Update app.py

app.py

@@ -5,7 +5,7 @@ import numpy as np
 import torch
 from evaluate import load
 
-# 加载情感分析模型和分词器
+# Load sentiment analysis model and word divider
 try:
     model = AutoModelForSequenceClassification.from_pretrained(
         "tabularisai/multilingual-sentiment-analysis",
@@ -20,15 +20,15 @@ try:
         tokenizer=tokenizer
     )
 except Exception as e:
-    st.error(f"加载情感分析模型时出错: {e}")
+    st.error(f"Error loading sentiment analysis model: {e}")
 
-# 加载关键词提取模型
+# Load keyword extraction model
 try:
     kw_model = KeyBERT()
 except Exception as e:
-    st.error(f"加载关键词提取模型时出错: {e}")
+    st.error(f"Error loading sentiment analysis model: {e}")
 
-# 定义标签映射
+# Define tag mapping
 label_map = {
     "Very Negative": 0,
     "Negative": 1,
@@ -37,21 +37,21 @@ label_map = {
     "Very Positive": 4
 }
 
-# Streamlit 应用标题
-st.title("文本情感与关键词分析")
+# Streamlit 
+st.title("Text emotion and keyword analysis")
 
-# 创建文本输入框
-input_text = st.text_area("请输入评价性文本", "")
+# Create a text entry box
+input_text = st.text_area("Please enter user feedback", "")
 
 if input_text:
     try:
-        # 情感分析
+        # sentiment analysis
         result = sentiment_pipeline(input_text)
         predicted_label = label_map[result[0]['label']]
-        rating = predicted_label + 1  # 将标签转换为 1 - 5 的评分
+        rating = predicted_label + 1  
         confidence = result[0]['score']
 
-        # 关键词提取
+        # ketwords extraction
         keywords = kw_model.extract_keywords(
             input_text,
             keyphrase_ngram_range=(1, 2),
@@ -60,9 +60,9 @@ if input_text:
         )
         keyword_text = [kw[0] for kw in keywords]
 
-        # 显示结果
-        st.write(f"情感评分: {rating}/5")
-        st.write(f"置信度: {confidence:.2f}")
-        st.write(f"关键词: {', '.join(keyword_text)}")
+        # result showing
+        st.write(f"Rating: {rating}/5")
+        st.write(f"Confidence: {confidence:.2f}")
+        st.write(f"Key words: {', '.join(keyword_text)}")
     except Exception as e:
-        st.error(f"分析文本时出错: {e}")
+        st.error(f"Error while analyzing text: {e}")