Update app.py

app.py

@@ -1,20 +1,396 @@
 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():
-    sentiment_pipeline = pipeline(model="isom5240/2025SpringL2")
+    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
+    """)
 
-    st.title("Sentiment Analysis with HuggingFace Spaces")
-    st.write("Enter a sentence to analyze its sentiment:")
+    # 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
 
-    user_input = st.text_input("")
-    if user_input:
-        result = sentiment_pipeline(user_input)
-        sentiment = result[0]["label"]
-        confidence = result[0]["score"]
+        # 显示预览
+        with st.expander("原始数据预览（前5行）"):
+            st.dataframe(df.head())
 
-        st.write(f"Sentiment: {sentiment}")
-        st.write(f"Confidence: {confidence:.2f}")
+        if st.button("开始分���"):
+            # 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()