Update app.py

app.py

@@ -1 +1,198 @@
-########################################## # Step 0: Import required libraries ########################################## import streamlit as st # For building the web application 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("Comment Reply for You") # Application title st.write("Generate automatic replies for user comments") # Application description text = st.text_area("Enter your comment", "") # Text input for user to enter comments ########################################## # Step 1: Sentiment Analysis Function ########################################## def analyze_dominant_emotion(user_review): """ Analyze the dominant emotion in the user's review using a text classification model. """ emotion_classifier = pipeline( "text-classification", model="Thea231/jhartmann_emotion_finetuning", return_all_scores=True ) # Load pre-trained emotion classification model emotion_results = emotion_classifier(user_review)[0] # Get emotion scores for the review dominant_emotion = max(emotion_results, key=lambda x: x['score']) # Find the emotion with the highest confidence return dominant_emotion ########################################## # Step 2: Response Generation Function ########################################## def response_gen(user_review): """ Generate a response based on the sentiment of the user's review. """ # Use Llama-based model to create a response based on a generated prompt dominant_emotion = analyze_dominant_emotion(user_review) # Get the dominant emotion emotion_label = dominant_emotion['label'].lower() # Extract emotion label # Define response templates for each emotion emotion_prompts = { "anger": ( "Customer complaint: '{review}'\n\n" "As a customer service representative, write a response that:\n" "- Sincerely apologizes for the issue\n" "- Explains how the issue will be resolved\n" "- Offers compensation where appropriate\n\n" "Response:" ), "joy": ( "Customer review: '{review}'\n\n" "As a customer service representative, write a positive 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:" ), # Add other emotions as needed... } # Format the prompt with the user's review prompt = emotion_prompts.get(emotion_label, "Neutral").format(review=user_review) # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-0.5B") inputs = tokenizer(prompt, return_tensors="pt") # Tokenize the prompt outputs = model.generate(**inputs, max_new_tokens=100) # Generate a response input_length = inputs.input_ids.shape[1] # Length of the input text response = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True) # Decode the generated text return 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 processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts") model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts") vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") # Load speaker embeddings (e.g., neutral female voice) embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) # 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 the vocoder to generate a waveform with torch.no_grad(): speech = vocoder(spectrogram) # Save the generated speech as a .wav file sf.write("customer_service_response.wav", speech.numpy(), samplerate=16000) st.audio("customer_service_response.wav") # Play the audio in Streamlit ########################################## # 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 entered a comment response = response_gen(text) # Generate a response st.write(f"Generated response: {response}") # Display the generated response sound_gen(response) # Convert the response to speech and play it # Run the main function if __name__ == "__main__": main() 
+##########################################
+# 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()