Update app.py

app.py

@@ -1,16 +1,21 @@
 import streamlit as st
 from transformers import pipeline
 from translate import Translator
-import time
+import re
 
 # Load models
 def load_models():
+    """Loads the sentiment analysis and summarization models."""
     sentiment_analyzer = pipeline("text-classification", model="miltonc/distilbert-base-uncased_ft_5")
     summarizer = pipeline("summarization", model="FelixChao/T5-Chinese-Summarization")
     return sentiment_analyzer, summarizer
 
+# Max length for models
+SENTIMENT_MAX_LENGTH = 512  # Maximum input length for DistilBERT sentiment analysis
+SUMMARIZER_MAX_LENGTH = 512  # Maximum input length for T5 summarization (approximate)
 
 def sentiment_analysis(text, sentiment_analyzer):
+    """Analyzes the sentiment of the input text."""
     try:
         result = sentiment_analyzer(text)[0]
         return result
@@ -18,9 +23,8 @@ def sentiment_analysis(text, sentiment_analyzer):
         print(f"sentiment_analysis error for '{text}': {e}. Returning 'sentiment_analysis Failed'")
         return {"label": "sentiment_analysis Failed", "score": 0.0}
 
-
-# Generate a narrative story using the GPT-2 genre-based story generator
 def summarize_news(text, summarizer):
+    """Summarizes the input news text."""
     try:
         summary = summarizer(text, max_length=30, min_length=10)[0]['summary_text']
         return summary
@@ -28,8 +32,8 @@ def summarize_news(text, summarizer):
         print(f"Summarization error for '{text}': {e}. Returning 'Summarization Failed'")
         return "Summarization Failed"
 
-        
 def translate_text(text, target_language='en', source_language='zh-TW'):
+    """Translates Chinese text to English."""
     translator = Translator(to_lang=target_language, from_lang=source_language)
     try:
         translation = translator.translate(text)
@@ -38,21 +42,38 @@ def translate_text(text, target_language='en', source_language='zh-TW'):
         print(f"Translation error for '{text}': {e}. Returning 'Translation Failed'")
         return "Translation Failed"
 
-        
+# Check if the input text is Chinese
+def is_chinese(text):
+    """Checks if the input text contains Chinese characters."""
+    return bool(re.search(r'[\u4e00-\u9fff]', text))
+
 # Main Streamlit app
 def main():
+    """Main Streamlit application."""
     st.title("AI-Powered Sentiment Analysis and Summarization")
 
     sentiment_analyzer, summarizer = load_models()
 
-    text = st.text_area("Enter the Chinese text here.....", height=200) # Changed from file_uploader to text_area
+    text = st.text_area(f"Enter the Chinese text here (Max {max(SENTIMENT_MAX_LENGTH, SUMMARIZER_MAX_LENGTH)} characters).....", height=200)
 
-    if st.button("Analyze"): # Added a button
+    if st.button("Analyze"):
         if text:
+            if not is_chinese(text):
+                st.error("Error: Please enter Chinese text.")
+                return
+
+            if len(text) > SENTIMENT_MAX_LENGTH:
+                st.error(f"Error: Input text for sentiment analysis exceeds the maximum length of {SENTIMENT_MAX_LENGTH} characters. Please shorten your text.")
+                return
+
+            if len(text) > SUMMARIZER_MAX_LENGTH:
+                st.error(f"Error: Input text for summarization exceeds the maximum length of {SUMMARIZER_MAX_LENGTH} characters. Please shorten your text.")
+                return
+
             with st.spinner("Analyzing sentiment..."):
                 text_en = translate_text(text, target_language='en', source_language='zh-TW')
                 sentiment_result = sentiment_analysis(text_en, sentiment_analyzer)
-                label_map = {"LABEL_0": 'negative', "LABEL_1": 'neutral', "LABEL_2": 'positive'} # direct mapping
+                label_map = {"LABEL_0": 'negative', "LABEL_1": 'neutral', "LABEL_2": 'positive'}
                 try:
                     sentiment_label = label_map.get(sentiment_result['label'], 'Unknown')
                     sentiment_score = sentiment_result['score']
@@ -68,4 +89,4 @@ def main():
                 st.write(story)
 
 if __name__ == "__main__":
-    main()
+    main()