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Update app.py
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app.py
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@@ -4,31 +4,53 @@ from langdetect.lang_detect_exception import LangDetectException
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import streamlit as st
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from transformers import pipeline
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# Initialize the pipelines
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summarizer = pipeline("summarization", model="Falconsai/text_summarization")
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classifier = pipeline("text-classification", model='Rainess/Finturned-Music-Sentiment', return_all_scores=True)
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@app.route('/process_lyrics', methods=['POST'])
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def process_lyrics():
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data = request.json
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lyrics = data.get('lyrics')
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if not lyrics:
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return jsonify({'error': 'No lyrics provided'}), 400
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# Classify the summarized text
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classification_result = classifier(summary)[0]
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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=5000)
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import streamlit as st
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from transformers import pipeline
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# Load the text summarization model pipeline
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summarizer = pipeline("summarization", model="Falconsai/text_summarization")
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# Load the text classification model pipeline
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classifier = pipeline("text-classification", model='Rainess/Finturned-Music-Sentiment', return_all_scores=True)
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def summary_song(text):
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input_length = len(text)
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if input_length < 200:
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return text # Return original text if length is less than 200
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else:
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max_length = 200
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min_length = 50
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summary = summarizer(text, max_length=max_length, min_length=min_length, truncation=True)
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summarized_text = summary[0]['summary_text']
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return summarized_text
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# Streamlit application title
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st.title("Text Summarization and Sentiment Classification for Music Lyrics")
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st.write("Classify the sentiment of summarized lyrics as Positive or Negative")
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# Text input for user to enter the lyrics
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text = st.text_area("Enter the lyrics to summarize and classify", "")
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# Perform text summarization and classification when the user clicks the "Summarize and Classify" button
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if st.button("Summarize and Classify"):
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if text.strip():
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# Perform text summarization on the input lyrics
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summarized_text = summary_song(text)
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# Perform text classification on the summarized text
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classification_results = classifier(summarized_text)
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# Get the highest scoring sentiment
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max_score = float('-inf')
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max_label = ''
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# Map label to sentiment
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sentiment = "Positive" if max_label == "1" else "Negative"
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# Display the summarized text and classification result
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st.write("Original Lyrics:", text)
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st.write("Summarized Text:", summarized_text)
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st.write("Sentiment:", sentiment)
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st.write("Score:", max_score)
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else:
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st.write("Please enter some lyrics to summarize and classify.")
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