Update app.py

app.py

@@ -1,19 +1,51 @@
 import streamlit as st
 from agent import classify_emoji_text
 
-st.set_page_config(page_title="Emoji Offensive Classifier", page_icon="🤖")
-st.title("🧠 Emoji Offensive Text Detector")
+# 页面配置
+st.set_page_config(page_title="Emoji Offensive Classifier", page_icon="🧠", layout="wide")
+st.title("🔥 Emoji-Based Offensive Text Classifier")
 
-st.markdown("Enter a sentence containing emoji and check if it’s offensive.")
+st.markdown("Detect potentially offensive Chinese sentences enhanced with emojis, slang, or homophones.")
 
-example = "你是🐷"
-text = st.text_area("Enter text:", value=example, height=100)
+# 分两栏布局
+left_col, right_col = st.columns([2, 1])
 
-if st.button("Analyze"):
-    with st.spinner("Processing..."):
-        translated, label, score = classify_emoji_text(text)
-        st.markdown(f"**Translated Text:** `{translated}`")
-        st.markdown(f"**Prediction:** `{label}`")
-        st.markdown(f"**Confidence:** `{score:.2%}`")
-else:
-    st.info("Click the button to start analysis.")
+# 左侧输入
+with left_col:
+    st.subheader("📥 Input Text")
+    example = "你是🐷"
+    user_input = st.text_area("Paste your message here:", value=example, height=200)
+    model_choice = st.selectbox(
+        "Choose offensive classifier model:",
+        options=[
+            "cardiffnlp/twitter-roberta-base-offensive",
+            "facebook/roberta-hate-speech-dynabench",
+            "microsoft/deberta-v3-base"
+        ],
+        index=0,
+        help="Select a backend classifier. LLM used for emoji translation is fixed (Qwen1.5-emoji)."
+    )
+
+    if st.button("🚦 Run Detection"):
+        with st.spinner("Running model inference..."):
+            translated, label, score = classify_emoji_text(user_input, model_id=model_choice)
+            st.session_state.result = {
+                "text": translated,
+                "label": label,
+                "score": score
+            }
+    else:
+        st.info("Click the button to start analysis.")
+
+# 右侧输出
+with right_col:
+    st.subheader("📊 Analysis Results")
+
+    if "result" in st.session_state:
+        result = st.session_state.result
+        st.success("✅ Classification Complete")
+        st.markdown(f"**Translated Text:** `{result['text']}`")
+        st.markdown(f"**Prediction:** `{result['label']}`")
+        st.markdown(f"**Confidence Score:** `{result['score']:.2%}`")
+    else:
+        st.markdown("⚠️ No output yet. Run detection to see results.")