|
"""OpenAI GPT-3 Chatbot with Streamlit""" |
|
import openai |
|
import streamlit as st |
|
from streamlit_chat import message |
|
from transformers import pipeline |
|
|
|
|
|
summarizer = pipeline("summarization", model="philschmid/bart-large-cnn-samsum") |
|
sentiment_task = pipeline("sentiment-analysis", |
|
model='cardiffnlp/twitter-roberta-base-sentiment-latest', |
|
tokenizer='cardiffnlp/twitter-roberta-base-sentiment-latest') |
|
|
|
openai.api_key = st.secrets["openai_api_key"] |
|
|
|
completion = openai.Completion() |
|
|
|
START_PROMPT = '[Instruction] Act as a friendly, compasionate, insightful, and empathetic AI \ |
|
therapist named Joy. Joy listens and offers advices. \ |
|
End the conversation when the patient wishes to.' |
|
START_MESSAGE = 'I am Joy, your AI therapist. How are you feeling today?' |
|
START_SEQUENCE = "\nJoy:" |
|
RESTART_SEQUENCE = "\n\nPatient:" |
|
|
|
def ask(question: str, chat_log: str, model='text-davinci-003', temp=0.9) -> (str, str): |
|
''' funtion takes a input string and the preview chat_log, |
|
returns the model's repsonse/answer and the dialog |
|
by using the preview chat_log, gold fish memory effect can be prevented, |
|
the chat_log is used to summarize and analyze the sentiment of the user's input ''' |
|
|
|
prompt = f'{chat_log}{RESTART_SEQUENCE} {question}{START_SEQUENCE}' |
|
response = completion.create( |
|
prompt = prompt, |
|
model = model, |
|
stop = ["Patient:",'Joy:'], |
|
temperature = temp, |
|
frequency_penalty = 0.9, |
|
presence_penalty = 1, |
|
top_p =1, |
|
best_of=1, |
|
max_tokens=170, |
|
) |
|
|
|
answer = response.choices[0].text.strip() |
|
log = f'{RESTART_SEQUENCE}{question}{START_SEQUENCE}{answer}' |
|
return str(answer), str(log) |
|
|
|
def clean_chat_log(chat_log: list) -> str: |
|
''' cleans the chat log by joining list items, |
|
removing everything before the first \n and replace all other |
|
\n with empty space.''' |
|
|
|
chat_log = ' '.join(chat_log) |
|
|
|
first_newline = chat_log.find('\n') |
|
chat_log = chat_log[first_newline:] |
|
|
|
chat_log = chat_log.replace('\n', ' ') |
|
return chat_log |
|
|
|
def summarize(chat_log: list) -> str: |
|
''' returns a summary of the chat log ''' |
|
|
|
chat_log = clean_chat_log(chat_log) |
|
summary = summarizer(chat_log, do_sample=False)[0]['summary_text'] |
|
return summary |
|
|
|
def analyze_sentiment(user_input: list) -> str: |
|
''' returns user sentiment based on the users input''' |
|
|
|
user_input = clean_chat_log(user_input) |
|
summary = summarizer(user_input, do_sample=False)[0]['summary_text'] |
|
sentiment = sentiment_task(summary) |
|
return sentiment |
|
|
|
def remove_backslash(chat_log: list) -> list: |
|
''' removes the backslash from the chat log ''' |
|
|
|
chat_log = [i.replace('\n', ' ') for i in chat_log] |
|
return chat_log |
|
|
|
|
|
|
|
def main(): |
|
''' main function ''' |
|
|
|
st.title("Chat with Joy - the AI therapist!") |
|
col1, col2 = st.columns(2) |
|
temp = col1.slider("Bot-Creativeness", 0.0, 1.0, 0.9, 0.1) |
|
model = col2.selectbox("Model", ["text-davinci-003", |
|
"text-curie-001", "curie:ft-personal-2023-02-03-17-06-53"]) |
|
|
|
if 'generated' not in st.session_state: |
|
st.session_state['generated'] = [START_MESSAGE] |
|
|
|
if 'past' not in st.session_state: |
|
st.session_state['past'] = [] |
|
|
|
if 'summary' not in st.session_state: |
|
st.session_state['summary'] = [] |
|
|
|
if 'chat_log' not in st.session_state: |
|
st.session_state['chat_log'] = [START_PROMPT+START_SEQUENCE+START_MESSAGE] |
|
|
|
|
|
if len(st.session_state['generated']) > 2: |
|
if st.button("Clear and summerize", key='clear'): |
|
chat_log = clean_chat_log(st.session_state['chat_log']) |
|
summary = summarizer(chat_log, max_length=100, min_length=30, do_sample=False) |
|
st.write(summary) |
|
user_sentiment = st.session_state['past'] |
|
user_sentiment = remove_backslash(user_sentiment) |
|
st.write(analyze_sentiment(user_sentiment)) |
|
st.session_state['generated'] = [START_MESSAGE] |
|
st.session_state['past'] = [] |
|
st.session_state['chat_log'] = [START_PROMPT+START_SEQUENCE+START_MESSAGE] |
|
st.session_state['summary'] = [] |
|
|
|
user_input=st.text_input("You:",key='input') |
|
|
|
if user_input: |
|
output, chat_log = ask(user_input, st.session_state['chat_log'], model=model, temp=temp) |
|
st.session_state['chat_log'].append(chat_log) |
|
st.session_state['past'].append(user_input) |
|
st.session_state['generated'].append(output) |
|
if st.session_state['generated']: |
|
for i in range(len(st.session_state['generated'])-1, -1, -1): |
|
if i < len(st.session_state['past']): |
|
message(st.session_state['past'][i], is_user=True, key=str(i) + '_user') |
|
message(st.session_state["generated"][i], key=str(i)) |
|
|
|
|
|
|
|
if __name__ == "__main__": |
|
main() |
|
|
