_state, express_chain_state, llm_state, embeddings_state, qa_chain_state, memory_state, use_gpt4_state]) reset_btn = gr.Button(value="Reset chat", variant="secondary").style(full_width=False) reset_btn.click(reset_memory, inputs=[history_state, memory_state], outputs=[chatbot, history_state, memory_state]) with gr.Tab("Whisper STT"): whisper_lang_radio = gr.Radio(label="Whisper speech-to-text language:", choices=[ WHISPER_DETECT_LANG, "Arabic", "Arabic (Gulf)", "Catalan", "Chinese (Cantonese)", "Chinese (Mandarin)", "Danish", "Dutch", "English (Australian)", "English (British)", "English (Indian)", "English (New Zealand)", "English (South African)", "English (US)", "English (Welsh)", "Finnish", "French", "French (Canadian)", "German", "German (Austrian)", "Georgian", "Hindi", "Icelandic", "Indonesian", "Italian", "Japanese", "Korean", "Norwegian", "Polish", "Portuguese (Brazilian)", "Portuguese (European)", "Romanian", "Russian", "Spanish (European)", "Spanish (Mexican)", "Spanish (US)", "Swedish", "Turkish", "Ukrainian", "Welsh"], value=WHISPER_DETECT_LANG) whisper_lang_radio.change(update_foo, inputs=[whisper_lang_radio, whisper_lang_state], outputs=[whisper_lang_state]) with gr.Tab("Output Language"): lang_level_radio = gr.Radio(label="Language level:", choices=[ LANG_LEVEL_DEFAULT, "1st grade", "2nd grade", "3rd grade", "4th grade", "5th grade", "6th grade", "7th grade", "8th grade", "9th grade", "10th grade", "11th grade", "12th grade", "University"], value=LANG_LEVEL_DEFAULT) lang_level_radio.change(update_foo, inputs=[lang_level_radio, lang_level_state], outputs=[lang_level_state]) translate_to_radio = gr.Radio(label="Language:", choices=[ TRANSLATE_TO_DEFAULT, "Arabic", "Arabic (Gulf)", "Catalan", "Chinese (Cantonese)", "Chinese (Mandarin)", "Danish", "Dutch", "English (Australian)", "English (British)", "English (Indian)", "English (New Zealand)", "English (South African)", "English (US)", "English (Welsh)", "Finnish", "French", "French (Canadian)", "German", "German (Austrian)", "Georgian", "Hindi", "Icelandic", "Indonesian", "Italian", "Japanese", "Korean", "Norwegian", "Polish", "Portuguese (Brazilian)", "Portuguese (European)", "Romanian", "Russian", "Spanish (European)", "Spanish (Mexican)", "Spanish (US)", "Swedish", "Turkish", "Ukrainian", "Welsh", "emojis", "Gen Z slang", "how the stereotypical Karen would say it", "Klingon", "Neanderthal", "Pirate", "Strange Planet expospeak technical talk", "Yoda"], value=TRANSLATE_TO_DEFAULT) translate_to_radio.change(update_foo, inputs=[translate_to_radio, translate_to_state], outputs=[translate_to_state]) with gr.Tab("Formality"): formality_radio = gr.Radio(label="Formality:", choices=[FORMALITY_DEFAULT, "Casual", "Polite", "Honorific"], value=FORMALITY_DEFAULT) formality_radio.change(update_foo, inputs=[formality_radio, formality_state], outputs=[formality_state]) with gr.Tab("Lit Style"): literary_style_radio = gr.Radio(label="Literary style:", choices=[ LITERARY_STYLE_DEFAULT, "Prose", "Story", "Summary", "Outline", "Bullets", "Poetry", "Haiku", "Limerick", "Rap", "Joke", "Knock-knock", "FAQ"], value=LITERARY_STYLE_DEFAULT) literary_style_radio.change(update_foo, inputs=[literary_style_radio, literary_style_state], outputs=[literary_style_state]) with gr.Tab("Emotions"): anticipation_level_radio = gr.Radio(label="Anticipation level:", choices=[EMOTION_DEFAULT, "Interest", "Anticipation", "Vigilance"], value=EMOTION_DEFAULT) anticipation_level_radio.change(update_foo, inputs=[anticipation_level_radio, anticipation_level_state], outputs=[anticipation_level_state]) joy_level_radio = gr.Radio(label="Joy level:", choices=[EMOTION_DEFAULT, "Serenity", "Joy", "Ecstasy"], value=EMOTION_DEFAULT) joy_level_radio.change(update_foo, inputs=[joy_level_radio, joy_level_state], outputs=[joy_level_state]) trust_level_radio = gr.Radio(label="Trust level:", choices=[EMOTION_DEFAULT, "Acceptance", "Trust", "Admiration"], value=EMOTION_DEFAULT) trust_level_radio.change(update_foo, inputs=[trust_level_radio, trust_level_state], outputs=[trust_level_state]) fear_level_radio = gr.Radio(label="Fear level:", choices=[EMOTION_DEFAULT, "Apprehension", "Fear", "Terror"], value=EMOTION_DEFAULT) fear_level_radio.change(update_foo, inputs=[fear_level_radio, fear_level_state], outputs=[fear_level_state]) surprise_level_radio = gr.Radio(label="Surprise level:", choices=[EMOTION_DEFAULT, "Distraction", "Surprise", "Amazement"], value=EMOTION_DEFAULT) surprise_level_radio.change(update_foo, inputs=[surprise_level_radio, surprise_level_state], outputs=[surprise_level_state]) sadness_level_radio = gr.Radio(label="Sadness level:", choices=[EMOTION_DEFAULT, "Pensiveness", "Sadness", "Grief"], value=EMOTION_DEFAULT) sadness_level_radio.change(update_foo, inputs=[sadness_level_radio, sadness_level_state], outputs=[sadness_level_state]) disgust_level_radio = gr.Radio(label="Disgust level:", choices=[EMOTION_DEFAULT, "Boredom", "Disgust", "Loathing"], value=EMOTION_DEFAULT) disgust_level_radio.change(update_foo, inputs=[disgust_level_radio, disgust_level_state], outputs=[disgust_level_state]) anger_level_radio = gr.Radio(label="Anger level:", choices=[EMOTION_DEFAULT, "Annoyance", "Anger", "Rage"], value=EMOTION_DEFAULT) anger_level_radio.change(update_foo, inputs=[anger_level_radio, anger_level_state], outputs=[anger_level_state]) with gr.Tab("Max Words"): num_words_slider = gr.Slider(label="Max number of words to generate (0 for don't care)", value=NUM_WORDS_DEFAULT, minimum=0, maximum=MAX_WORDS, step=10) num_words_slider.change(update_foo, inputs=[num_words_slider, num_words_state], outputs=[num_words_state]) with gr.Tab("Embeddings"): embeddings_text_box = gr.Textbox(value=ARC, label="Enter text for embeddings and hit Create:", lines=20) with gr.Row(): use_embeddings_cb = gr.Checkbox(label="Use embeddings", value=True) use_embeddings_cb.change(update_use_embeddings, inputs=[use_embeddings_cb, use_embeddings_state], outputs=[use_embeddings_state]) embeddings_text_submit = gr.Button(value="Create", variant="secondary").style(full_width=False) embeddings_text_submit.click(update_embeddings, inputs=[embeddings_text_box, embeddings_state, qa_chain_state], outputs=[docsearch_state]) # ARC2 = str(ARC) # # embeddings2_text_submit = gr.Button(value="Activate", variant="secondary").style(full_width=False) # embeddings2_text_submit.click(update_embeddings, # inputs=[ARC2, embeddings_state, qa_chain_state], # outputs=[docsearch_state]) gr.HTML("""

This is a proof of concept chatbot for Cover Whale.

''') message.submit(chat, inputs=[openai_api_key_textbox, message, history_state, chain_state, trace_chain_state, speak_text_state, talking_head_state, monologue_state, express_chain_state, num_words_state, formality_state, anticipation_level_state, joy_level_state, trust_level_state, fear_level_state, surprise_level_state, sadness_level_state, disgust_level_state, anger_level_state, lang_level_state, translate_to_state, literary_style_state, qa_chain_state, docsearch_state, use_embeddings_state, force_translate_state], outputs=[chatbot, history_state, video_html, my_file, audio_html, tmp_aud_file, message]) submit.click(chat, inputs=[openai_api_key_textbox, message, history_state, chain_state, trace_chain_state, speak_text_state, talking_head_state, monologue_state, express_chain_state, num_words_state, formality_state, anticipation_level_state, joy_level_state, trust_level_state, fear_level_state, surprise_level_state, sadness_level_state, disgust_level_state, anger_level_state, lang_level_state, translate_to_state, literary_style_state, qa_chain_state, docsearch_state, use_embeddings_state, force_translate_state], outputs=[chatbot, history_state, video_html, my_file, audio_html, tmp_aud_file, message]) openai_api_key_textbox.change(set_openai_api_key, inputs=[openai_api_key_textbox, use_gpt4_state], outputs=[chain_state, express_chain_state, llm_state, embeddings_state, qa_chain_state, memory_state, use_gpt4_state]) openai_api_key_textbox.submit(set_openai_api_key, inputs=[openai_api_key_textbox, use_gpt4_state], outputs=[chain_state, express_chain_state, llm_state, embeddings_state, qa_chain_state, memory_state, use_gpt4_state]) # Pertains to question answering functionalityy block.launch(debug=True)