Update app.py

app.py

@@ -1,33 +1,36 @@
 import streamlit as st
-from agent import classify_emoji_text, available_models
+from agent import classify_emoji_text
 
-st.set_page_config(page_title="Emoji Offensive Classifier", page_icon="🚨", layout="wide")
-st.title("🚨 Offensive Text Detection Agent (with Emoji Translation)")
+# ✅ 页面配置
+st.set_page_config(page_title="Emoji Offensive Text Detector", page_icon="🚨", layout="wide")
+
+# ✅ 页面标题
+st.title("🧠 Emoji-based Offensive Language Classifier")
 
 st.markdown("""
-This demo uses a two-step AI agent:
-1. Translates emojis & phonetic expressions into clear Chinese using a fine-tuned Qwen model.
-2. Classifies the translated sentence for offensiveness using a selectable large language model.
+This application translates emojis in a sentence and classifies whether the final sentence is offensive or not using two AI models.
+- The **first model** translates emoji or symbolic phrases into standard Chinese text.
+- The **second model** performs offensive language detection.
 """)
 
-# 左侧：输入区
-st.subheader("① Enter a sentence with emoji or homophones:")
-example = "你是🐷，好像🤡"
-text = st.text_area("Input text:", value=example, height=120)
-
-# 右侧：模型选择区
-st.subheader("② Select classification model:")
-model_choice = st.selectbox("Choose a classifier", options=list(available_models.keys()))
+# ✅ 输入区域
+default_text = "你是🐷"
+text = st.text_area("✍️ Input your sentence here:", value=default_text, height=150)
 
+# ✅ 触发按钮
 if st.button("🚦 Analyze"):
-    with st.spinner("Running multi-stage agent pipeline..."):
-        translated, label, score = classify_emoji_text(text, model_choice)
-
-        st.success("✅ Completed!")
-        st.markdown(f"### 🔄 Translated sentence:\n```
-{translated}
-```")
-        st.markdown(f"### 🎯 Prediction: `{label}`")
-        st.markdown(f"### 📊 Confidence: `{score:.2%}`")
+    with st.spinner("🔍 Processing..."):
+        try:
+            translated, label, score = classify_emoji_text(text)
+
+            # 输出结果显示（修复多行字符串语法）
+            st.markdown("### 🔄 Translated sentence:")
+            st.code(translated, language="text")
+
+            st.markdown(f"### 🎯 Prediction: `{label}`")
+            st.markdown(f"### 📊 Confidence Score: `{score:.2%}`")
+
+        except Exception as e:
+            st.error(f"❌ An error occurred during processing:\n\n{e}")
 else:
-    st.info("Click 'Analyze' to start inference.")
+    st.info("👈 Please input text and click the button to classify.")