上传文件至 src

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"""
+                    <div style="background-color: {sentiment_color}; padding: 20px; border-radius: 10px; text-align: center;">
+                        <h1 style="color: white; margin: 0;">{sentiment_emoji} {result.classification.sentiment.upper()}</h1>
+                        <p style="color: white; margin: 10px 0 0 0;">置信度: {result.classification.confidence:.1%}</p>
+                    </div>
+                    """,
+                    unsafe_allow_html=True,
+                )
+                
+                st.divider()
+                
+                # Original tweet
+                st.subheader("📝 原始推文")
+                st.info(f"**航空公司**: {result.airline}\n\n**内容**: {result.tweet_text}")
+                
+                # Explanation
+                st.subheader("🔍 情感解释")
+                st.markdown("**关键因素:**")
+                for factor in result.explanation.key_factors:
+                    st.write(f"- {factor}")
+                
+                st.markdown("**推理过程:**")
+                st.write(result.explanation.reasoning)
+                
+                # Disposal plan
+                st.subheader("📋 处置方案")
+                
+                priority_color = get_priority_color(result.disposal_plan.priority)
+                st.markdown(
+                    f"""
+                    <div style="background-color: {priority_color}; padding: 10px; border-radius: 5px; display: inline-block;">
+                        <span style="color: white; font-weight: bold;">优先级: {result.disposal_plan.priority.upper()}</span>
+                    </div>
+                    <br><br>
+                    **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(
+    """
+    <div style="text-align: center; color: gray; font-size: 12px;">
+        航空推文情感分析 AI 助手 | 基于 VotingClassifier (LR + NB + RF + LightGBM + XGBoost)
+    </div>
+    """,
+    unsafe_allow_html=True,
+)