########################################## # 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( "

Just Comment

", unsafe_allow_html=True ) # Use HTML and CSS to set a custom title design # Display a smaller, gentle and warm subtitle below the title st.markdown( "

I'm listening to you, my friend

", 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=280, 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 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:" ) } # Select the appropriate prompt based on the user's emotion 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 response generation tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B") # Load tokenizer for text processing model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-0.5B") # Load language model for text generation inputs = tokenizer(prompt, return_tensors="pt") # Tokenize the input prompt outputs = model.generate( **inputs, max_new_tokens=300, # Limit generated tokens to ensure concise responses min_length=75, # Ensure the generated response is logical and complete no_repeat_ngram_size=2, # Avoid repetitive phrases temperature=0.7 # Add randomness for natural-sounding responses ) # Decode the generated response back into text response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(f"Generated response: {response}") # Debugging: print the response 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 it as a .wav file. """ processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts") # Load processor for TTS model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts") # Load pre-trained TTS model vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") # Load vocoder for waveform generation # Load neutral female voice embedding from 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 generate a spectrogram inputs = processor(text=response, return_tensors="pt") spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings) # Use vocoder to convert the spectrogram into a waveform with torch.no_grad(): speech = vocoder(spectrogram) # Save the audio file as .wav sf.write("customer_service_response.wav", speech.numpy(), samplerate=16000) # Create an auto-playing audio player in Streamlit st.audio("customer_service_response.wav", start_time=0) # Enable audio playback with autoplay ########################################## # 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 a concise and logical response st.markdown( f"

{response}

", unsafe_allow_html=True ) # Display the response in a styled font sound_gen(response) # Convert the response to speech and play it # Execute the main function if __name__ == "__main__": main()