From 35a84b9ce1fb4b8d9563444209ad9347d7fd0864 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E5=88=99=E6=96=87?= Date: Thu, 15 Jan 2026 17:42:31 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=A0=E9=99=A4=20src/streamlit=5Ftweet=5Fap?= =?UTF-8?q?p.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/streamlit_tweet_app.py | 353 ------------------------------------- 1 file changed, 353 deletions(-) delete mode 100644 src/streamlit_tweet_app.py 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%}

-
- """, - 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""" -
- 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, -)