Update app.py

app.py

@@ -26,19 +26,6 @@ model_options = {
 # 页面配置
 st.set_page_config(page_title="Emoji Offensive Text Detector", page_icon="🚨", layout="wide")
 
-# 页面布局
-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 editing suggestions based on past analyses.")
-
 # 初始化历史记录
 if "history" not in st.session_state:
     st.session_state.history = []
@@ -60,14 +47,21 @@ 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
 
-# 功能逻辑
-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"):
+# 页面布局
+st.sidebar.header("🧠 Settings")
+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)
+
+# 主页面：集成 Text Moderation 和 Text Analysis
+st.title("🚨 Emoji Offensive Text Detector & Violation Analysis")
+
+# 输入与分类
+st.markdown("## ✍️ 输入或上传文本进行分类")
+col1, col2 = st.columns([2,1])
+with col1:
+    text = st.text_area("Enter sentence with emojis:", value="你是🐷", height=150)
+    if st.button("🚦 Analyze Text"):
         with st.spinner("🔍 Processing..."):
             try:
                 translated, label, score, reason = classify_emoji_text(text)
@@ -78,48 +72,44 @@ if section == "📍 Text Moderation":
                 st.markdown(f"### 📊 Confidence Score: `{score:.2%}`")
                 st.markdown("### 🧠 Model Explanation:")
                 st.info(reason)
-
             except Exception as e:
-                st.error(f"❌ An error occurred during processing:\n\n{e}")
+                st.error(f"❌ Error during processing: {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"])
-
-    if uploaded_file is not None:
+with col2:
+    st.markdown("### 🖼️ Or upload a screenshot:")
+    uploaded_file = st.file_uploader("Image (JPG/PNG)", type=["jpg","png","jpeg"])
+    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")
-            st.markdown("#### 📋 Extracted Text:")
-            st.code(ocr_text.strip())
-
-            translated, label, score, reason = classify_emoji_text(ocr_text.strip())
-            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.image(image, caption="Uploaded Image", use_column_width=True)
+        with st.spinner("🧠 Running OCR..."):
+            ocr_text = pytesseract.image_to_string(image, lang="chi_sim+eng").strip()
+            st.markdown("#### 📋 OCR Extracted Text:")
+            st.code(ocr_text)
+            translated, label, score, reason = classify_emoji_text(ocr_text)
+            st.markdown("#### 🔄 Translated:")
+            st.code(translated)
+            st.markdown(f"#### 🎯 Prediction: `{label}`")
+            st.markdown(f"#### 📊 Confidence: `{score:.2%}`")
+            st.markdown("#### 🧠 Explanation:")
             st.info(reason)
 
-elif section == "📊 Text Analysis":
-    st.title("📊 Violation Analysis Dashboard")
-    if 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']}")
-
-        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")
-        st.plotly_chart(radar_fig)
-    else:
-        st.info("⚠️ No classification data available yet.")
+st.markdown("---")
+
+# 违规分析仪表盘
+st.markdown("## 📊 Violation Analysis Dashboard")
+if st.session_state.history:
+    df = pd.DataFrame(st.session_state.history)
+    st.markdown("### 🧾 历史记录详情")
+    for item in st.session_state.history:
+        st.markdown(f"- 🔹 **输入:** `{item['text']}` | **Label:** `{item['label']}` | **Confidence:** `{item['score']:.2%}`")
+        st.markdown(f"  - **Translated:** `{item['translated']}`")
+        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")
+    st.plotly_chart(radar_fig)
+else:
+    st.info("⚠️ No data available. Please analyze some text first.")