Create app.py

app.py

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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
+
+# Initialize the pipelines
+summarizer = pipeline("summarization", model="Falconsai/text_summarization")
+classifier = pipeline("text-classification", model='Rainess/Finturned-Music-Sentiment', return_all_scores=True)
+
+@app.route('/process_lyrics', methods=['POST'])
+def process_lyrics():
+    data = request.json
+    lyrics = data.get('lyrics')
+
+    if not lyrics:
+        return jsonify({'error': 'No lyrics provided'}), 400
+
+    # Summarize the lyrics
+    summary = summarizer(lyrics, max_length=50, min_length=25, do_sample=False)[0]['summary_text']
+
+    # Classify the summarized text
+    classification_result = classifier(summary)[0]
+
+    # Determine the sentiment
+    sentiment = "Negative" if classification_result['label'] == 'LABEL_0' else "Positive"
+
+    return jsonify({
+        'summary': summary,
+        'sentiment': sentiment
+    })
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000)