Update app.py

app.py

@@ -38,7 +38,6 @@ if "history" not in st.session_state:
     st.session_state.history = []
 
 # 分类函数
-
 def classify_emoji_text(text: str):
     prompt = f"输入：{text}\n输出："
     input_ids = emoji_tokenizer(prompt, return_tensors="pt").to(emoji_model.device)
@@ -50,9 +49,18 @@ def classify_emoji_text(text: str):
     result = classifier(translated_text)[0]
     label = result["label"]
     score = result["score"]
-    reasoning = f"The sentence was flagged as '{label}' due to potentially offensive phrases. Consider replacing emotionally charged, ambiguous, or abusive terms."
-
-    st.session_state.history.append({"text": text, "translated": translated_text, "label": label, "score": score, "reason": reasoning})
+    reasoning = (
+        f"The sentence was flagged as '{label}' due to potentially offensive phrases. "
+        "Consider replacing emotionally charged, ambiguous, or abusive terms."
+    )
+
+    st.session_state.history.append({
+        "text": text,
+        "translated": translated_text,
+        "label": label,
+        "score": score,
+        "reason": reasoning
+    })
     return translated_text, label, score, reasoning
 
 # 主页面：输入与分析共存
@@ -119,38 +127,42 @@ if st.session_state.history:
     radar_fig = px.line_polar(radar_df, r='Score', theta='Category', line_close=True, title="⚠️ Risk Radar by Category")
     radar_fig.update_traces(line_color='black')
     st.plotly_chart(radar_fig)
-else:
-    st.info("⚠️ No classification data available yet.")
-        # 單詞冒犯性分析模塊
+
+    # —— 新增：单词级冒犯性相关性分析 —— #
     st.markdown("### 🧬 Word-level Offensive Correlation")
 
+    # 取最近一次翻译文本，按空格拆分单词
     last_translated_text = st.session_state.history[-1]["translated"]
     words = last_translated_text.split()
 
+    # 对每个单词进行分类并收集分数
     word_scores = []
     for word in words:
         try:
-            result = classifier(word)[0]
+            res = classifier(word)[0]
             word_scores.append({
                 "Word": word,
-                "Label": result["label"],
-                "Score": result["score"]
+                "Label": res["label"],
+                "Score": res["score"]
             })
-        except:
+        except Exception:
             continue
 
     if word_scores:
         word_df = pd.DataFrame(word_scores)
         word_df = word_df.sort_values(by="Score", ascending=False).reset_index(drop=True)
 
-        # 顯示前5個，隱藏表格邊框
         max_display = 5
-        show_more = st.toggle("Show more words")
+        # Streamlit 1.22+ 支持 st.toggle，若版本不支持可改用 checkbox
+        show_more = st.toggle("Show more words", value=False)
 
         display_df = word_df if show_more else word_df.head(max_display)
+        # 隐藏边框并渲染 HTML 表格
         st.markdown(
             display_df.to_html(index=False, border=0),
             unsafe_allow_html=True
         )
     else:
-        st.info("❕ No word-level analysis available.")
+        st.info("❕ No word-level analysis available.")
+else:
+    st.info("⚠️ No classification data available yet.")