Update app.py

app.py

@@ -1,39 +1,48 @@
 import streamlit as st
 from transformers import pipeline
 
-# 頁面設定
-st.set_page_config(page_title="輕量級文本分類器", page_icon="📊", layout="centered")
-st.title("🧠 輕量級 AI 文本判斷器")
+# Page setup
+st.set_page_config(page_title="Hate Speech Detector", page_icon="🚨", layout="centered")
+st.title("🚨 Hate Speech Classification Demo")
 st.markdown("""
-使用開源模型 `distilbert-base-uncased-finetuned-sst-2-english` 進行快速文本分類。  
-此模型主要訓練於英文情感分類任務，可初步判斷文本是否帶有正向或負面情緒。  
+This app uses the fine-tuned model `JenniferHJF/qwen1.5-emoji-finetuned` to predict whether the given input text contains **offensive or hateful content**.
 
-> ⚠️ 注意：模型非專門用於仇恨語言檢測，僅用作演示用途。
+The model returns:
+- `1` if the content is offensive
+- `0` if it is not offensive
+
+⚠️ Note: This is a demo and the model may not be perfect in detecting nuanced or implicit hate speech.
 """)
 
-# 示例句子
+# Example inputs
 examples = [
-    "I really love how inclusive this community is!",
-    "This is terrible and offensive.",
-    "The movie was okay, not great.",
-    "I hate everything about this.",
-    "What a lovely message!",
-    "You are stupid and shouldn't exist."
+    "You're a disgrace to this country.",
+    "Hope you have a great day!",
+    "Why are you even alive?",
+    "That was really rude and uncalled for.",
+    "You are amazing and smart!",
+    "Get lost, nobody wants you here."
 ]
 
-selected_example = st.selectbox("📚 選擇範例句子", options=examples, index=0)
-text = st.text_area("✍️ 或自行輸入文本：", value=selected_example, height=150)
+selected_example = st.selectbox("📘 Choose an example sentence:", options=examples, index=0)
+text = st.text_area("📝 Or enter your own text below:", value=selected_example, height=150)
 
-if st.button("🚦 開始分析"):
-    with st.spinner("模型正在分析中..."):
-        classifier = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
-        result = classifier(text)[0]
-        label = result['label']
-        score = result['score']
+if st.button("🚀 Analyze"):
+    with st.spinner("Running model inference..."):
+        classifier = pipeline("text-generation", model="JenniferHJF/qwen1.5-emoji-finetuned", max_new_tokens=20)
+        output = classifier(f"""Please determine whether the following text is offensive.
+Reply with '1' for offensive, '0' for non-offensive.
+Text: {text}
+""")[0]["generated_text"]
 
-        st.markdown(f"### 🔍 預測結果：`{label}`")
-        st.progress(score)
-        st.write(f"模型信心指數：**{score:.2%}**")
-else:
-    st.info("👈 請輸入內容，然後點擊「開始分析」")
+        # Extract the last '0' or '1' from output
+        prediction = "Unknown"
+        if "1" in output.strip().splitlines()[-1]:
+            prediction = "Offensive (1)"
+        elif "0" in output.strip().splitlines()[-1]:
+            prediction = "Non-Offensive (0)"
 
+        st.markdown(f"### ✅ Prediction: `{prediction}`")
+        st.code(output.strip(), language="text")
+else:
+    st.info("👈 Enter text and click 'Analyze' to begin.")