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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) | |
st.write(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']=="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']=="fear"): | |
with open('fearplaylist.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) | |
st.header("stream your emotions") | |
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) | |