########################################## # 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()