增加更多的历史记录，修正部份API数据获取错误，增加备用API (#5)
* 增加更多的历史记录，修正部份数据获取错误 * 增加更多的历史记录，修正部份API数据获取错误，增加备用API --------- Co-authored-by: bathfire <>
This commit is contained in:
Eikwang
+148
-47
@@ -146,10 +146,10 @@ def display_analysis_tab():
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col1, col2, col3 = st.columns([2, 2, 2])
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with col1:
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analyze_button = st.button("🚀 开始分析", type="primary", use_container_width=True)
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analyze_button = st.button("🚀 开始分析", type="primary", width='stretch')
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with col2:
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if st.button("🔄 清除结果", use_container_width=True):
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if st.button("🔄 清除结果", width='stretch'):
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if 'longhubang_result' in st.session_state:
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del st.session_state.longhubang_result
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st.success("已清除分析结果")
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@@ -335,13 +335,29 @@ def display_scoring_ranking(result):
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# 显示TOP10评分表格
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st.markdown("### 🥇 TOP10 综合评分排名")
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# 兼容历史数据与类型统一，避免 Arrow 序列化错误
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if isinstance(scoring_df, list):
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scoring_df = pd.DataFrame(scoring_df)
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numeric_cols = ['排名','综合评分','资金含金量','净买入额','卖出压力','机构共振','加分项','顶级游资','买方数','净流入']
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for col in numeric_cols:
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if col in scoring_df.columns:
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scoring_df[col] = pd.to_numeric(scoring_df[col], errors='coerce')
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text_cols = ['股票名称','股票代码','机构参与']
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for col in text_cols:
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if col in scoring_df.columns:
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scoring_df[col] = scoring_df[col].astype(str)
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top10_df = scoring_df.head(10).copy()
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if '排名' in top10_df.columns:
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top10_df['排名'] = pd.to_numeric(top10_df['排名'], errors='coerce').fillna(0).astype(int)
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# 格式化显示
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st.dataframe(
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top10_df,
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column_config={
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"排名": st.column_config.TextColumn("排名", width="small"),
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"排名": st.column_config.NumberColumn("排名", format="%d", width="small"),
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"股票名称": st.column_config.TextColumn("股票名称", width="medium"),
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"股票代码": st.column_config.TextColumn("代码", width="small"),
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"综合评分": st.column_config.NumberColumn(
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@@ -385,7 +401,7 @@ def display_scoring_ranking(result):
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"净流入": st.column_config.NumberColumn("净流入(元)", format="%.2f")
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},
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hide_index=True,
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use_container_width=True
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width='stretch'
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)
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# 一键批量分析功能
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@@ -401,12 +417,15 @@ def display_scoring_ranking(result):
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"分析数量",
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options=[3, 5, 10],
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index=0,
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help="选择分析前N只股票"
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help="选择分析前N只股票",
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key="batch_count_selector"
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)
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# 同步更新session_state中的batch_count
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st.session_state.batch_count = batch_count
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with col_batch3:
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st.write("") # 占位
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if st.button("🚀 开始批量分析", type="primary", use_container_width=True):
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if st.button("🚀 开始批量分析", type="primary", width='stretch'):
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# 提取股票代码
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stock_codes = top10_df.head(batch_count)['股票代码'].tolist()
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@@ -439,25 +458,35 @@ def display_scoring_ranking(result):
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showlegend=False,
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height=400
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)
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st.plotly_chart(fig1, use_container_width=True)
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st.plotly_chart(fig1, config={'displayModeBar': False}, use_container_width=True)
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with col2:
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# 五维评分雷达图（显示第一名）
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# 五维评分雷达图（显示批量分析数量的股票）
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if len(top10_df) > 0:
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first_place = top10_df.iloc[0]
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display_count = min(5, len(top10_df))
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fig2 = go.Figure(data=go.Scatterpolar(
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r=[
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first_place['资金含金量'] / 30 * 100,
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first_place['净买入额'] / 25 * 100,
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first_place['卖出压力'] / 20 * 100,
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first_place['机构共振'] / 15 * 100,
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first_place['加分项'] / 10 * 100
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],
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theta=['资金含金量', '净买入额', '卖出压力', '机构共振', '加分项'],
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fill='toself',
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name=first_place['股票名称']
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))
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fig2 = go.Figure()
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# 为每只股票添加雷达图
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colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7']
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for i in range(display_count):
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stock = top10_df.iloc[i]
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fig2.add_trace(go.Scatterpolar(
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r=[
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stock['资金含金量'] / 30 * 100,
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stock['净买入额'] / 25 * 100,
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stock['卖出压力'] / 20 * 100,
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stock['机构共振'] / 15 * 100,
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stock['加分项'] / 10 * 100
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],
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theta=['资金含金量', '净买入额', '卖出压力', '机构共振', '加分项'],
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fill='toself',
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name=f"{stock['股票名称']}",
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line_color=colors[i % len(colors)],
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fillcolor=colors[i % len(colors)],
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opacity=0.6
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))
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fig2.update_layout(
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polar=dict(
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@@ -467,10 +496,17 @@ def display_scoring_ranking(result):
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)
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),
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showlegend=True,
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title=f"🥇 {first_place['股票名称']} 五维评分",
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height=400
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title=f"🏆 TOP{display_count} 五维评分对比",
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height=400,
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legend=dict(
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orientation="h",
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yanchor="auto",
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y=-0.2,
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xanchor="center",
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x=0.5
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)
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)
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st.plotly_chart(fig2, use_container_width=True)
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st.plotly_chart(fig2, config={'displayModeBar': False}, use_container_width=True)
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st.markdown("---")
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@@ -480,7 +516,7 @@ def display_scoring_ranking(result):
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st.dataframe(
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scoring_df,
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column_config={
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"排名": st.column_config.TextColumn("排名", width="small"),
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"排名": st.column_config.NumberColumn("排名", format="%d", width="small"),
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"股票名称": st.column_config.TextColumn("股票名称"),
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"股票代码": st.column_config.TextColumn("代码"),
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"综合评分": st.column_config.NumberColumn("综合评分", format="%.1f"),
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@@ -490,7 +526,7 @@ def display_scoring_ranking(result):
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"净流入": st.column_config.NumberColumn("净流入(元)", format="%.2f")
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},
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hide_index=True,
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use_container_width=True
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width='stretch'
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)
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@@ -523,7 +559,7 @@ def display_recommended_stocks(result):
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"reason": st.column_config.TextColumn("推荐理由")
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},
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hide_index=True,
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use_container_width=True
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width='stretch'
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)
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# 详细推荐理由
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@@ -599,7 +635,7 @@ def display_data_details(result):
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"净流入金额": st.column_config.NumberColumn("净流入金额(元)", format="%.2f")
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},
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hide_index=True,
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use_container_width=True
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width='stretch'
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)
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# TOP股票
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@@ -616,7 +652,7 @@ def display_data_details(result):
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"net_inflow": st.column_config.NumberColumn("净流入金额(元)", format="%.2f")
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},
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hide_index=True,
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use_container_width=True
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width='stretch'
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)
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# 热门概念
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@@ -637,7 +673,7 @@ def display_data_details(result):
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"出现次数": st.column_config.NumberColumn("出现次数", format="%d")
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},
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hide_index=True,
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use_container_width=True
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width='stretch'
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)
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@@ -664,7 +700,7 @@ def display_visualizations(result):
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labels={'name': '股票名称', 'net_inflow': '净流入金额(元)'}
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)
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fig.update_layout(xaxis_tickangle=-45)
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st.plotly_chart(fig, use_container_width=True)
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st.plotly_chart(fig, config={'displayModeBar': False}, use_container_width=True)
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# 热门概念图表
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if summary.get('hot_concepts'):
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@@ -679,7 +715,7 @@ def display_visualizations(result):
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names='概念',
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title='热门概念出现次数分布'
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)
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st.plotly_chart(fig, use_container_width=True)
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st.plotly_chart(fig, config={'displayModeBar': False}, use_container_width=True)
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def display_pdf_export_section(result):
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@@ -693,7 +729,7 @@ def display_pdf_export_section(result):
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st.info("💡 点击按钮生成并下载专业的PDF分析报告")
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with col2:
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if st.button("📥 生成PDF", type="primary", use_container_width=True):
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if st.button("📥 生成PDF", type="primary", width='stretch'):
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with st.spinner("正在生成PDF报告..."):
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try:
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generator = LonghubangPDFGenerator()
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@@ -709,7 +745,7 @@ def display_pdf_export_section(result):
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data=pdf_bytes,
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file_name=f"智瞰龙虎报告_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf",
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mime="application/pdf",
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use_container_width=True
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width='stretch'
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)
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st.success("✅ PDF报告生成成功！")
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@@ -780,7 +816,7 @@ def display_history_tab():
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"hold_period": st.column_config.TextColumn("持有周期")
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},
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hide_index=True,
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use_container_width=True
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width='stretch'
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)
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st.markdown("---")
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@@ -818,6 +854,17 @@ def display_history_tab():
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st.markdown("#### 🏆 AI智能评分排名 (TOP10)")
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df_scoring = pd.DataFrame(scoring_ranking[:10])
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# 类型统一，避免Arrow序列化错误
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numeric_cols = ['排名','综合评分','资金含金量','净买入额','卖出压力','机构共振','加分项','顶级游资','买方数','净流入']
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for col in numeric_cols:
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if col in df_scoring.columns:
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df_scoring[col] = pd.to_numeric(df_scoring[col], errors='coerce')
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text_cols = ['股票名称','股票代码','机构参与']
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for col in text_cols:
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if col in df_scoring.columns:
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df_scoring[col] = df_scoring[col].astype(str)
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if '排名' in df_scoring.columns:
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df_scoring['排名'] = pd.to_numeric(df_scoring['排名'], errors='coerce').fillna(0).astype(int)
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# 显示完整的评分表格
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st.dataframe(
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@@ -867,7 +914,7 @@ def display_history_tab():
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"净流入": st.column_config.NumberColumn("净流入(元)", format="%.2f")
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},
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hide_index=True,
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use_container_width=True
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width='stretch'
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)
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# 显示评分说明
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@@ -903,34 +950,88 @@ def display_history_tab():
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st.markdown("#### 📄 原始分析内容")
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analysis_content = report_detail.get('analysis_content', '')
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if analysis_content:
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st.text_area("", value=analysis_content[:2000], height=200, disabled=True)
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st.text_area("原始分析内容", value=analysis_content[:2000], height=200, disabled=True)
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if len(analysis_content) > 2000:
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st.caption("(内容过长，仅显示前2000字符)")
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# 导出按钮
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# 操作按钮
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st.markdown("---")
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col_export1, col_export2 = st.columns(2)
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col_export1, col_export2, col_export3 = st.columns(3)
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with col_export1:
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if st.button(f"📥 导出为PDF", key=f"export_pdf_{report_id}"):
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st.info("PDF导出功能开发中...")
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with col_export2:
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if st.button(f"📋 加载到分析页", key=f"load_report_{report_id}"):
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# 使用session_state来管理按钮状态，避免需要点击两次的问题
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load_key = f"load_report_{report_id}"
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if st.button(f"📋 加载到分析页", key=load_key):
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# 将历史报告加载到当前分析结果中
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if analysis_content_parsed:
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# 重建完整的result结构
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scoring_data = analysis_content_parsed.get('scoring_ranking', [])
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if scoring_data:
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df_scoring = pd.DataFrame(scoring_data)
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# 类型统一，避免Arrow序列化错误
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numeric_cols = ['排名','综合评分','资金含金量','净买入额','卖出压力','机构共振','加分项','顶级游资','买方数','净流入']
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for col in numeric_cols:
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if col in df_scoring.columns:
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df_scoring[col] = pd.to_numeric(df_scoring[col], errors='coerce')
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text_cols = ['股票名称','股票代码','机构参与']
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for col in text_cols:
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if col in df_scoring.columns:
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df_scoring[col] = df_scoring[col].astype(str)
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if '排名' in df_scoring.columns:
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df_scoring['排名'] = pd.to_numeric(df_scoring['排名'], errors='coerce').fillna(0).astype(int)
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else:
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df_scoring = None
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loaded_result = {
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"success": True,
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"timestamp": report_detail.get('analysis_date', ''),
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"data_info": analysis_content_parsed.get('data_info', {}),
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"agents_analysis": analysis_content_parsed.get('agents_analysis', {}),
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"scoring_ranking": pd.DataFrame(analysis_content_parsed.get('scoring_ranking', [])) if analysis_content_parsed.get('scoring_ranking') else None,
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"scoring_ranking": df_scoring,
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"final_report": analysis_content_parsed.get('final_report', {}),
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"recommended_stocks": report_detail.get('recommended_stocks', [])
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}
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st.session_state.longhubang_result = loaded_result
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# 使用rerun来立即刷新页面状态
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st.success('✅ 报告已加载到分析页面，请切换到"龙虎榜分析"标签查看')
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st.rerun()
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with col_export3:
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# 删除按钮
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delete_key = f"delete_report_{report_id}"
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if st.button(f"🗑️ 删除报告", key=delete_key, type="secondary"):
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# 使用session_state来管理删除确认状态
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st.session_state[f"confirm_delete_{report_id}"] = True
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st.rerun()
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# 删除确认对话框
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if st.session_state.get(f"confirm_delete_{report_id}", False):
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st.warning(f"⚠️ 确认删除报告 #{report_id}？此操作不可撤销！")
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col_confirm1, col_confirm2 = st.columns(2)
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with col_confirm1:
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if st.button(f"✅ 确认删除", key=f"confirm_delete_yes_{report_id}", type="primary"):
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try:
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# 调用数据库删除方法 - 修复属性名
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engine.database.delete_analysis_report(report_id)
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st.success(f"✅ 报告 #{report_id} 已成功删除")
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# 清除确认状态并刷新页面
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if f"confirm_delete_{report_id}" in st.session_state:
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del st.session_state[f"confirm_delete_{report_id}"]
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st.rerun()
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except Exception as e:
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st.error(f"❌ 删除失败: {str(e)}")
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with col_confirm2:
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if st.button(f"❌ 取消", key=f"confirm_delete_no_{report_id}"):
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# 清除确认状态
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if f"confirm_delete_{report_id}" in st.session_state:
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del st.session_state[f"confirm_delete_{report_id}"]
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st.rerun()
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except Exception as e:
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st.error(f"❌ 加载历史报告失败: {str(e)}")
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@@ -986,7 +1087,7 @@ def display_statistics_tab():
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"total_net_inflow": st.column_config.NumberColumn("总净流入(元)", format="%.2f")
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},
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hide_index=True,
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use_container_width=True
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width='stretch'
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)
|
||||
|
||||
st.markdown("---")
|
||||
@@ -1006,7 +1107,7 @@ def display_statistics_tab():
|
||||
"total_net_inflow": st.column_config.NumberColumn("总净流入(元)", format="%.2f")
|
||||
},
|
||||
hide_index=True,
|
||||
use_container_width=True
|
||||
width='stretch'
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
@@ -1026,7 +1127,7 @@ def run_longhubang_batch_analysis():
|
||||
# 返回按钮
|
||||
col_back, col_clear = st.columns(2)
|
||||
with col_back:
|
||||
if st.button("🔙 返回龙虎榜分析", use_container_width=True):
|
||||
if st.button("🔙 返回龙虎榜分析", width='stretch'):
|
||||
# 清除所有批量分析相关状态
|
||||
if 'longhubang_batch_trigger' in st.session_state:
|
||||
del st.session_state.longhubang_batch_trigger
|
||||
@@ -1037,7 +1138,7 @@ def run_longhubang_batch_analysis():
|
||||
st.rerun()
|
||||
|
||||
with col_clear:
|
||||
if st.button("🔄 重新分析", use_container_width=True):
|
||||
if st.button("🔄 重新分析", width='stretch'):
|
||||
# 清除结果，保留触发标志和代码
|
||||
if 'longhubang_batch_results' in st.session_state:
|
||||
del st.session_state.longhubang_batch_results
|
||||
@@ -1098,11 +1199,11 @@ def run_longhubang_batch_analysis():
|
||||
|
||||
start_analysis = False
|
||||
with col_confirm:
|
||||
if st.button("🚀 确认开始分析", type="primary", use_container_width=True):
|
||||
if st.button("🚀 确认开始分析", type="primary", width='stretch'):
|
||||
start_analysis = True
|
||||
|
||||
with col_cancel:
|
||||
if st.button("❌ 取消", type="secondary", use_container_width=True):
|
||||
if st.button("❌ 取消", type="secondary", width='stretch'):
|
||||
# 清除所有批量分析相关状态
|
||||
if 'longhubang_batch_trigger' in st.session_state:
|
||||
del st.session_state.longhubang_batch_trigger
|
||||
|
||||
Reference in New Issue
Block a user