Update app.py

app.py

@@ -1,5 +1,6 @@
 from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 import torch
+import streamlit as st
 
 # ✅ Step 1: Emoji 翻译模型（你自己训练的模型）
 emoji_model_id = "JenniferHJF/qwen1.5-emoji-finetuned"
@@ -7,33 +8,66 @@ emoji_tokenizer = AutoTokenizer.from_pretrained(emoji_model_id, trust_remote_cod
 emoji_model = AutoModelForCausalLM.from_pretrained(
     emoji_model_id,
     trust_remote_code=True,
-    torch_dtype=torch.float16
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
 ).to("cuda" if torch.cuda.is_available() else "cpu")
 emoji_model.eval()
 
-# ✅ Step 2: 冒犯性文本识别模型
-classifier = pipeline("text-classification", model="unitary/toxic-bert", device=0 if torch.cuda.is_available() else -1)
+# ✅ Step 2: 可选择的冒犯性文本识别模型
+model_options = {
+    "Toxic-BERT": "unitary/toxic-bert",
+    "Roberta Offensive": "cardiffnlp/twitter-roberta-base-offensive",
+    "BERT Emotion": "bhadresh-savani/bert-base-go-emotion"
+}
 
-def classify_emoji_text(text: str):
-    """
-    Step 1: 翻译文本中的 emoji
-    Step 2: 使用分类器判断是否冒犯
-    """
-    prompt = f"""请判断下面的文本是否具有冒犯性。
-这里的“冒犯性”主要指包含人身攻击、侮辱、歧视、仇恨言论或极端粗俗的内容。
-如果文本具有冒犯性，请仅回复冒犯；如果不具有冒犯性，请仅回复不冒犯。
-文本如下：
-{text}
-"""
+# ✅ 页面配置
+st.set_page_config(page_title="Emoji Offensive Text Detector", page_icon="🚨", layout="wide")
+
+# ✅ 页面标题
+st.title("🧠 Emoji-based Offensive Language Classifier")
+
+st.markdown("""
+This application translates emojis in a sentence and classifies whether the final sentence is offensive or not using two AI models.
+- The **first model** translates emoji or symbolic phrases into standard Chinese text.
+- The **second model** performs offensive language detection.
+""")
+
+# Streamlit 侧边栏模型选择
+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)
+
+# ✅ 输入区域
+st.markdown("### ✍️ Input your sentence:")
+default_text = "你是🐷"
+text = st.text_area("Enter sentence with emojis:", value=default_text, height=150)
 
+# ✅ 主逻辑封装函数
+def classify_emoji_text(text: str):
+    prompt = f"输入：{text}\n输出："
     input_ids = emoji_tokenizer(prompt, return_tensors="pt").to(emoji_model.device)
     with torch.no_grad():
-        output_ids = emoji_model.generate(**input_ids, max_new_tokens=50, do_sample=False)
+        output_ids = emoji_model.generate(**input_ids, max_new_tokens=64, do_sample=False)
     decoded = emoji_tokenizer.decode(output_ids[0], skip_special_tokens=True)
-    translated_text = decoded.strip().split("文本如下：")[-1].strip()
+    translated_text = decoded.split("输出：")[-1].strip() if "输出：" in decoded else decoded.strip()
 
     result = classifier(translated_text)[0]
     label = result["label"]
     score = result["score"]
 
-    return translated_text, label, score
+    return translated_text, label, score
+
+# ✅ 触发按钮
+if st.button("🚦 Analyze"):
+    with st.spinner("🔍 Processing..."):
+        try:
+            translated, label, score = 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%}`")
+
+        except Exception as e:
+            st.error(f"❌ An error occurred during processing:\n\n{e}")
+else:
+    st.info("👈 Please input text and click the button to classify.")