Update app.py

app.py

@@ -2,25 +2,14 @@
 # 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  # For tokenization (required by SpeechT5Processor)
 
 ##########################################
 # Streamlit application title and input
 ##########################################
 # Display a colorful, large title in a visually appealing font
 st.markdown(
-    "<h1 style='text-align: center; color: #FF5733; font-size: 50px;'>Just Comment</h1>",
+    "<h1 style='text-align: center; color: #FF5720; font-size: 50px;'>Just Comment</h1>",
     unsafe_allow_html=True
 )  # Use HTML and CSS to set a custom title design
 
@@ -34,7 +23,7 @@ st.markdown(
 text = st.text_area(
     "Enter your comment", 
     placeholder="Type something here...", 
-    height=150, 
+    height=280, 
     help="Write a comment you would like us to analyze and respond to!"  # Provide a helpful tooltip
 )
 
@@ -70,7 +59,7 @@ def response_gen(user_review):
     # Define response templates for each emotion
     emotion_prompts = {
         "anger": (
-            f"Customer complaint: '{user_review}'\n\n"
+            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"
@@ -79,7 +68,7 @@ def response_gen(user_review):
             "Response:"
         ),
         "disgust": (
-            f"Customer quality concern: '{user_review}'\n\n"
+            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"
@@ -88,7 +77,7 @@ def response_gen(user_review):
             "Response:"
         ),
         "fear": (
-            f"Customer safety concern: '{user_review}'\n\n"
+            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"
@@ -97,7 +86,7 @@ def response_gen(user_review):
             "Response:"
         ),
         "joy": (
-            f"Customer review: '{user_review}'\n\n"
+            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"
@@ -105,7 +94,7 @@ def response_gen(user_review):
             "Response:"
         ),
         "neutral": (
-            f"Customer feedback: '{user_review}'\n\n"
+            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"
@@ -114,7 +103,7 @@ def response_gen(user_review):
             "Response:"
         ),
         "sadness": (
-            f"Customer disappointment: '{user_review}'\n\n"
+            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"
@@ -123,7 +112,7 @@ def response_gen(user_review):
             "Response:"
         ),
         "surprise": (
-            f"Customer enthusiastic feedback: '{user_review}'\n\n"
+            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"
@@ -154,7 +143,7 @@ def response_gen(user_review):
 
     # Decode the generated response back into readable text
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    print(f"Generated response: {response}")  # Print the response for debugging
+    print(f"{response}")  # Print the response for debugging
     return response  # Return the generated response
 
 ##########################################