Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,73 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import requests
+
+# ====================== 页面配置 ======================
+st.set_page_config(
+    page_title="Manipulative Language Detector",
+    page_icon="🧠",
+    layout="wide"
+)
+
+# ====================== 模型加载 ======================
+@st.cache_resource
+def load_model():
+    model_name = "LilithHu/mbert-manipulative-detector"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    model.eval()
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# ====================== 多语言支持 ======================
+lang = st.sidebar.selectbox("Language / 语言", ["English", "中文"])
+st.sidebar.markdown("---")
+
+# ====================== 标题 ======================
+if lang == "English":
+    st.title("🧠 Manipulative Language Detector")
+    st.markdown("This tool uses an AI model to detect manipulative language in messages.")
+else:
+    st.title("🧠 情感操控语言识别器")
+    st.markdown("本工具使用 AI 模型检测文本中的情感操控语言。")
+
+st.markdown("---")
+
+# ====================== 用户输入 ======================
+user_text = st.text_area("Enter your message / 输入文本", height=150)
+
+# ====================== 推理函数 ======================
+def predict(text):
+    inputs = tokenizer(text, truncation=True, padding='max_length', max_length=128, return_tensors='pt')
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predicted_class = torch.argmax(logits, dim=1).item()
+    return predicted_class
+
+# ====================== 按钮 & 结果显示 ======================
+if st.button("🔍 Analyze / 分析"):
+    if not user_text.strip():
+        st.warning("⚠️ Please enter a message!" if lang == "English" else "⚠️ 请输入内容！")
+    else:
+        with st.spinner("Analyzing..." if lang == "English" else "分析中..."):
+            try:
+                prediction = predict(user_text)
+                label = "manipulative" if prediction == 1 else "non-manipulative"
+
+                if label == "manipulative":
+                    st.error("⚠️ Manipulative Language Detected!" if lang == "English" else "⚠️ 检测到操纵性语言！")
+                else:
+                    st.success("✅ No manipulative language detected." if lang == "English" else "✅ 未检测到操纵性语言。")
+            except Exception as e:
+                st.error(f"❌ Error: {e}")
+
+# ====================== 页脚 ======================
+st.markdown("---")
+st.markdown(
+    "<p style='text-align: center; color: #888;'>© 2025 Manipulative Language Detector</p>",
+    unsafe_allow_html=True
+)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))