Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+
+st.set_page_config(page_title="Text to Sentiment Analysis Audio", page_icon="🦜")
+st.header("Text to Sentiment Analysis Audio")
+
+# Define the sentiment analysis pipeline
+senti_ana_pipeline = pipeline("text-classification", model="imljls/gpt_review_senti_1")
+
+# Define the text-to-speech pipeline (replace 'your-tts-model' with the actual model name)
+text_audio_pipeline = pipeline("text-to-audio", model="Matthijs/mms-tts-eng")
+
+def analyze_sentiment(text):
+    result = senti_ana_pipeline(text)[0]
+    sentiment = result['label']
+    probability = result['score']
+    return sentiment, probability
+
+def format_sentiment_text(sentiment, probability):
+    return f"The sentiment of the text is {sentiment} with a probability of {probability:.2f}."
+
+def text_to_audio(text):
+    audio = text_audio_pipeline(text)
+    return audio
+
+input_text = st.text_input("Enter text for sentiment analysis:")
+
+if input_text:
+    # Stage 1: Analyze sentiment
+    st.text('Analyzing sentiment...')
+    sentiment, probability = analyze_sentiment(input_text)
+    sentiment_text = format_sentiment_text(sentiment, probability)
+    st.write(sentiment_text)
+
+    # Stage 2: Convert text to audio
+    st.text('Generating audio...')
+    sentiment_text_for_audio = f"The sentiment of the input text is {sentiment}, and the probability is {probability:.2f}."
+    audio_data = text_to_audio(sentiment_text_for_audio)
+
+    # Play button
+    if st.button("Play Audio"):
+        st.audio(audio_data['audio'], format="audio/wav", start_time=0, sample_rate=audio_data['sampling_rate'])