351 lines
13 KiB
Python
351 lines
13 KiB
Python
"""Streamlit 演示应用 - 推文情感分析
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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。
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"""
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import os
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import sys
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import streamlit as st
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from dotenv import load_dotenv
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from src.tweet_agent import TweetSentimentAgent, analyze_tweet
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# Load env variables
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load_dotenv()
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st.set_page_config(page_title="航空推文情感分析", page_icon="✈️", layout="wide")
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# Sidebar Configuration
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st.sidebar.header("🔧 配置")
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st.sidebar.markdown("### 模型信息")
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st.sidebar.info(
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"""
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**模型**: VotingClassifier (5个基学习器)
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- Logistic Regression
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- Multinomial Naive Bayes
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- Random Forest
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- LightGBM
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- XGBoost
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**性能**: Macro-F1 = 0.7533 ✅
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"""
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)
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st.sidebar.markdown("---")
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# Mode Selection
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mode = st.sidebar.radio("功能选择", ["📝 单条分析", "📊 批量分析", "📈 数据概览"])
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# Initialize session state
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if "agent" not in st.session_state:
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with st.spinner("🔄 加载模型..."):
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st.session_state.agent = TweetSentimentAgent()
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if "batch_results" not in st.session_state:
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st.session_state.batch_results = []
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# --- Helper Functions ---
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def get_sentiment_emoji(sentiment: str) -> str:
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"""获取情感对应的表情符号"""
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emoji_map = {
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"negative": "😠",
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"neutral": "😐",
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"positive": "😊",
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}
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return emoji_map.get(sentiment, "❓")
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def get_sentiment_color(sentiment: str) -> str:
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"""获取情感对应的颜色"""
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color_map = {
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"negative": "#ff6b6b",
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"neutral": "#ffd93d",
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"positive": "#6bcb77",
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}
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return color_map.get(sentiment, "#e0e0e0")
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def get_priority_color(priority: str) -> str:
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"""获取优先级对应的颜色"""
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color_map = {
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"high": "#ff4757",
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"medium": "#ffa502",
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"low": "#2ed573",
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}
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return color_map.get(priority, "#e0e0e0")
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# --- Main Views ---
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if mode == "📝 单条分析":
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st.title("✈️ 航空推文情感分析")
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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。")
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# Input form
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with st.form("tweet_analysis_form"):
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col1, col2 = st.columns([3, 1])
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with col1:
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tweet_text = st.text_area(
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"推文内容",
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placeholder="@United This is the worst airline ever! My flight was delayed for 5 hours...",
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height=100,
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)
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with col2:
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airline = st.selectbox(
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"航空公司",
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["United", "US Airways", "American", "Southwest", "Delta", "Virgin America"],
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)
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submitted = st.form_submit_button("🔍 分析", type="primary")
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if submitted and tweet_text:
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with st.spinner("🤖 AI 正在分析..."):
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try:
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result = analyze_tweet(tweet_text, airline)
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# Display results
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st.divider()
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# Header with sentiment
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sentiment_emoji = get_sentiment_emoji(result.classification.sentiment)
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sentiment_color = get_sentiment_color(result.classification.sentiment)
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st.markdown(
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f"""
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<div style="background-color: {sentiment_color}; padding: 20px; border-radius: 10px; text-align: center;">
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<h1 style="color: white; margin: 0;">{sentiment_emoji} {result.classification.sentiment.upper()}</h1>
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<p style="color: white; margin: 10px 0 0 0;">置信度: {result.classification.confidence:.1%}</p>
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</div>
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""",
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unsafe_allow_html=True,
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)
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st.divider()
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# Original tweet
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st.subheader("📝 原始推文")
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st.info(f"**航空公司**: {result.airline}\n\n**内容**: {result.tweet_text}")
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# Explanation
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st.subheader("🔍 情感解释")
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st.markdown("**关键因素:**")
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for factor in result.explanation.key_factors:
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st.write(f"- {factor}")
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st.markdown("**推理过程:**")
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st.write(result.explanation.reasoning)
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# Disposal plan
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st.subheader("📋 处置方案")
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priority_color = get_priority_color(result.disposal_plan.priority)
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st.markdown(
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f"""
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<div style="background-color: {priority_color}; padding: 10px; border-radius: 5px; display: inline-block;">
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<span style="color: white; font-weight: bold;">优先级: {result.disposal_plan.priority.upper()}</span>
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</div>
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<br><br>
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**行动类型**: {result.disposal_plan.action_type}
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""",
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unsafe_allow_html=True,
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)
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if result.disposal_plan.suggested_response:
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st.markdown("**建议回复:**")
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st.success(result.disposal_plan.suggested_response)
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st.markdown("**后续行动:**")
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for action in result.disposal_plan.follow_up_actions:
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st.write(f"- {action}")
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except Exception as e:
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st.error(f"分析失败: {e!s}")
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elif mode == "📊 批量分析":
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st.title("📊 批量推文分析")
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st.markdown("上传 CSV 文件或输入多条推文，进行批量情感分析。")
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# Input method selection
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input_method = st.radio("输入方式", ["手动输入", "CSV 上传"], horizontal=True)
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if input_method == "手动输入":
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st.markdown("### 输入推文（每行一条）")
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tweets_input = st.text_area(
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"推文列表",
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placeholder="@United Flight delayed again!\n@Southwest Great service!\n@American Baggage policy?",
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height=200,
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)
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if st.button("🔍 批量分析", type="primary") and tweets_input:
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lines = [line.strip() for line in tweets_input.split("\n") if line.strip()]
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if lines:
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with st.spinner("🤖 AI 正在分析..."):
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results = []
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for line in lines:
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try:
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# Extract airline from tweet (simple heuristic)
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airline = "United" # Default
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for a in ["United", "US Airways", "American", "Southwest", "Delta", "Virgin America"]:
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if a.lower() in line.lower():
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airline = a
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break
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result = analyze_tweet(line, airline)
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results.append(result)
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except Exception as e:
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st.warning(f"分析失败: {line[:50]}... - {e}")
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if results:
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st.session_state.batch_results = results
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st.success(f"✅ 成功分析 {len(results)} 条推文")
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else: # CSV upload
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st.markdown("### 上传 CSV 文件")
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st.info("CSV 文件应包含以下列: `text` (推文内容), `airline` (航空公司)")
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uploaded_file = st.file_uploader("选择文件", type=["csv"])
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if uploaded_file and st.button("🔍 分析上传文件", type="primary"):
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import pandas as pd
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try:
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df = pd.read_csv(uploaded_file)
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if "text" not in df.columns:
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st.error("CSV 文件必须包含 'text' 列")
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else:
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with st.spinner("🤖 AI 正在分析..."):
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results = []
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for _, row in df.iterrows():
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try:
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text = row["text"]
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airline = row.get("airline", "United")
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result = analyze_tweet(text, airline)
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results.append(result)
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except Exception as e:
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st.warning(f"分析失败: {text[:50]}... - {e}")
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if results:
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st.session_state.batch_results = results
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st.success(f"✅ 成功分析 {len(results)} 条推文")
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except Exception as e:
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st.error(f"文件读取失败: {e!s}")
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# Display batch results
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if st.session_state.batch_results:
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st.divider()
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st.subheader(f"📊 分析结果 ({len(st.session_state.batch_results)} 条)")
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# Summary statistics
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sentiments = [r.classification.sentiment for r in st.session_state.batch_results]
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negative_count = sentiments.count("negative")
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neutral_count = sentiments.count("neutral")
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positive_count = sentiments.count("positive")
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col1, col2, col3 = st.columns(3)
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col1.metric("😠 负面", negative_count)
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col2.metric("😐 中性", neutral_count)
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col3.metric("😊 正面", positive_count)
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# Detailed results table
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st.markdown("### 详细结果")
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results_data = []
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for r in st.session_state.batch_results:
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results_data.append({
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"推文": r.tweet_text[:50] + "..." if len(r.tweet_text) > 50 else r.tweet_text,
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"航空公司": r.airline,
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"情感": f"{get_sentiment_emoji(r.classification.sentiment)} {r.classification.sentiment}",
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"置信度": f"{r.classification.confidence:.1%}",
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"优先级": r.disposal_plan.priority.upper(),
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"行动类型": r.disposal_plan.action_type,
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})
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st.dataframe(results_data, use_container_width=True)
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# Clear button
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if st.button("🗑️ 清除结果"):
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st.session_state.batch_results = []
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st.rerun()
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elif mode == "📈 数据概览":
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st.title("📈 数据集概览")
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st.markdown("查看训练数据集的统计信息。")
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try:
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import polars as pl
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from src.tweet_data import load_cleaned_tweets, print_data_summary
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df = load_cleaned_tweets("data/Tweets_cleaned.csv")
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# Display summary
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st.subheader("📊 数据统计")
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print_data_summary(df, "数据集统计")
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# Display sample data
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st.subheader("📝 样本数据")
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sample_df = df.head(10).to_pandas()
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st.dataframe(sample_df, use_container_width=True)
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# Sentiment distribution chart
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st.subheader("📈 情感分布")
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sentiment_counts = df.group_by("airline_sentiment").agg(
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pl.col("airline_sentiment").count().alias("count")
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).sort("count", descending=True)
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import pandas as pd
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import plotly.express as px
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sentiment_df = sentiment_counts.to_pandas()
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fig = px.pie(
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sentiment_df,
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values="count",
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names="airline_sentiment",
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title="情感分布",
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color_discrete_map={
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"negative": "#ff6b6b",
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"neutral": "#ffd93d",
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"positive": "#6bcb77",
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},
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)
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st.plotly_chart(fig, use_container_width=True)
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# Airline distribution chart
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st.subheader("✈️ 航空公司分布")
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airline_counts = df.group_by("airline").agg(
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pl.col("airline").count().alias("count")
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).sort("count", descending=True)
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airline_df = airline_counts.to_pandas()
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fig = px.bar(
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airline_df,
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x="airline",
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y="count",
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title="各航空公司推文数量",
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color="count",
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color_continuous_scale="Blues",
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)
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st.plotly_chart(fig, use_container_width=True)
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except Exception as e:
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st.error(f"数据加载失败: {e!s}")
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# Footer
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st.divider()
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st.markdown(
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"""
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<div style="text-align: center; color: gray; font-size: 12px;">
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航空推文情感分析 AI 助手 | 基于 VotingClassifier (LR + NB + RF + LightGBM + XGBoost)
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</div>
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""",
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unsafe_allow_html=True,
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)
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