app.py

import streamlit.components.v1 as components
from streamlit_player import st_player
from transformers import pipeline
import streamlit as st
import random 


st.header("stream your emotions")
st.caption("tester")

def tester(text):
  classifier = pipeline("sentiment-analysis", model='bhadresh-savani/distilbert-base-uncased-emotion')
  results = classifier(text)
  #st.subheader(results[0]['label'])

#tester(emo)
  generator = st.button("Generate Song!")
  if (generator == True):
    st.subheader(results[0]['label'])
  
    if (results[0]['label']=="joy"): #songs for joy emotion
      with open('joyplaylist.txt') as f:
        contents = f.read()
      components.html(contents,width=560,height=325)
    
    elif (results[0]['label']=="fear"):
      with open('fearplaylist.txt') as f:
        contents = f.read()
      components.html(contents,width=560,height=325)
    
    elif (results[0]['label']=="anger"): #songs for anger emotion
      with open('angryplaylist.txt') as f:
        contents = f.read()
      components.html(contents,width=560,height=325)    

    elif (results[0]['label']=="sadness"): #songs for sadness emotion
      with open('sadplaylist.txt') as f:
        contents = f.read()
      components.html(contents,width=560,height=325)

    elif (results[0]['label']=="surprise"):
      st.write("gulat ka noh")

    elif (results[0]['label']=="love"):
      with open('loveplaylist.txt') as f:
        contents = f.read()
      components.html(contents,width=560,height=325)
 
emo = st.text_input("Enter a text/phrase/sentence. A corresponding song will be recommended based on its emotion.")


st.sidebar.subheader("Model Description")
st.sidebar.write("This application uses the DistilBERT model, a distilled version of BERT. The BERT framework uses" )



tester(emo)