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##########################################
# 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 colorful, large title in a visually appealing font
st.markdown(
"<h1 style='text-align: center; color: #FF5720; font-size: 50px;'>Just Comment</h1>",
unsafe_allow_html=True
) # Use HTML and CSS for a custom title design
# Display a smaller, gentle 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
) # Use HTML for a friendly and soft-styled subtitle
# Add a well-designed text area for user input
text = st.text_area(
"Enter your comment",
placeholder="Type something here...",
height=150,
help="Write a comment you would like us to analyze and respond to!" # Provide a helpful tooltip
)
##########################################
# Step 1: Sentiment Analysis Function
##########################################
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 fine-tuned text classification model from Hugging Face
emotion_results = emotion_classifier(user_review)[0] # Perform sentiment analysis on 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_prompts = {
"anger": (
f"'{user_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": (
f"'{user_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": (
f"'{user_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": (
f"'{user_review}'\n\n"
"As a customer service representative, craft a concise and enthusiastic response that:\n"
"- Thanks the customer for their feedback\n"
"- Acknowledges both positive and constructive comments\n"
"- Invites them to explore loyalty programs\n\n"
"Response:"
),
"neutral": (
f"'{user_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": (
f"'{user_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": (
f"'{user_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:"
)
}
prompt = emotion_prompts.get(
emotion_label,
f"Neutral feedback: '{user_review}'\n\nWrite a professional and concise response (50-200 words max).\n\nResponse:"
) # Default to neutral if emotion is not found
# Load the tokenizer and language model for response 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,
min_length=75, # Ensure concise and complete responses
no_repeat_ngram_size=2, # Avoid repetitive phrases
temperature=0.7 # Add randomness for more natural responses
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True) # Decode the generated response
return response # Return the response
##########################################
# Step 3: Text-to-Speech Conversion Function
##########################################
def sound_gen(response):
"""
Convert the generated response to speech and save it as a .wav file.
"""
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
# Create speaker embedding to match text input
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 embedding
inputs = processor(text=response, return_tensors="pt") # Process text for spectrogram generation
inputs["input_ids"] = inputs["input_ids"].to(torch.int32) # Match tensor format (fix runtime error)
spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings) # Generate the spectrogram
with torch.no_grad():
speech = vocoder(spectrogram) # Convert spectrogram to waveform
sf.write("customer_service_response.wav", speech.numpy(), samplerate=16000) # Save as .wav file
st.audio("customer_service_response.wav", start_time=0) # Embed an auto-playing audio player
##########################################
# Main Function
##########################################
def main():
"""
Main function to handle sentiment analysis, response generation, and text-to-speech functionalities.
"""
if text: # Check if the user has entered a comment
response = response_gen(text) # Generate the response
st.markdown(
f"<p style='color:#3498DB; font-size:20px;'>{response}</p>",
unsafe_allow_html=True
) # Display the response with styled formatting
sound_gen(response) # Convert the response to speech and play it
# Execute the main function
if __name__ == "__main__":
main() |