Update app.py

app.py

@@ -26,22 +26,18 @@ model_options = {
 # ✅ 页面配置
 st.set_page_config(page_title="Emoji Offensive Text Detector", page_icon="🚨", layout="wide")
 
-# ✅ 页面布局
+# ✅ 侧边栏：模型选择
 with st.sidebar:
-    st.header("🧠 Navigation")
-    section = st.radio("Select Mode:", ["📍 Text Moderation", "📊 Text Analysis"])
-
-    if section == "📍 Text Moderation":
-        selected_model = st.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)
-
-    elif section == "📊 Text Analysis":
-        st.markdown("You can view the violation distribution chart and editing suggestions.")
+    st.header("🧠 Configuration")
+    selected_model = st.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)
 
+# 初始化历史记录
 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："
@@ -59,70 +55,69 @@ def classify_emoji_text(text: str):
     st.session_state.history.append({"text": text, "translated": translated_text, "label": label, "score": score, "reason": reasoning})
     return translated_text, label, score, reasoning
 
-# ✅ Section logic
-if section == "📍 Text Moderation":
-    st.title("📍 Offensive Text Classification")
-    st.markdown("### ✍️ Input your sentence:")
-    default_text = "你是🐷"
-    text = st.text_area("Enter sentence with emojis:", value=default_text, height=150)
-
-    if st.button("🚦 Analyze"):
-        with st.spinner("🔍 Processing..."):
-            try:
-                translated, label, score, reason = 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%}")
-                st.markdown(f"### 🧠 Model Explanation:")
-                st.info(reason)
-
-            except Exception as e:
-                st.error(f"❌ An error occurred during processing:\n\n{e}")
-
-    st.markdown("---")
-    st.markdown("### 🖼️ Or upload a screenshot of bullet comments:")
-
-    uploaded_file = st.file_uploader("Upload an image (JPG/PNG)", type=["jpg", "jpeg", "png"])
+# 主页面：输入与分析共存
+st.title("🚨 Emoji Offensive Text Detector & Analysis Dashboard")
 
-    if uploaded_file is not None:
-        image = Image.open(uploaded_file)
-        st.image(image, caption="Uploaded Screenshot", use_column_width=True)
+# 文本输入
+st.subheader("1. 输入与分类")
+default_text = "你是🐷"
+text = st.text_area("Enter sentence with emojis:", value=default_text, height=150)
 
-        with st.spinner("🧠 Extracting text via OCR..."):
-            ocr_text = pytesseract.image_to_string(image, lang="chi_sim+eng")
-            st.markdown("#### 📋 Extracted Text:")
-            st.code(ocr_text.strip())
-
-            translated, label, score, reason = classify_emoji_text(ocr_text.strip())
-            st.markdown("### 🔄 Translated sentence:")
+if st.button("🚦 Analyze Text"):
+    with st.spinner("🔍 Processing..."):
+        try:
+            translated, label, score, reason = 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%}")
-            st.markdown("### 🧠 Model Explanation:")
+            st.markdown(f"**Prediction:** {label}")
+            st.markdown(f"**Confidence Score:** {score:.2%}")
+            st.markdown("**Model Explanation:**")
             st.info(reason)
-
-elif section == "📊 Text Analysis":
-    st.title("📊 Violation Analysis Dashboard")
-    if st.session_state.history:
-        df = pd.DataFrame(st.session_state.history)
-        # 已移除 Offensive Category Distribution 饼图
-
-        st.markdown("### 🧾 Offensive Terms & Suggestions")
-        for item in st.session_state.history:
-            st.markdown(f"- 🔹 **Input:** {item['text']}")
-            st.markdown(f"   - ✨ **Translated:** {item['translated']}")
-            st.markdown(f"   - ❗ **Label:** {item['label']} with **{item['score']:.2%}** confidence")
-            st.markdown(f"   - 🔧 **Suggestion:** {item['reason']}")
-
-        radar_df = pd.DataFrame({
-            "Category": ["Insult", "Abuse", "Discrimination", "Hate Speech", "Vulgarity"],
-            "Score": [0.7, 0.4, 0.3, 0.5, 0.6]
-        })
-        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')  # 将雷达图线条改为黑色
-        st.plotly_chart(radar_fig)
-    else:
-        st.info("⚠️ No classification data available yet.")
+        except Exception as e:
+            st.error(f"❌ An error occurred:\n{e}")
+
+# 图片上传与 OCR
+st.markdown("---")
+st.subheader("2. 图片 OCR & 分类")
+uploaded_file = st.file_uploader("Upload an image (JPG/PNG)", type=["jpg","jpeg","png"])
+if uploaded_file:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Screenshot", use_column_width=True)
+    with st.spinner("🧠 Extracting text via OCR..."):
+        ocr_text = pytesseract.image_to_string(image, lang="chi_sim+eng").strip()
+        if ocr_text:
+            st.markdown("**Extracted Text:**")
+            st.code(ocr_text)
+            translated, label, score, reason = classify_emoji_text(ocr_text)
+            st.markdown("**Translated sentence:**")
+            st.code(translated, language="text")
+            st.markdown(f"**Prediction:** {label}")
+            st.markdown(f"**Confidence Score:** {score:.2%}")
+            st.markdown("**Model Explanation:**")
+            st.info(reason)
+        else:
+            st.info("⚠️ No text detected in the image.")
+
+# 分析仪表盘
+st.markdown("---")
+st.subheader("3. Violation Analysis Dashboard")
+if st.session_state.history:
+    # 展示历史记录
+    df = pd.DataFrame(st.session_state.history)
+    st.markdown("### 🧾 Offensive Terms & Suggestions")
+    for item in st.session_state.history:
+        st.markdown(f"- 🔹 **Input:** {item['text']}")
+        st.markdown(f"   - ✨ **Translated:** {item['translated']}")
+        st.markdown(f"   - ❗ **Label:** {item['label']} with **{item['score']:.2%}** confidence")
+        st.markdown(f"   - 🔧 **Suggestion:** {item['reason']}")
+
+    # 雷达图
+    radar_df = pd.DataFrame({
+        "Category": ["Insult","Abuse","Discrimination","Hate Speech","Vulgarity"],
+        "Score": [0.7,0.4,0.3,0.5,0.6]
+    })
+    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.")