app.py

import streamlit as st
import pandas as pd
from transformers import pipeline
import tempfile
import os
from typing import List, Dict
import matplotlib.pyplot as plt

@st.cache_resource
def load_model():
    """Load and cache the sentiment analysis model"""
    try:
        return pipeline(
            "text-classification",
            model="KeonBlackwell/movie_sentiment_model",
            tokenizer="distilbert-base-uncased"
        )
    except Exception as e:
        st.error(f"模型加载失败: {str(e)}")
        return None

def analyze_comments(comments: List[str], classifier) -> List[Dict]:
    """Analyze a list of comments and return sentiment results"""
    results = []
    for comment in comments:
        prediction = classifier(comment)[0]
        results.append({
            'comment': comment,
            'sentiment': 1 if prediction['label'] == 'LABEL_1' else 0,
            'confidence': prediction['score']
        })
    return results

def calculate_star_rating(positive_percent: float) -> int:
    """Convert positive percentage to star rating (1-5)"""
    if positive_percent >= 80:
        return 5
    elif positive_percent >= 60:
        return 4
    elif positive_percent >= 40:
        return 3
    elif positive_percent >= 20:
        return 2
    return 1

def show_sentiment_distribution(positive_percent: float):
    """Display a pie chart of sentiment distribution"""
    fig, ax = plt.subplots()
    ax.pie([positive_percent, 100-positive_percent],
           labels=['Positive', 'Negative'],
           autopct='%1.1f%%',
           colors=['#4CAF50', '#F44336'])
    ax.axis('equal')  # Equal aspect ratio ensures pie is drawn as a circle
    st.pyplot(fig)

def main():
    st.set_page_config(page_title="电影评论分析系统", page_icon="🎬")
    
    # Custom CSS
    st.markdown("""
    <style>
    .reportview-container {
        background: #f0f2f6;
    }
    .stProgress > div > div > div > div {
        background-color: #4CAF50;
    }
    </style>
    """, unsafe_allow_html=True)

    # Load model
    classifier = load_model()
    if classifier is None:
        return

    # Page layout
    st.title("🎬 电影评论批量分析系统")
    st.markdown("""
    ### 使用说明：
    1. 上传包含电影评论的CSV文件（需包含'comment'列）
    2. 系统自动分析每条评论的情感倾向
    3. 生成整体评分和分析报告
    """)

    # Sample file download
    with st.expander("下载示例文件"):
        sample_data = pd.DataFrame({'comment': [
            "This movie was fantastic! The acting was superb.",
            "I didn't like the plot. It was too predictable.",
            "The cinematography was beautiful but the story was weak."
        ]})
        st.download_button(
            label="下载示例CSV",
            data=sample_data.to_csv(index=False).encode('utf-8'),
            file_name="sample_reviews.csv",
            mime="text/csv"
        )

    # File upload
    uploaded_file = st.file_uploader("上传CSV文件", type=["csv"])
    
    if uploaded_file is not None:
        try:
            df = pd.read_csv(uploaded_file)
            if 'comment' not in df.columns:
                st.error("CSV文件必须包含'comment'列")
                return
                
            comments = df['comment'].dropna().tolist()
            
            with st.expander("原始数据预览（前5行）"):
                st.dataframe(df.head())

            if st.button("开始分析", type="primary"):
                if len(comments) > 1000:
                    st.warning(f"检测到大量评论 ({len(comments)} 条)，分析可能需要较长时间...")
                
                with st.spinner("分析中，请稍候..."):
                    results = analyze_comments(comments, classifier)
                    result_df = pd.DataFrame(results)
                    
                    # Calculate statistics
                    positive_count = result_df['sentiment'].sum()
                    total_reviews = len(result_df)
                    positive_percent = (positive_count / total_reviews) * 100
                    star_rating = calculate_star_rating(positive_percent)
                    
                    # Display results
                    st.success("分析完成！")
                    
                    # Metrics
                    col1, col2, col3 = st.columns(3)
                    with col1:
                        st.metric("⭐ 综合评分", f"{star_rating} 星")
                    with col2:
                        st.metric("👍 正面评价", f"{positive_count}/{total_reviews}")
                    with col3:
                        st.metric("📈 正面比例", f"{positive_percent:.1f}%")
                    
                    # Visualizations
                    show_sentiment_distribution(positive_percent)
                    
                    # Sample results
                    with st.expander("查看详细分析结果（前10条）"):
                        st.dataframe(result_df.head(10))
                    
                    # Download
                    with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as tmp:
                        result_df.to_csv(tmp.name, index=False)
                        with open(tmp.name, "rb") as f:
                            st.download_button(
                                label="下载完整结果",
                                data=f,
                                file_name="analysis_results.csv",
                                mime="text/csv"
                            )
                    os.unlink(tmp.name)
                    
        except Exception as e:
            st.error(f"处理文件时出错: {str(e)}")

if __name__ == "__main__":
    main()
Key improvements made:

Added model caching

Included a sample file download

Added a pie chart visualization

Better organized the code into functions

Added type hints

Improved error handling and user feedback

Added warning for large files

Would you like me to explain any specific part of these improvements in more detail?

我不需要优化，在源代码上添加以下几步，合并CSV里的每行评论，合并成一个文本，利用huggingface上的模型keyphrase_extractor = pipeline(
    "token-classification",
    model="ml6team/keyphrase-extraction-distilbert-inspec",
    aggregation_strategy="simple"  # 用于合并子词/标记
和summarizer = pipeline("summarization",
            model="facebook/bart-large-cnn")
进行提取5个关键词，和生成总结，我是在huggingface上设计app
我将在你的原始代码基础上添加关键词提取和总结生成功能。以下是修改后的代码，保留了原有功能，同时增加了你要求的新特性：

python
import streamlit as st
import pandas as pd
from transformers import pipeline
import tempfile
import os

def calculate_star_rating(positive_percent):
    """将正面评价百分比转换为星级评分"""
    if positive_percent >= 80:
        return 5
    elif positive_percent >= 60:
        return 4
    elif positive_percent >= 40:
        return 3
    elif positive_percent >= 20:
        return 2
    else:
        return 1

def main():
    st.set_page_config(page_title="电影评论分析系统", page_icon="🎬")
    
    # 自定义样式
    st.markdown("""
    <style>
    .reportview-container {
        background: #f0f2f6;
    }
    .stProgress > div > div > div > div {
        background-color: #4CAF50;
    }
    </style>
    """, unsafe_allow_html=True)

    # 模型加载
    with st.spinner("正在加载所有模型，这可能需要几分钟..."):
        try:
            # 情感分析模型
            classifier = pipeline(
                "text-classification",
                model="KeonBlackwell/movie_sentiment_model",
                tokenizer="distilbert-base-uncased"
            )
            
            # 关键词提取模型
            keyphrase_extractor = pipeline(
                "token-classification",
                model="ml6team/keyphrase-extraction-distilbert-inspec",
                aggregation_strategy="simple"
            )
            
            # 摘要生成模型
            summarizer = pipeline("summarization",
                                model="facebook/bart-large-cnn")
            
        except Exception as e:
            st.error(f"模型加载失败: {str(e)}")
            return

    # 页面布局
    st.title("🎬 电影评论批量分析系统")
    st.markdown("""
    ### 使用说明：
    1. 上传包含电影评论的CSV文件（需包含'comment'列）
    2. 系统自动分析每条评论的情感倾向
    3. 生成整体评分、关键词提取和总结报告
    """)

    # 文件上传
    uploaded_file = st.file_uploader("上传CSV文件", type=["csv"])
    
    if uploaded_file is not None:
        # 读取数据
        try:
            df = pd.read_csv(uploaded_file)
            if 'comment' not in df.columns:
                st.error("CSV文件必须包含'comment'列")
                return
                
            comments = df['comment'].tolist()
        except Exception as e:
            st.error(f"文件读取失败: {str(e)}")
            return

        # 显示预览
        with st.expander("原始数据预览（前5行）"):
            st.dataframe(df.head())

        if st.button("开始分析"):
            # 进度条设置
            progress_bar = st.progress(0)
            status_text = st.empty()
            
            results = []
            total = len(comments)
            
            # 批量预测
            try:
                # 情感分析
                for i, comment in enumerate(comments):
                    progress = (i+1)/total
                    progress_bar.progress(progress)
                    status_text.text(f"正在分析情感 {i+1}/{total} 条评论...")
                    
                    prediction = classifier(comment)[0]
                    results.append({
                        'comment': comment,
                        'sentiment': 1 if prediction['label'] == 'LABEL_1' else 0,
                        'confidence': prediction['score']
                    })
                
                # 转换为DataFrame
                result_df = pd.DataFrame(results)
                
                # 计算统计指标
                positive_count = result_df['sentiment'].sum()
                total_reviews = len(result_df)
                positive_percent = (positive_count / total_reviews) * 100
                star_rating = calculate_star_rating(positive_percent)
                
                # 显示结果
                st.success("情感分析完成！")
                
                # 评分展示
                col1, col2, col3 = st.columns(3)
                with col1:
                    st.metric("⭐ 综合评分", f"{star_rating} 星")
                with col2:
                    st.metric("👍 正面评价", f"{positive_count}/{total_reviews}")
                with col3:
                    st.metric("📈 正面比例", f"{positive_percent:.1f}%")
                
                # 进度条可视化
                st.progress(positive_percent/100)
                
                # 显示示例结果
                with st.expander("查看详细分析结果（前10条）"):
                    st.dataframe(result_df.head(10))
                
                # 关键词提取和总结
                st.subheader("📌 评论关键词提取与总结")
                
                # 合并所有评论为一个文本
                combined_text = " ".join(comments)
                
                # 关键词提取
                with st.spinner("正在提取关键词..."):
                    keyphrases = keyphrase_extractor(combined_text)
                    # 按置信度排序并取前5个
                    top_keyphrases = sorted(keyphrases, key=lambda x: x['score'], reverse=True)[:5]
                
                # 显示关键词
                st.markdown("**🔍 提取的关键词:**")
                cols = st.columns(5)
                for i, phrase in enumerate(top_keyphrases):
                    cols[i].markdown(f"""
                    <div style="
                        border: 1px solid #ddd;
                        border-radius: 5px;
                        padding: 10px;
                        text-align: center;
                        margin: 5px;
                        background-color: #f9f9f9;
                    ">
                        <b>{phrase['word']}</b><br>
                        <small>置信度: {phrase['score']:.2f}</small>
                    </div>
                    """, unsafe_allow_html=True)
                
                # 生成总结
                with st.spinner("正在生成评论总结..."):
                    # 限制文本长度以避免模型限制
                    max_length = 1024  # 模型的最大输入长度
                    if len(combined_text) > max_length:
                        combined_text = combined_text[:max_length]
                    
                    summary = summarizer(combined_text, 
                                        max_length=130, 
                                        min_length=30, 
                                        do_sample=False)
                
                # 显示总结
                st.markdown("**📝 评论总结:**")
                st.info(summary[0]['summary_text'])
                
                # 生成可下载文件
                with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as tmp:
                    result_df.to_csv(tmp.name, index=False)
                    with open(tmp.name, "rb") as f:
                        st.download_button(
                            label="下载完整结果",
                            data=f,
                            file_name="analysis_results.csv",
                            mime="text/csv"
                        )
                os.unlink(tmp.name)
                
            except Exception as e:
                st.error(f"分析过程中出现错误: {str(e)}")
            finally:
                progress_bar.empty()
                status_text.empty()

if __name__ == "__main__":
    main()