Update app.py

app.py

@@ -38,6 +38,7 @@ 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)
@@ -49,18 +50,9 @@ 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."
-    )
+    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
-    })
+    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
@@ -86,7 +78,7 @@ if st.button("🚦 Analyze Text"):
 
 # 图片上传与 OCR
 st.markdown("---")
-st.subheader("2. 图片 OCR & 分类")
+st.subheader("2. Image OCR")
 uploaded_file = st.file_uploader("Upload an image (JPG/PNG)", type=["jpg","jpeg","png"])
 if uploaded_file:
     image = Image.open(uploaded_file)
@@ -127,42 +119,5 @@ 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)
-
-    # —— 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:
-            res = classifier(word)[0]
-            word_scores.append({
-                "Word": word,
-                "Label": res["label"],
-                "Score": res["score"]
-            })
-        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)
-
-        max_display = 5
-        # 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.")
 else:
     st.info("⚠️ No classification data available yet.")