app.py

import streamlit as st
from transformers import pipeline
from datetime import datetime

# Set up Streamlit title and description
st.title("Sentiment Analysis , Audio Feedback, and Response Generation")
st.write("Enter a review, guess its sentiment (positive/negative), and get audio feedback on your guess and improve your Enlish.")

# 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")

# Load the text generation model
response_generator = pipeline("text-generation", model="openai-community/gpt2")

# Text input for user to enter the review
review = st.text_area("Enter the review to analyze", "")

# Dropdown for user to guess the sentiment
user_guess = st.selectbox("Guess the sentiment of your review:", ("Positive", "Negative"))

# Perform operations when the user clicks the "Analyze and Respond" button
if st.button("Analyze and Generate"):
    # Perform sentiment analysis on the input review
    sentiment_result = sentiment_analysis(review)[0]
    label = sentiment_result['label'].upper()
    score = sentiment_result['score']
    
    # Check user's guess against the analysis
    guess_is_correct = (user_guess.upper() == label)
    guess_feedback = "Right" if guess_is_correct else "Wrong"
    if guess_feedback == "Right":
        feedback_text = f"Congratulations! Your guess was {guess_feedback}! The actual sentiment of the review is {label}."
    else:
        feedback_text = f"Sorry! Your guess was {guess_feedback}! The actual sentiment of the review is {label}."

    
    # Generate audio feedback for the guess
    feedback_audio = text_to_audio(feedback_text)
    st.audio(feedback_audio['audio'], format='audio/wav', sample_rate=feedback_audio['sampling_rate'])
    
    # Display the sentiment analysis result and feedback
    st.write("Predicted Sentiment:", label)
    st.write("Confidence Score:", f"{score * 100:.2f}%")
    st.write(feedback_text)

    # Generate audio of the review
    st.write("Audio of the review for practice your listening")
    review_audio = text_to_audio(f"Audio of the review for practice your listening: {review}")
    st.audio(review_audio['audio'], format='audio/wav', sample_rate=review_audio['sampling_rate'])

    # Generate a professional response based on the review and its sentiment
    if label == "POSITIVE":
        prompt = "Thank you for sharing your experience and choosing our hotel. We are glad that you had a wonderful stay with us."
    else:
        prompt = "Thank you for letting us know about the issues that you experienced during your recent stay at our hotel. Sorry for any inconvenience caused."

    professional_response = response_generator(prompt, max_length=50, num_return_sequences=1)[0]['generated_text']
    
    # Display the generated professional response
    st.write("Generated Professional Response:", professional_response)


st.title('Monthly Best Response Competition')
# Simple form for input using Streamlit
with st.form("user_input_form"):
    user_input = st.text_input("Enter your response:")
    submit_button = st.form_submit_button("Submit")

    if submit_button:
        # Current time when the input is received
        current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        # Format the entry to be saved
        entry = f"{current_time}: {user_input}\n"
        # Save the input to a local file
        with open("monthly_competition_entries.txt", "a") as file:
            file.write(entry)
        st.success("Your response has been saved and entered into the competition!")

# Display outside the form context
st.write("Feel free to submit more responses. Each unique response counts as a separate entry.")