Update app.py

app.py

@@ -1,50 +1,41 @@
 import streamlit as st
 from transformers import pipeline
 
-# 加载模型
 @st.cache_resource
 def load_pipelines():
     sentiment_pipeline = pipeline(model="KeviniveK/CustomModel_IMDB")
+    # 轻量化翻译模型
     translation_pipeline = pipeline(
         "translation_en_to_zh",
-        model="Helsinki-NLP/opus-mt-en-zh"
+        model="google/t5-small"
     )
     return sentiment_pipeline, translation_pipeline
 
 sentiment_pipeline, translation_pipeline = load_pipelines()
 
-# 标签映射
 label_map = {
-    "LABEL_0": "差评 | Negative",
-    "LABEL_1": "好评 | Positive"
+    "LABEL_0": "差评 (Negative)",
+    "LABEL_1": "好评 (Positive)"
 }
 
-# 页面标题
 st.title("影评分析与翻译 | Sentiment Analysis & Translation")
 st.write("请输入一段英文影评，我们将分析其情感并翻译成中文。")
 st.write("Enter an English movie review below. The app will analyze its sentiment and translate it into Chinese.")
 
-# 用户输入
 user_input = st.text_area("英文影评输入 | Enter English Movie Review", height=150)
 
 if user_input:
-    # 情感分析
     result = sentiment_pipeline(user_input)
-    sentiment_raw = result[0]["label"]
-    sentiment = label_map.get(sentiment_raw, sentiment_raw)
+    sentiment = label_map[result[0]["label"]]
     confidence = result[0]["score"]
 
-    # 翻译
+    # 直接调用翻译 pipeline
     translation = translation_pipeline(user_input)
     translated_text = translation[0]["translation_text"]
 
-    # 显示结果
     st.subheader("情感分析结果 | Sentiment Analysis Result")
     st.write(f"**情感 (Sentiment):** {sentiment}")
     st.write(f"**置信度 (Confidence):** {confidence:.2f}")
 
     st.subheader("中文翻译结果 | Chinese Translation")
-    st.write(translated_text)
-
-if __name__ == "__main__":
-    pass
+    st.write(translated_text)