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import pandas as pd
from langdetect import detect, DetectorFactory
from langdetect.lang_detect_exception import LangDetectException
import streamlit as st
from transformers import pipeline
# Load the text summarization model pipeline
summarizer = pipeline("summarization", model="Falconsai/text_summarization")
# Load the text classification model pipeline
classifier = pipeline("text-classification", model='Rainess/Finturned-Music-Sentiment')
def summary_song(text):
input_length = len(text)
if input_length < 200:
return text # Return original text if length is less than 200
else:
max_length = 200
min_length = 50
summary = summarizer(text, max_length=max_length, min_length=min_length, truncation=True)
summarized_text = summary[0]['summary_text']
return summarized_text
# Streamlit application title
st.title("Text Summarization and Sentiment Classification for Music Lyrics")
st.write("Classify the sentiment of summarized lyrics as Positive or Negative")
# Text input for user to enter the lyrics
text = st.text_area("Enter the lyrics to summarize and classify", "")
# Perform text summarization and classification when the user clicks the "Summarize and Classify" button
if st.button("Summarize and Classify"):
if text.strip():
try:
# Perform text summarization on the input lyrics
summarized_text = summary_song(text)
# Perform text classification on the summarized text
result = classifier(summarized_text)[0]
# Extract the classification label and score
max_label = result['label']
max_score = result['score']
# Map label to sentiment
sentiment = "Positive" if max_label == "POSITIVE" else "Negative"
# Display the summarized text and classification result
st.write("Original Lyrics:", text)
st.write("Summarized Text:", summarized_text)
st.write("Sentiment:", sentiment)
st.write("Score:", max_score)
except Exception as e:
st.write("Error processing the text:", str(e))
else:
st.write("Please enter some lyrics to summarize and classify.")
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