app.py

import os
import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.schema import (AIMessage,HumanMessage,SystemMessage)
from transformers import pipeline
from streamlit_extras.let_it_rain import rain

def get_response(question):
    st.session_state.sessionMessages.append(HumanMessage(content=question))

    assistant_answer  = chat(st.session_state.sessionMessages )

    st.session_state.sessionMessages.append(AIMessage(content=assistant_answer.content))

    return assistant_answer.content

def get_sentiment(user_input, nlp):
    result = nlp(user_input)
    sentiment = ""
    if (result[0]['label'] == '1 star'):
        sentiment = 'very negative'
    elif (result[0]['label'] == '2 stars'):
        sentiment = 'negative'
    elif (result[0]['label'] == '3 stars'):
        sentiment = 'neutral'
    elif (result[0]['label'] == '4 stars'):
        sentiment = 'positive'
    else:
        sentiment = 'very positive'

    prob = result[0]['score']

    return sentiment, prob



# open ai
chat = ChatOpenAI(model="gpt-3.5-turbo", temperature=1)

# hugging-face model
nlp = pipeline(task='sentiment-analysis', model='nlptown/bert-base-multilingual-uncased-sentiment')





st.set_page_config(page_title="Home Assistant", page_icon=":robot:")
st.header("Knock knock, It's meeee the JOKER!")

if "sessionMessages" not in st.session_state:
     st.session_state.sessionMessages = [SystemMessage(content="You have an evil personality like Joker from Batman")]

if "messages" not in st.session_state:
	st.session_state.messages = []

if user_input := st.chat_input("Say something"):
    assistant_input = get_response(user_input)
    sentiment, prob = get_sentiment(user_input, nlp)

    sentiment_analysis = f" (sentiment:{sentiment},score:{prob})"
    
    # add user input to history
    st.session_state.messages.append({"role": "user", "content": user_input})

    # add assistant input to history
    st.session_state.messages.append({"role": "assistant", "content": assistant_input})



    # sentiment analysis
    
    if sentiment == "very negative":
        rain( 
        emoji="⛔", 
        font_size=20,  # the size of emoji 
        falling_speed=3,  # speed of raining 
        animation_length="infinite",  # for how much time the animation will happen 
        )         
    elif sentiment == "negative":
        rain( 
        emoji="⛔", 
        font_size=20,  # the size of emoji 
        falling_speed=3,  # speed of raining 
        animation_length="infinite",  # for how much time the animation will happen 
        ) 
    elif sentiment == "neutral":
        rain( 
        emoji="😐", 
        font_size=20,  # the size of emoji 
        falling_speed=3,  # speed of raining 
        animation_length="infinite",  # for how much time the animation will happen 
        ) 
    elif sentiment == "positive":
        rain( 
        emoji="🟢", 
        font_size=20,  # the size of emoji 
        falling_speed=3,  # speed of raining 
        animation_length="infinite",  # for how much time the animation will happen 
        ) 
    elif sentiment == "very positive":
        rain( 
        emoji="🟢", 
        font_size=20,  # the size of emoji 
        falling_speed=3,  # speed of raining 
        animation_length="infinite",  # for how much time the animation will happen 
        ) 






    

# display chat history
for message in st.session_state.messages:
     with st.chat_message(message["role"]):
          st.markdown(message["content"])