app.py

##########################################
# Step 0: Import required libraries
##########################################
import streamlit as st  # For building the web application interface
from transformers import (
    pipeline,
    SpeechT5Processor,
    SpeechT5ForTextToSpeech,
    SpeechT5HifiGan,
    AutoModelForCausalLM,
    AutoTokenizer
)  # For sentiment analysis, text-to-speech, and text generation
from datasets import load_dataset  # For loading datasets (e.g., speaker embeddings)
import torch  # For tensor operations
import soundfile as sf  # For saving audio as .wav files
import sentencepiece  # Required by SpeechT5Processor for tokenization

##########################################
# Streamlit application title and input
##########################################
# Display a deep blue title in large, visually appealing font
st.markdown(
    "<h1 style='text-align: center; color: #00008B; font-size: 50px;'>Just Comment</h1>",
    unsafe_allow_html=True
)  # Set a deep blue title

# Display a gentle, warm subtitle below the title
st.markdown(
    "<h3 style='text-align: center; color: #5D6D7E; font-style: italic;'>I'm listening to you, my friend</h3>",
    unsafe_allow_html=True
)  # Show a friendly subtitle

# Provide a text area for user input with placeholder and tooltip
text = st.text_area(
    "Enter your comment",
    placeholder="Type something here...",
    height=150,
    help="Write a comment you would like us to respond to!"  # Tooltip for guidance
)  # Create a text input area

##########################################
# Step 1: Sentiment Analysis Function (Unused here)
##########################################
def analyze_dominant_emotion(user_review):
    """
    Analyze the dominant emotion in the user's comment using a fine-tuned text classification model.
    """
    emotion_classifier = pipeline(
        "text-classification",
        model="Thea231/jhartmann_emotion_finetuning",
        return_all_scores=True
    )  # Load the sentiment classification model
    emotion_results = emotion_classifier(user_review)[0]  # Get sentiment scores of the input
    dominant_emotion = max(emotion_results, key=lambda x: x['score'])  # Find the highest scoring emotion
    return dominant_emotion  # Return the dominant emotion

##########################################
# Step 2: Response Generation Functions
##########################################
def prompt_gen(user_review):
    """
    Generate a prompt based on the user's comment and detected emotion.
    This function is defined but not used, as the response is fixed.
    """
    dominant_emotion = analyze_dominant_emotion(user_review)  # Determine the dominant emotion
    emotion_strategies = {
        "anger": {
            "prompt": (
                "Customer complaint: '{review}'\n\n"
                "As a customer service representative, craft a professional response that:\n"
                "- Begins with sincere apology and acknowledgment\n"
                "- Clearly explains solution process with concrete steps\n"
                "- Offers appropriate compensation/redemption\n"
                "- Keeps tone humble and solution-focused (3-4 sentences)\n\n"
                "Response:"
            )
        },
        "disgust": {
            "prompt": (
                "Customer quality concern: '{review}'\n\n"
                "As a customer service representative, craft a response that:\n"
                "- Immediately acknowledges the product issue\n"
                "- Explains quality control measures being taken\n"
                "- Provides clear return/replacement instructions\n"
                "- Offers goodwill gesture (3-4 sentences)\n\n"
                "Response:"
            )
        },
        "fear": {
            "prompt": (
                "Customer safety concern: '{review}'\n\n"
                "As a customer service representative, craft a reassuring response that:\n"
                "- Directly addresses the safety worries\n"
                "- References relevant certifications/standards\n"
                "- Offers dedicated support contact\n"
                "- Provides satisfaction guarantee (3-4 sentences)\n\n"
                "Response:"
            )
        },
        "joy": {
            "prompt": (
                "Customer review: '{review}'\n\n"
                "As a customer service representative, craft a concise response that:\n"
                "- Specifically acknowledges both positive and constructive feedback\n"
                "- Briefly mentions loyalty/referral programs\n"
                "- Ends with shopping invitation (3-4 sentences)\n\n"
                "Response:"
            )
        },
        "neutral": {
            "prompt": (
                "Customer feedback: '{review}'\n\n"
                "As a customer service representative, craft a balanced response that:\n"
                "- Provides additional relevant product information\n"
                "- Highlights key service features\n"
                "- Politely requests more detailed feedback\n"
                "- Maintains professional tone (3-4 sentences)\n\n"
                "Response:"
            )
        },
        "sadness": {
            "prompt": (
                "Customer disappointment: '{review}'\n\n"
                "As a customer service representative, craft an empathetic response that:\n"
                "- Shows genuine understanding of the issue\n"
                "- Proposes personalized recovery solution\n"
                "- Offers extended support options\n"
                "- Maintains positive outlook (3-4 sentences)\n\n"
                "Response:"
            )
        },
        "surprise": {
            "prompt": (
                "Customer enthusiastic feedback: '{review}'\n\n"
                "As a customer service representative, craft a response that:\n"
                "- Matches customer's positive energy appropriately\n"
                "- Highlights unexpected product benefits\n"
                "- Invites to user community/events\n"
                "- Maintains brand voice (3-4 sentences)\n\n"
                "Response:"
            )
        }
    }  # Mapping of each emotion to its response template
    template = emotion_strategies[dominant_emotion['label'].lower()]["prompt"]  # Select template based on emotion
    prompt = template.format(review=user_review)  # Format the template with the user's review
    print(f"Prompt generated: {prompt}")  # Debug: print the generated prompt using an f-string
    return prompt  # Return the constructed prompt

def response_gen(user_review):
    """
    Generate a response based on the user's comment.
    For this application, always return a fixed response message.
    """
    fixed_response = ("Dear [Customer], I'm sorry to hear that you're experiencing a delay in delivery. "
                      "I understand how frustrating it can be when you're expecting a dress that you love. "
                      "I'd be happy to help you resolve this issue.")
    print(f"Response generated: {fixed_response}")  # Debug: print the generated response using an f-string
    return fixed_response  # Return the fixed response message

##########################################
# Step 3: Text-to-Speech Conversion Function
##########################################
def sound_gen(response):
    """
    Convert the fixed response to speech and save it as a .wav file,
    then embed an auto-playing audio player.
    """
    processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")  # Load the TTS processor
    model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")  # Load the TTS model
    vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")  # Load the vocoder for waveform generation

    # Process the full response text (no truncation) for spectrogram generation
    inputs = processor(text=response, return_tensors="pt")  # Tokenize and process the response text for TTS
    # Use dummy speaker embeddings (zeros) with the expected dimension (1 x 768)
    speaker_embeddings = torch.zeros(1, 768)  # Create placeholder speaker embeddings
    spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings)  # Generate the speech spectrogram

    with torch.no_grad():
        speech = vocoder(spectrogram)  # Convert the spectrogram to an audio waveform using the vocoder

    sf.write("customer_service_response.wav", speech.numpy(), samplerate=16000)  # Save the waveform as a .wav file
    st.audio("customer_service_response.wav", start_time=0)  # Embed an audio player that autoplays the audio

##########################################
# Main Function
##########################################
def main():
    """
    The main function orchestrates response generation and text-to-speech conversion.
    It displays only the fixed response and plays its audio.
    """
    if text:  # Check if the user has entered a comment (although the response is fixed)
        response = response_gen(text)  # Generate the fixed response message
        st.markdown(
            f"<p style='color:#3498DB; font-size:20px;'>{response}</p>",
            unsafe_allow_html=True
        )  # Display the response in styled formatting (only the fixed message is shown)
        sound_gen(response)  # Convert the response to speech and play it
        print(f"Final response output: {response}")  # Debug: print the final response using an f-string

# Execute the main function when the script is run
if __name__ == "__main__":
    main()  # Call the main function