Update app.py

app.py

@@ -19,31 +19,55 @@ model_options = {
     "BERT Emotion": "bhadresh-savani/bert-base-go-emotion"
 }
 
+# ✅ 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 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.
+""")
+
 # Streamlit 侧边栏模型选择
 selected_model = st.sidebar.selectbox("Choose classification model", list(model_options.keys()))
 selected_model_id = model_options[selected_model]
-
 classifier = pipeline("text-classification", model=selected_model_id, device=0 if torch.cuda.is_available() else -1)
 
+# ✅ 输入区域
+st.markdown("### ✍️ Input your sentence:")
+default_text = "你是🐷"
+text = st.text_area("Enter sentence with emojis:", value=default_text, height=150)
+
+# ✅ 主逻辑封装函数
 def classify_emoji_text(text: str):
-    """
-    Step 1: 翻译文本中的 emoji
-    Step 2: 使用分类器判断是否冒犯
-    """
     prompt = f"输入：{text}\n输出："
     input_ids = emoji_tokenizer(prompt, return_tensors="pt").to(emoji_model.device)
     with torch.no_grad():
         output_ids = emoji_model.generate(**input_ids, max_new_tokens=64, do_sample=False)
     decoded = emoji_tokenizer.decode(output_ids[0], skip_special_tokens=True)
-
-    # 保留真正输出部分（移除 prompt）
-    if "输出：" in decoded:
-        translated_text = decoded.split("输出：")[-1].strip()
-    else:
-        translated_text = decoded.strip()
+    translated_text = decoded.split("输出：")[-1].strip() if "输出：" in decoded else decoded.strip()
 
     result = classifier(translated_text)[0]
     label = result["label"]
     score = result["score"]
 
     return translated_text, label, score
+
+# ✅ 触发按钮
+if st.button("🚦 Analyze"):
+    with st.spinner("🔍 Processing..."):
+        try:
+            translated, label, score = classify_emoji_text(text)
+            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("👈 Please input text and click the button to classify.")