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 emotion 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

##########################################
# Streamlit application title and input
##########################################
st.title("Just Comment")  # Set the app title for user interface
st.write("I'm listening to you, my friend")  # Add a brief app description
text = st.text_area("Enter your comment", "")  # Text area for user to input their comment or feedback

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

##########################################
# Step 2: Response Generation Function
##########################################
def response_gen(user_review):
    """
    Generate a concise and logical response based on the sentiment of the user's comment.
    """
    
    dominant_emotion = analyze_dominant_emotion(user_review)  # Get the dominant emotion of the user's comment
    emotion_label = dominant_emotion['label'].lower()  # Extract the emotion label in lowercase format
    
    # Define response templates for each 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:"
          )
      }

    # Select the appropriate prompt based on the user's emotion, or default to neutral
    prompt = emotion_prompts.get(
        emotion_label,
        f"Neutral feedback: '{user_review}'\n\nWrite a professional and concise response (50-200 words max).\n\nResponse:"
    )
    
    # Load the tokenizer and language model for text generation
    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B") # Load tokenizer for processing text inputs
    model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-0.5B") # Load language model for response generation

    inputs = tokenizer(prompt, return_tensors="pt")  # Tokenize the input prompt
    outputs = model.generate(
        **inputs,
        max_new_tokens=300,  # Set the upper limit of tokens generated to ensure the response isn't too lengthy
        min_length=75,  # Set the minimum length of the generated response
        no_repeat_ngram_size=2,  # Avoid repeating phrases
        temperature=0.7  # Add slight randomness for natural-sounding responses
    )

    # Decode the generated response back into text
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    print(f" {response}")  # Debug print statement for generated text
    return response  # Return the generated response

##########################################
# Step 3: Text-to-Speech Conversion Function
##########################################
def sound_gen(response):
    """
    Convert the generated response to speech and save as a .wav file.
    """
    # Load the pre-trained TTS models for speech synthesis
    processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")  # Pre-trained processor for TTS
    model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")  # Pre-trained TTS model
    vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")  # Vocoder for generating waveforms

    # Load a neutral female voice embedding from a pre-trained dataset
    embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")  # Load speaker embeddings
    speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)  # Use a default speaker embedding

    # Process the input text and create a speech spectrogram
    inputs = processor(text=response, return_tensors="pt")
    spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings)

    # Convert the spectrogram into an audio waveform using the vocoder
    with torch.no_grad():
        speech = vocoder(spectrogram)
    
    # Save the audio as a .wav file
    sf.write("customer_service_response.wav", speech.numpy(), samplerate=16000)
    # Play the generated audio in the Streamlit app
    st.audio("customer_service_response.wav")  # Embed an audio player in the web app


##########################################
# Main Function
##########################################
def main():
    """
    Main function to orchestrate the workflow of sentiment analysis, response generation, and text-to-speech.
    """
    if text:  # Check if the user has entered a comment
        response = response_gen(text)  # Generate a logical and concise response
        st.write(f"I wanna tell you that: {response}")  # Display the generated response in the Streamlit app
        sound_gen(response)  # Convert the text response to speech and make it available for playback

# Run the main function when the script is executed
if __name__ == "__main__":
    main()