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


def tester(text):
  classifier = pipeline("sentiment-analysis", model='bhadresh-savani/distilbert-base-uncased-emotion')
  results = classifier(text)

  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)
    contents.close()   

  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)
    contents.close()     
  elif (results[0]['label']=="disgust"):
    st_player("https://www.youtube.com/watch?v=zWq2TT3ieGE")
    
  elif (results[0]['label']=="fear"):
    with open('fearplaylist.txt') as f:
      contents = f.read()
    components.html(contents,width=560,height=325)
    contents.close()     
  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)
    contents.close() 
  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)
    contents.close()   
  return results[0]['label']





st.header("stream your emotions")


#st.write("Enter a text/phrase/sentence. A corresponding song will be recommended based on its emotion")
emo = st.text_input("Enter a text/phrase/sentence. A corresponding song will be recommended based on its emotion.")
st.write("Examples: i love you so much")
st.write("I am exhausted.")
st.write("I feel energetic.")
st.write("bro you scared me there")

tester(emo)