diff --git a/src/streamlit_tweet_app.py b/src/streamlit_tweet_app.py
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+"""Streamlit 演示应用 - 推文情感分析
+
+航空推文情感分析 AI 助手 - 支持情感分类、解释和处置方案生成。
+"""
+
+import os
+import sys
+
+import streamlit as st
+
+# Ensure project root is in path
+sys.path.append(os.getcwd())
+
+from dotenv import load_dotenv
+
+from src.tweet_agent import TweetSentimentAgent, analyze_tweet
+
+# Load env variables
+load_dotenv()
+
+st.set_page_config(page_title="航空推文情感分析", page_icon="✈️", layout="wide")
+
+# Sidebar Configuration
+st.sidebar.header("🔧 配置")
+st.sidebar.markdown("### 模型信息")
+st.sidebar.info(
+ """
+ **模型**: VotingClassifier (5个基学习器)
+ - Logistic Regression
+ - Multinomial Naive Bayes
+ - Random Forest
+ - LightGBM
+ - XGBoost
+
+ **性能**: Macro-F1 = 0.7533 ✅
+ """
+)
+
+st.sidebar.markdown("---")
+# Mode Selection
+mode = st.sidebar.radio("功能选择", ["📝 单条分析", "📊 批量分析", "📈 数据概览"])
+
+# Initialize session state
+if "agent" not in st.session_state:
+ with st.spinner("🔄 加载模型..."):
+ st.session_state.agent = TweetSentimentAgent()
+
+if "batch_results" not in st.session_state:
+ st.session_state.batch_results = []
+
+
+# --- Helper Functions ---
+
+
+def get_sentiment_emoji(sentiment: str) -> str:
+ """获取情感对应的表情符号"""
+ emoji_map = {
+ "negative": "😠",
+ "neutral": "😐",
+ "positive": "😊",
+ }
+ return emoji_map.get(sentiment, "❓")
+
+
+def get_sentiment_color(sentiment: str) -> str:
+ """获取情感对应的颜色"""
+ color_map = {
+ "negative": "#ff6b6b",
+ "neutral": "#ffd93d",
+ "positive": "#6bcb77",
+ }
+ return color_map.get(sentiment, "#e0e0e0")
+
+
+def get_priority_color(priority: str) -> str:
+ """获取优先级对应的颜色"""
+ color_map = {
+ "high": "#ff4757",
+ "medium": "#ffa502",
+ "low": "#2ed573",
+ }
+ return color_map.get(priority, "#e0e0e0")
+
+
+# --- Main Views ---
+
+if mode == "📝 单条分析":
+ st.title("✈️ 航空推文情感分析")
+ st.markdown("输入推文文本，获取 AI 驱动的情感分析、解释和处置方案。")
+
+ # Input form
+ with st.form("tweet_analysis_form"):
+ col1, col2 = st.columns([3, 1])
+
+ with col1:
+ tweet_text = st.text_area(
+ "推文内容",
+ placeholder="@United This is the worst airline ever! My flight was delayed for 5 hours...",
+ height=100,
+ )
+
+ with col2:
+ airline = st.selectbox(
+ "航空公司",
+ ["United", "US Airways", "American", "Southwest", "Delta", "Virgin America"],
+ )
+
+ submitted = st.form_submit_button("🔍 分析", type="primary")
+
+ if submitted and tweet_text:
+ with st.spinner("🤖 AI 正在分析..."):
+ try:
+ result = analyze_tweet(tweet_text, airline)
+
+ # Display results
+ st.divider()
+
+ # Header with sentiment
+ sentiment_emoji = get_sentiment_emoji(result.classification.sentiment)
+ sentiment_color = get_sentiment_color(result.classification.sentiment)
+
+ st.markdown(
+ f"""
+
+
{sentiment_emoji} {result.classification.sentiment.upper()}
+
置信度: {result.classification.confidence:.1%}
+
+ 优先级: {result.disposal_plan.priority.upper()}
+
+
+ **行动类型**: {result.disposal_plan.action_type}
+ """,
+ unsafe_allow_html=True,
+ )
+
+ if result.disposal_plan.suggested_response:
+ st.markdown("**建议回复:**")
+ st.success(result.disposal_plan.suggested_response)
+
+ st.markdown("**后续行动:**")
+ for action in result.disposal_plan.follow_up_actions:
+ st.write(f"- {action}")
+
+ except Exception as e:
+ st.error(f"分析失败: {e!s}")
+
+elif mode == "📊 批量分析":
+ st.title("📊 批量推文分析")
+ st.markdown("上传 CSV 文件或输入多条推文，进行批量情感分析。")
+
+ # Input method selection
+ input_method = st.radio("输入方式", ["手动输入", "CSV 上传"], horizontal=True)
+
+ if input_method == "手动输入":
+ st.markdown("### 输入推文（每行一条）")
+ tweets_input = st.text_area(
+ "推文列表",
+ placeholder="@United Flight delayed again!\n@Southwest Great service!\n@American Baggage policy?",
+ height=200,
+ )
+
+ if st.button("🔍 批量分析", type="primary") and tweets_input:
+ lines = [line.strip() for line in tweets_input.split("\n") if line.strip()]
+
+ if lines:
+ with st.spinner("🤖 AI 正在分析..."):
+ results = []
+ for line in lines:
+ try:
+ # Extract airline from tweet (simple heuristic)
+ airline = "United" # Default
+ for a in ["United", "US Airways", "American", "Southwest", "Delta", "Virgin America"]:
+ if a.lower() in line.lower():
+ airline = a
+ break
+
+ result = analyze_tweet(line, airline)
+ results.append(result)
+ except Exception as e:
+ st.warning(f"分析失败: {line[:50]}... - {e}")
+
+ if results:
+ st.session_state.batch_results = results
+ st.success(f"✅ 成功分析 {len(results)} 条推文")
+
+ else: # CSV upload
+ st.markdown("### 上传 CSV 文件")
+ st.info("CSV 文件应包含以下列: `text` (推文内容), `airline` (航空公司)")
+
+ uploaded_file = st.file_uploader("选择文件", type=["csv"])
+
+ if uploaded_file and st.button("🔍 分析上传文件", type="primary"):
+ import pandas as pd
+
+ try:
+ df = pd.read_csv(uploaded_file)
+
+ if "text" not in df.columns:
+ st.error("CSV 文件必须包含 'text' 列")
+ else:
+ with st.spinner("🤖 AI 正在分析..."):
+ results = []
+ for _, row in df.iterrows():
+ try:
+ text = row["text"]
+ airline = row.get("airline", "United")
+ result = analyze_tweet(text, airline)
+ results.append(result)
+ except Exception as e:
+ st.warning(f"分析失败: {text[:50]}... - {e}")
+
+ if results:
+ st.session_state.batch_results = results
+ st.success(f"✅ 成功分析 {len(results)} 条推文")
+
+ except Exception as e:
+ st.error(f"文件读取失败: {e!s}")
+
+ # Display batch results
+ if st.session_state.batch_results:
+ st.divider()
+ st.subheader(f"📊 分析结果 ({len(st.session_state.batch_results)} 条)")
+
+ # Summary statistics
+ sentiments = [r.classification.sentiment for r in st.session_state.batch_results]
+ negative_count = sentiments.count("negative")
+ neutral_count = sentiments.count("neutral")
+ positive_count = sentiments.count("positive")
+
+ col1, col2, col3 = st.columns(3)
+ col1.metric("😠 负面", negative_count)
+ col2.metric("😐 中性", neutral_count)
+ col3.metric("😊 正面", positive_count)
+
+ # Detailed results table
+ st.markdown("### 详细结果")
+
+ results_data = []
+ for r in st.session_state.batch_results:
+ results_data.append({
+ "推文": r.tweet_text[:50] + "..." if len(r.tweet_text) > 50 else r.tweet_text,
+ "航空公司": r.airline,
+ "情感": f"{get_sentiment_emoji(r.classification.sentiment)} {r.classification.sentiment}",
+ "置信度": f"{r.classification.confidence:.1%}",
+ "优先级": r.disposal_plan.priority.upper(),
+ "行动类型": r.disposal_plan.action_type,
+ })
+
+ st.dataframe(results_data, use_container_width=True)
+
+ # Clear button
+ if st.button("🗑️ 清除结果"):
+ st.session_state.batch_results = []
+ st.rerun()
+
+elif mode == "📈 数据概览":
+ st.title("📈 数据集概览")
+ st.markdown("查看训练数据集的统计信息。")
+
+ try:
+ import polars as pl
+ from src.tweet_data import load_cleaned_tweets, print_data_summary
+
+ df = load_cleaned_tweets("data/Tweets_cleaned.csv")
+
+ # Display summary
+ st.subheader("📊 数据统计")
+ print_data_summary(df, "数据集统计")
+
+ # Display sample data
+ st.subheader("📝 样本数据")
+ sample_df = df.head(10).to_pandas()
+ st.dataframe(sample_df, use_container_width=True)
+
+ # Sentiment distribution chart
+ st.subheader("📈 情感分布")
+ sentiment_counts = df.group_by("airline_sentiment").agg(
+ pl.col("airline_sentiment").count().alias("count")
+ ).sort("count", descending=True)
+
+ import pandas as pd
+ import plotly.express as px
+
+ sentiment_df = sentiment_counts.to_pandas()
+ fig = px.pie(
+ sentiment_df,
+ values="count",
+ names="airline_sentiment",
+ title="情感分布",
+ color_discrete_map={
+ "negative": "#ff6b6b",
+ "neutral": "#ffd93d",
+ "positive": "#6bcb77",
+ },
+ )
+ st.plotly_chart(fig, use_container_width=True)
+
+ # Airline distribution chart
+ st.subheader("✈️ 航空公司分布")
+ airline_counts = df.group_by("airline").agg(
+ pl.col("airline").count().alias("count")
+ ).sort("count", descending=True)
+
+ airline_df = airline_counts.to_pandas()
+ fig = px.bar(
+ airline_df,
+ x="airline",
+ y="count",
+ title="各航空公司推文数量",
+ color="count",
+ color_continuous_scale="Blues",
+ )
+ st.plotly_chart(fig, use_container_width=True)
+
+ except Exception as e:
+ st.error(f"数据加载失败: {e!s}")
+
+# Footer
+st.divider()
+st.markdown(
+ """
+
+ 航空推文情感分析 AI 助手 | 基于 VotingClassifier (LR + NB + RF + LightGBM + XGBoost)
+
+ """,
+ unsafe_allow_html=True,
+)