Update app.py

app.py

@@ -5,23 +5,19 @@ import streamlit as st
 from transformers import pipeline
 from langdetect import detect
 
-
-# 继续应用的主逻辑
-# 加载翻译pipeline、加载情感分析pipeline、定义语言映射
-# 然后定义 main() 函数等逻辑...
-
-# 加载翻译pipeline
+# 加载翻译 pipeline
 @st.cache_resource
 def load_translation_pipeline():
-    return pipeline("translation", model="facebook/m2m100_418M")
+    return pipeline("translation", model="facebook/m2m100_418M", max_length=256)
 
-# 加载情感分析pipeline
+# 加载情感分析 pipeline
 @st.cache_resource
 def load_sentiment_pipeline():
-    return pipeline("sentiment-analysis", model="Rocky080808/finetuned-roberta-base")
+    return pipeline("sentiment-analysis", model="Rocky080808/finetuned-roberta-base", max_length=128)
 
 # 定义语言映射
 language_name_map = {
+    'en': "English",
     'zh-cn': "Chinese (Simplified)",
     'zh-tw': "Chinese (Traditional)",
     'ja': "Japanese",
@@ -30,63 +26,60 @@ language_name_map = {
     'fr': "French"
 }
 
+# 翻译到英语的函数
 def translate_to_english(text, translation_pipeline):
-    # 检测语言
     detected_language = detect(text)
     
     # 语言映射
     language_map = {
-        'zh-cn': "zh", 
-        'zh-tw': "zh", 
-        'ja': "ja",     
-        'de': "de",     
-        'es': "es",     
-        'fr': "fr"      
+        'en': "en",     # 英语直接通过
+        'zh-cn': "zh",  # Simplified Chinese
+        'zh-tw': "zh",  # Traditional Chinese
+        'ja': "ja",     # Japanese
+        'de': "de",     # German
+        'es': "es",     # Spanish
+        'fr': "fr"      # French
     }
-    
+
     if detected_language not in language_map:
         return None, "Unsupported language"
     
-    # 翻译到英语
-    translated_text = translation_pipeline(text, src_lang=language_map[detected_language], tgt_lang="en")
+    # 如果检测到是英语，直接返回原文本
+    if detected_language == 'en':
+        return text, "en"
     
+    # 翻译为英语
+    translated_text = translation_pipeline(text, src_lang=language_map[detected_language], tgt_lang="en")
     return translated_text[0]['translation_text'], language_name_map.get(detected_language, detected_language)
 
 # 主程序逻辑
 def main():
-    # 加载模型
+    # 加载翻译和情感分析模型
     translation_pipeline = load_translation_pipeline()
     sentiment_pipeline = load_sentiment_pipeline()
 
-    st.title("Global Customer Sentiment Analyzer")
+    st.title("Global Customer Reviews Sentiment Analyzer")
     st.write("Analyze customer sentiment in multiple languages including Chinese, Japanese, German, Spanish, and French.")
 
-    st.write("There will be 5 kinds of results. Please take action accordingly when needed.")
-    st.write("Very dissatisfied, immediate follow-up is required.")
-    st.write("Dissatisfied, please arrange follow-up.")
-    st.write("Neutral sentiment, further case analysis is needed.")
-    st.write("Satisfied, the customer may return for a purchase.")
-    st.write("Very satisfied, the customer is very likely to return and recommend.")
-    
     user_input = st.text_input("Enter customer comments in supported languages:")
 
     # 用户点击分析按钮后触发
     if st.button("Analyze"):
         if user_input:
-            # 翻译
+            # 翻译或直接处理英语
             translated_text, detected_language = translate_to_english(user_input, translation_pipeline)
             
             if detected_language == "Unsupported language":
                 st.write("The input language is not supported. Please use Chinese, Japanese, German, Spanish, or French.")
             else:
-                # 显示检测语言和翻译结果
-                st.write(f"Detected language: {detected_language}")
-                st.write(f"Translated Text: {translated_text}")
+                # 显示检测语言和翻译结果（如果需要翻译）
+                st.write(f"Detected language: {language_name_map.get(detected_language, detected_language)}")
+                st.write(f"Translated Text: {translated_text}" if detected_language != "English" else f"Original Text: {translated_text}")
 
                 # 情感分析
                 result = sentiment_pipeline(translated_text)
-                label_str = result[0]["label"]  
-                label = int(label_str.split("_")[-1])  
+                label_str = result[0]["label"]
+                label = int(label_str.split("_")[-1])
                 confidence = result[0]["score"]
 
                 # 情感结果映射
@@ -107,3 +100,4 @@ if __name__ == "__main__":
 
 
 
+