Update app.py

app.py

@@ -26,15 +26,19 @@ model_options = {
 # ✅ 页面配置
 st.set_page_config(page_title="Emoji Offensive Text Detector", page_icon="🚨", layout="wide")
 
-# ✅ 侧边栏：模型选择
+# ✅ 页面布局
 with st.sidebar:
-    st.header("🧠 Settings")
-    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)
+    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.")
 
-# 初始化会话历史
 if "history" not in st.session_state:
     st.session_state.history = []
 
@@ -50,85 +54,75 @@ 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
-    })
-    return translated_text, label, score, reasoning
+    reasoning = f"The sentence was flagged as '{label}' due to potentially offensive phrases. Consider replacing emotionally charged, ambiguous, or abusive terms."
 
-# 主页面布局
-st.title("🚨 Emoji Offensive Text Detector & Analysis")
-st.markdown("---")
+    st.session_state.history.append({"text": text, "translated": translated_text, "label": label, "score": score, "reason": reasoning})
+    return translated_text, label, score, reasoning
 
-# 输入与分析
-st.header("✍️ Input & Moderation")
-def text_moderation_section():
-    st.markdown("Enter text with emojis or upload an image with text.")
-    text = st.text_area("Sentence (or OCR text will appear here):", height=120)
+# ✅ 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)
 
-    uploaded_file = st.file_uploader("Or upload an image for OCR:", type=["jpg", "jpeg", "png"])
-    if uploaded_file:
-        image = Image.open(uploaded_file)
-        st.image(image, caption="Uploaded Image", use_column_width=True)
-        with st.spinner("Extracting text via OCR..."):
-            ocr_text = pytesseract.image_to_string(image, lang="chi_sim+eng").strip()
-        st.text_area("Extracted Text:", value=ocr_text, height=120)
-        text = ocr_text
-
-    if st.button("🚦 Analyze Text") and text:
-        with st.spinner("Processing..."):
+    if st.button("🚦 Analyze"):
+        with st.spinner("🔍 Processing..."):
             try:
                 translated, label, score, reason = classify_emoji_text(text)
-                st.subheader("🔄 Translated Text")
-                st.code(translated)
-                st.subheader(f"🎯 Prediction: {label}")
-                st.write(f"Confidence: {score:.2%}")
-                st.subheader("🧠 Explanation")
+                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"Error during processing: {e}")
-
-# 分析仪表板
-st.markdown("---")
-st.header("📊 Violation Analysis")
-def analysis_dashboard():
-    if not st.session_state.history:
-        st.info("No data to display. Please analyze some text first.")
-        return
-
-    df = pd.DataFrame(st.session_state.history)
-
-    # 建议列表
-    st.subheader("📝 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']} ({item['score']:.2%})")
-        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)
-
-# 渲染各部分
-text_moderation_section()
-analysis_dashboard()
+                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"])
+
+    if uploaded_file is not None:
+        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.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.")