Update app.py

app.py

@@ -10,7 +10,7 @@ from transformers import (  # For text classification, text-to-speech, and text
     AutoModelForCausalLM,
     AutoTokenizer
 )
-from datasets import load_dataset  # To load speaker embeddings dataset
+from datasets import load_dataset  # For loading speaker embeddings dataset
 import torch  # For tensor operations
 import soundfile as sf  # For saving audio as .wav files
 import sentencepiece  # Required by SpeechT5Processor for tokenization
@@ -18,25 +18,25 @@ import sentencepiece  # Required by SpeechT5Processor for tokenization
 ##########################################
 # Streamlit application title and input
 ##########################################
-# Display a deep blue title in a large, visually appealing font
+# Display a deep blue title using HTML and CSS
 st.markdown(
-    "<h1 style='text-align: center; color: #00008B; font-size: 50px;'>🚀 Just Comment</h1>",
+    "<h1 style='text-align: center; color: #00008B; font-size: 50px;'>Just Comment</h1>",
     unsafe_allow_html=True
-)  # Set deep blue title
+)  # Set the title in deep blue
 
 # Display a gentle, warm subtitle below the title
 st.markdown(
-    "<h3 style='text-align: center; color: #5D6D7E; font-style: italic;'>I'm listening to you, my friend～</h3>",
+    "<h3 style='text-align: center; color: #5D6D7E; font-style: italic;'>I'm listening to you, my friend</h3>",
     unsafe_allow_html=True
-)  # Set a friendly subtitle
+)  # Set the subtitle with warm styling
 
-# Add a text area for user input with placeholder and tooltip
+# Provide a text area for user input with a placeholder and tooltip
 text = st.text_area(
     "Enter your comment",
     placeholder="Type something here...",
     height=100,
-    help="Write a comment you would like us to respond to!"  # Provide tooltip
-)  # Create text input field
+    help="Write a comment you would like us to respond to!"  # Tooltip for guidance
+)  # Create the text input field
 
 ##########################################
 # Step 1: Sentiment Analysis Function
@@ -45,14 +45,17 @@ def analyze_dominant_emotion(user_review):
     """
     Analyze the dominant emotion in the user's comment using a fine-tuned text classification model.
     """
+    # Load the fine-tuned sentiment classification model from Hugging Face
     emotion_classifier = pipeline(
         "text-classification",
         model="Thea231/jhartmann_emotion_finetuning",
         return_all_scores=True
-    )  # Load the sentiment classification model
-    emotion_results = emotion_classifier(user_review)[0]  # Get sentiment scores for the input text
-    dominant_emotion = max(emotion_results, key=lambda x: x['score'])  # Identify the emotion with highest score
-    return dominant_emotion  # Return the dominant emotion (as a dict with label and score)
+    )
+    # Get sentiment scores for the input text
+    emotion_results = emotion_classifier(user_review)[0]
+    # Identify the emotion with the highest confidence score
+    dominant_emotion = max(emotion_results, key=lambda x: x['score'])
+    return dominant_emotion  # Return the dominant emotion as a dictionary
 
 ##########################################
 # Step 2: Response Generation Functions
@@ -61,9 +64,9 @@ def prompt_gen(user_review):
     """
     Generate the text generation prompt based on the user's comment and detected emotion.
     """
-    # Get dominant emotion for the input
-    dominant_emotion = analyze_dominant_emotion(user_review)  # Analyze user's comment
-    # Define response templates for 7 emotions
+    # Determine the dominant emotion from the user's comment
+    dominant_emotion = analyze_dominant_emotion(user_review)
+    # Define prompt templates for seven emotions
     emotion_strategies = {
         "anger": {
             "prompt": (
@@ -72,7 +75,7 @@ def prompt_gen(user_review):
                 "- Begins with a sincere apology and acknowledgment.\n"
                 "- Clearly explains a solution process with concrete steps.\n"
                 "- Offers appropriate compensation or redemption.\n"
-                "- Keeps a humble and solution-focused tone (1-3 sentences).\n\n"
+                "- Keeps a humble, solution-focused tone (1-3 sentences).\n\n"
                 "Response:"
             )
         },
@@ -81,7 +84,7 @@ def prompt_gen(user_review):
                 "Customer quality concern: '{review}'\n\n"
                 "As a customer service representative, craft a response that:\n"
                 "- Immediately acknowledges the product issue.\n"
-                "- Explains measures taken in quality control.\n"
+                "- Explains quality control measures being taken.\n"
                 "- Provides clear return/replacement instructions.\n"
                 "- Offers a goodwill gesture (1-3 sentences).\n\n"
                 "Response:"
@@ -91,9 +94,9 @@ def prompt_gen(user_review):
             "prompt": (
                 "Customer safety concern: '{review}'\n\n"
                 "As a customer service representative, craft a reassuring response that:\n"
-                "- Directly addresses the safety worries.\n"
+                "- Directly addresses safety worries.\n"
                 "- References relevant certifications or standards.\n"
-                "- Offers a dedicated support contact.\n"
+                "- Offers dedicated support contact.\n"
                 "- Provides a satisfaction guarantee (1-3 sentences).\n\n"
                 "Response:"
             )
@@ -104,7 +107,7 @@ def prompt_gen(user_review):
                 "As a customer service representative, craft a concise response that:\n"
                 "- Thanks the customer for their feedback.\n"
                 "- Acknowledges both positive and constructive points.\n"
-                "- Invites them to explore loyalty or referral programs (1-3 sentences).\n\n"
+                "- Invites exploration of loyalty or referral programs (1-3 sentences).\n\n"
                 "Response:"
             )
         },
@@ -112,9 +115,9 @@ def prompt_gen(user_review):
             "prompt": (
                 "Customer feedback: '{review}'\n\n"
                 "As a customer service representative, craft a balanced response that:\n"
-                "- Provides additional relevant product information.\n"
+                "- Provides relevant product information.\n"
                 "- Highlights key service features.\n"
-                "- Politely requests more detailed feedback.\n"
+                "- Politely requests detailed feedback.\n"
                 "- Maintains a professional tone (1-3 sentences).\n\n"
                 "Response:"
             )
@@ -136,37 +139,40 @@ def prompt_gen(user_review):
                 "As a customer service representative, craft a response that:\n"
                 "- Matches the customer's positive energy.\n"
                 "- Highlights unexpected product benefits.\n"
-                "- Invites the customer to join community events or programs.\n"
+                "- Invites the customer to join community events.\n"
                 "- Maintains the brand's voice (1-3 sentences).\n\n"
                 "Response:"
             )
         }
-    }  # Dictionary mapping each emotion to a prompt template
-    # Get the template for the detected emotion, default to 'neutral' if not found
+    }  # End dictionary of prompt templates
+    # Select the template based on detected emotion; default to neutral if not found
     template = emotion_strategies.get(dominant_emotion["label"].lower(), emotion_strategies["neutral"])["prompt"]
-    prompt = template.format(review=user_review)  # Insert the user review into the template
-    print(f"Generated prompt: {prompt}")  # Debug print using f-string
-    return prompt  # Return the generated prompt
+    prompt = template.format(review=user_review)  # Format the prompt with the user's comment
+    print(f"Generated prompt: {prompt}")  # Debug: print the generated prompt using an f-string
+    return prompt  # Return the text generation prompt
 
 def response_gen(user_review):
     """
-    Generate a response using text generation based on the user's comment.
+    Generate a response using text generation based on the user's comment and detected emotion.
     """
-    prompt = prompt_gen(user_review)  # Get the generated prompt using the detected emotion template
+    # Get the text generation prompt based on the user's comment and its dominant emotion
+    prompt = prompt_gen(user_review)
     # Load the tokenizer and language model for text generation
-    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B")  # Load tokenizer for text generation
-    model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-0.5B")  # Load causal language model for generation
+    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B")  # Load tokenizer
+    model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-0.5B")  # Load causal language model
     inputs = tokenizer(prompt, return_tensors="pt")  # Tokenize the prompt
+    # Generate a response with constraints to ensure a concise and complete answer
     outputs = model.generate(
         **inputs,
-        max_new_tokens=100,  # Allow up to 100 new tokens for the answer
-        min_length=30,  # Ensure a minimum length for the generated response
+        max_new_tokens=100,  # Allow up to 100 new tokens for the generated answer
+        min_length=30,       # Ensure at least 30 tokens in the generated response
         no_repeat_ngram_size=2,  # Avoid repeated phrases
-        temperature=0.7  # Use a moderate temperature for creativity
-    )  # Generate response from the model
-    input_length = inputs.input_ids.shape[1]  # Determine length of the input prompt
-    response = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True)  # Extract only generated answer text
-    print(f"Generated response: {response}")  # Debug print using f-string
+        temperature=0.7      # Moderate randomness for creative responses
+    )
+    input_length = inputs.input_ids.shape[1]  # Get the length of the input prompt
+    # Decode only the generated text (after the prompt)
+    response = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True)
+    print(f"Generated response: {response}")  # Debug: print the generated response using an f-string
     return response  # Return the generated response
 
 ##########################################
@@ -176,20 +182,22 @@ def sound_gen(response):
     """
     Convert the generated response to speech and embed an auto-playing audio player.
     """
-    # Load SpeechT5 processor, TTS model, and vocoder
+    # Load the SpeechT5 processor, TTS model, and vocoder for audio synthesis
     processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")  # Load TTS processor
     model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")  # Load TTS model
     vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")  # Load vocoder
-    # Process the full generated response text for TTS
-    inputs = processor(text=response, return_tensors="pt")  # Convert text to model input tensors
-    # Use dummy speaker embeddings with shape (1,768) to avoid dimension mismatch
-    speaker_embeddings = torch.zeros(1, 768, dtype=torch.float32)  # Create dummy speaker embedding
-    spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings)  # Generate speech spectrogram
+    # Process the entire generated response text for TTS
+    inputs = processor(text=response, return_tensors="pt")  # Tokenize and process the response
+    # Create a dummy speaker embedding with the expected dimension (1 x 1280)
+    speaker_embeddings = torch.zeros(1, 1280, dtype=torch.float32)  # Dummy embedding to avoid shape mismatches
+    # Generate the speech spectrogram using the input tokens and dummy speaker embeddings
+    spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings)
     with torch.no_grad():
-        speech = vocoder(spectrogram)  # Convert spectrogram to waveform
-    # Save the audio as a .wav file with 16kHz sampling rate
-    sf.write("customer_service_response.wav", speech.numpy(), samplerate=16000)  # Write the waveform to file
-    st.audio("customer_service_response.wav", start_time=0)  # Embed an auto-playing audio widget
+        speech = vocoder(spectrogram)  # Convert the spectrogram into an audio waveform
+    # Save the audio waveform as a .wav file with a 16kHz sampling rate
+    sf.write("customer_service_response.wav", speech.numpy(), samplerate=16000)
+    # Embed an auto-playing audio player in the app to play the full response
+    st.audio("customer_service_response.wav", start_time=0)
 
 ##########################################
 # Main Function
@@ -197,16 +205,16 @@ def sound_gen(response):
 def main():
     """
     Main function to orchestrate text generation and text-to-speech conversion.
-    It displays only the generated response and plays its audio.
+    It displays only the generated response and plays its audio without extra information.
     """
-    if text:  # Check if the user has entered a comment
-        response = response_gen(text)  # Generate a response using text generation based on emotion
+    if text:  # Only proceed if the user has entered a comment
+        response = response_gen(text)  # Generate a response based on text generation and emotion detection
         st.markdown(
             f"<p style='color:#3498DB; font-size:20px;'>{response}</p>",
             unsafe_allow_html=True
-        )  # Display the generated response in styled format
-        sound_gen(response)  # Convert the generated response to speech and embed the audio player
-        print(f"Final generated response: {response}")  # Debug print using f-string
+        )  # Display the response in styled formatting (only the final answer)
+        sound_gen(response)  # Convert the full generated response to speech and embed the audio player
+        print(f"Final generated response: {response}")  # Debug: print the final response using an f-string
 
 # Execute the main function when the script is run
 if __name__ == "__main__":