Update app.py

app.py

@@ -1,26 +1,28 @@
 import streamlit as st
 from transformers import pipeline
-import numpy as np
 
 # Set up Streamlit title and description
-st.title("Sentiment Analysis and Audio Feedback")
-st.write("Enter a review and get the sentiment analysis with audio feedback.")
+st.title("Sentiment Analysis, Audio Feedback, and Response Generation")
+st.write("Enter a review and get the sentiment analysis with audio feedback and a professional response.")
 
 # Load the sentiment analysis model
 sentiment_analysis = pipeline("text-classification", model="JACOBBBB/CustomModel_JL")
 
 # Load the text-to-speech model
-text_to_audio = pipeline("text-to-audio", model="vbrydik/mms-tts-eng-finetune-v3-train")
+text_to_audio = pipeline("text-to-speech", model="facebook/fastspeech2-en-ljspeech")
+
+# Load the text generation model
+response_generator = pipeline("text-generation", model="gpt-2")
 
 # Text input for user to enter the review
 review = st.text_area("Enter the review to analyze", "")
 
-# Perform sentiment analysis when the user clicks the "Analyze" button
-if st.button("Analyze"):
+# Perform operations when the user clicks the "Analyze and Respond" button
+if st.button("Analyze and Respond"):
     # Perform sentiment analysis on the input review
     sentiment_result = sentiment_analysis(review)[0]
     label = sentiment_result['label']
-    score = sentiment_result['score']  # Score as a fraction
+    score = sentiment_result['score']
 
     # Display the sentiment analysis result
     st.write("Review:", review)
@@ -32,7 +34,11 @@ if st.button("Analyze"):
 
     # Generate audio from response text
     audio = text_to_audio(response_text)
+    st.audio(audio['audio'], format='audio/wav', sample_rate=audio['sampling_rate'])
+
+    # Generate a professional response based on the review and its sentiment
+    prompt = f"The guest has left a {label} review stating: '{review}'. How should the hotel management professionally respond?"
+    professional_response = response_generator(prompt, max_length=150, num_return_sequences=1)[0]['generated_text']
 
-    # Display the audio file using Streamlit's audio widget
-    # Ensure to include the sample_rate when audio data is a numpy array
-    st.audio(audio['audio'], format='audio/wav', sample_rate=audio['sampling_rate'])
+    # Display the generated professional response
+    st.write("Generated Professional Response:", professional_response)