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import streamlit as st
from streamlit_player import st_player
from transformers import pipeline
from IPython.display import YouTubeVideo
def tester(text):
#classifier = pipeline("sentiment-analysis", model='arpanghoshal/EmoRoBERTa')
#classifier = pipeline("sentiment-analysis", model='cardiffnlp/twitter-roberta-base-emotion')
#classifier = pipeline("sentiment-analysis", 'j-hartmann/emotion-english-distilroberta-base')
classifier = pipeline("sentiment-analysis", model='bhadresh-savani/distilbert-base-uncased-emotion')
results = classifier(text)
st.write(results[0]['label'])
if (results[0]['label']=="anger"):
st_player("https://www.youtube.com/watch?v=kh0BWQ4Uo6w")
elif (results[0]['label']=="disgust"):
st_player("https://www.youtube.com/watch?v=zWq2TT3ieGE")
elif (results[0]['label']=="fear"):
st_player("https://www.youtube.com/watch?v=iyEUvUcMHgE")
elif (results[0]['label']=="joy"):
st_player("https://www.youtube.com/watch?v=1k8craCGpgs")
elif (results[0]['label']=="sadness"):
#video = YouTubeVideo("1k8craCGpgs")
#display(video)
st_player("https://www.youtube.com/watch?v=BZsXcc_tC-o")
elif (results[0]['label']=="surprise"):
st_player("https://youtu.be/CmSKVW1v0xM")
elif (results[0]['label']=="love"):
st_player("https://www.youtube.com/watch?v=XVhEm62Uqog")
return results[0]['label']
#return results
emo = st.text_input('This application detects the emotion in your text input and suggests a song that matches it. Please enter text below to try:')
tester(emo)
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