Update app.py

app.py

@@ -1,36 +1,49 @@
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+import torch
 import streamlit as st
-from agent import classify_emoji_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.
-""")
-
-# ✅ 输入区域
-default_text = "你是🐷"
-text = st.text_area("✍️ Input your sentence here:", value=default_text, height=150)
-
-# ✅ 触发按钮
-if st.button("🚦 Analyze"):
-    with st.spinner("🔍 Processing..."):
-        try:
-            translated, label, score = classify_emoji_text(text)
-
-            # 输出结果显示（修复多行字符串语法）
-            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.")
+# ✅ Step 1: Emoji 翻译模型（你自己训练的模型）
+emoji_model_id = "JenniferHJF/qwen1.5-emoji-finetuned"
+emoji_tokenizer = AutoTokenizer.from_pretrained(emoji_model_id, trust_remote_code=True)
+emoji_model = AutoModelForCausalLM.from_pretrained(
+    emoji_model_id,
+    trust_remote_code=True,
+    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: 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"
+}
+
+# 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)
+
+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"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=64, do_sample=False)
+    decoded = emoji_tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    # 保留真正输出部分（移除 prompt）
+    if "输出：" in decoded:
+        translated_text = decoded.split("输出：")[-1].strip()
+    else:
+        translated_text = decoded.strip()
+
+    result = classifier(translated_text)[0]
+    label = result["label"]
+    score = result["score"]
+
+    return translated_text, label, score