diff --git a/src/streamlit_tweet_app.py b/src/streamlit_tweet_app.py
deleted file mode 100644
index aa4ecdf..0000000
--- a/src/streamlit_tweet_app.py
+++ /dev/null
@@ -1,353 +0,0 @@
-"""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,
-)