PDF转交互式仪表盘提示词

""" Interactive Dashboard for Key Figures Evolution Author: Data Visualization Specialist Description: A modern, interactive dashboard built with Plotly Dash to visualize key financial and operational metrics from PDF documents, featuring dynamic filters, hover effects, and comparative analysis. """ import dash from dash import dcc, html, Input, Output, callback import plotly.graph_objs as go import plotly.express as px import pandas as pd import numpy as np from datetime import datetime # ============================================================================ # DATA PREPARATION SECTION # ============================================================================ def generate_sample_data(): """ Generate sample data representing key figures typically found in financial/operational PDF documents. In production, this would be replaced with actual PDF parsing and data extraction. """ years = list(range(2018, 2024)) # Revenue data (in millions) revenue = [120, 145, 168, 192, 215, 238] # Operating costs operating_costs = [85, 98, 112, 125, 138, 150] # Net profit net_profit = [35, 47, 56, 67, 77, 88] # Customer acquisition new_customers = [1500, 1800, 2100, 2500, 2900, 3300] # Employee count employees = [45, 52, 60, 68, 75, 82] # Market share (%) market_share = [12.5, 14.2, 15.8, 17.3, 18.9, 20.4] # Customer satisfaction (0-100) satisfaction = [78, 81, 83, 85, 87, 89] # Create DataFrame data = { 'Year': years, 'Revenue': revenue, 'Operating_Costs': operating_costs, 'Net_Profit': net_profit, 'New_Customers': new_customers, 'Employees': employees, 'Market_Share': market_share, 'Satisfaction': satisfaction } df = pd.DataFrame(data) # Calculate derived metrics df['Profit_Margin'] = (df['Net_Profit'] / df['Revenue']) * 100 df['Revenue_Growth'] = df['Revenue'].pct_change() * 100 df['Cost_Ratio'] = (df['Operating_Costs'] / df['Revenue']) * 100 return df # Generate the dataset df = generate_sample_data() # ============================================================================ # DASHBOARD INITIALIZATION # ============================================================================ # Initialize the Dash app app = dash.Dash(__name__) app.title = "Key Figures Dashboard" # Define color scheme for modern look COLORS = { 'background': '#1e1e2e', 'card': '#2d2d44', 'text': '#cdd6f4', 'accent': '#89b4fa', 'accent2': '#f38ba8', 'accent3': '#a6e3a1', 'accent4': '#fab387' } # ============================================================================ # LAYOUT SECTION # ============================================================================ app.layout = html.Div( style={ 'backgroundColor': COLORS['background'], 'padding': '20px', 'fontFamily': 'Inter, system-ui, -apple-system, sans-serif' }, children=[ # Header Section html.Div([ html.H1( "Key Figures Dashboard", style={ 'color': COLORS['text'], 'textAlign': 'center', 'marginBottom': '30px', 'fontWeight': '600' } ), html.P( "Interactive visualization of organizational performance metrics", style={ 'color': COLORS['text'], 'textAlign': 'center', 'opacity': '0.8', 'marginBottom': '30px' } ), ]), # Filters Section html.Div([ html.Label( "Select Year Range:", style={'color': COLORS['text'], 'marginRight': '15px'} ), dcc.RangeSlider( id='year-filter', min=df['Year'].min(), max=df['Year'].max(), value=[df['Year'].min(), df['Year'].max()], marks={year: str(year) for year in df['Year']}, step=1, tooltip={"placement": "bottom", "always_visible": True}, className="range-slider" ), ], style={ 'backgroundColor': COLORS['card'], 'padding': '20px', 'borderRadius': '10px', 'marginBottom': '30px' }), # Charts Container html.Div(id='charts-container', children=[ # Chart 1: Revenue and Profit Evolution html.Div([ dcc.Graph(id='revenue-profit-chart') ], style={'marginBottom': '30px'}), # Chart 2: Cost Analysis html.Div([ dcc.Graph(id='cost-analysis-chart') ], style={'marginBottom': '30px'}), # Chart 3: Customer Metrics html.Div([ dcc.Graph(id='customer-metrics-chart') ], style={'marginBottom': '30px'}), # Chart 4: Operational Metrics html.Div([ dcc.Graph(id='operational-metrics-chart') ], style={'marginBottom': '30px'}), # Chart 5: Efficiency Metrics html.Div([ dcc.Graph(id='efficiency-metrics-chart') ], style={'marginBottom': '30px'}), ]), # Footer html.Div( "Data source: Financial Reports 2018-2023", style={ 'color': COLORS['text'], 'textAlign': 'center', 'opacity': '0.6', 'marginTop': '20px' } ) ] ) # ============================================================================ # CALLBACKS AND CHART CREATION # ============================================================================ @callback( [Output('revenue-profit-chart', 'figure'), Output('cost-analysis-chart', 'figure'), Output('customer-metrics-chart', 'figure'), Output('operational-metrics-chart', 'figure'), Output('efficiency-metrics-chart', 'figure')], [Input('year-filter', 'value')] ) def update_charts(year_range): """ Update all charts based on selected year range. Args: year_range: List containing [start_year, end_year] Returns: List of figure objects for each chart """ # Filter data based on selected years filtered_df = df[(df['Year'] >= year_range[0]) & (df['Year'] <= year_range[1])] # Chart 1: Revenue and Profit Evolution fig1 = go.Figure() # Revenue line fig1.add_trace(go.Scatter( x=filtered_df['Year'], y=filtered_df['Revenue'], name='Revenue', mode='lines+markers', line=dict(color=COLORS['accent'], width=3), marker=dict(size=8, color=COLORS['accent']), hovertemplate='Year: %{x}<br>Revenue: $%{y:.1f}M<extra></extra>' )) # Net Profit line fig1.add_trace(go.Scatter( x=filtered_df['Year'], y=filtered_df['Net_Profit'], name='Net Profit', mode='lines+markers', line=dict(color=COLORS['accent2'], width=3), marker=dict(size=8, color=COLORS['accent2']), hovertemplate='Year: %{x}<br>Net Profit: $%{y:.1f}M<extra></extra>' )) fig1.update_layout( title="Revenue and Net Profit Evolution", xaxis_title="Year", yaxis_title="Amount (Millions $)", template='plotly_dark', paper_bgcolor=COLORS['card'], plot_bgcolor=COLORS['card'], font=dict(color=COLORS['text']), hovermode='x unified', legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1) ) # Chart 2: Cost Analysis fig2 = go.Figure() # Operating costs bar fig2.add_trace(go.Bar( x=filtered_df['Year'], y=filtered_df['Operating_Costs'], name='Operating Costs', marker_color=COLORS['accent3'], hovertemplate='Year: %{x}<br>Operating Costs: $%{y:.1f}M<extra></extra>' )) # Cost ratio line fig2.add_trace(go.Scatter( x=filtered_df['Year'], y=filtered_df['Cost_Ratio'], name='Cost Ratio (%)', mode='lines+markers', yaxis='y2', line=dict(color=COLORS['accent4'], width=3), marker=dict(size=8, color=COLORS['accent4']), hovertemplate='Year: %{x}<br>Cost Ratio: %{y:.1f}%<extra></extra>' )) fig2.update_layout( title="Operating Costs and Cost Ratio Analysis", xaxis_title="Year", yaxis_title="Amount (Millions $)", yaxis2=dict( title="Cost Ratio (%)", overlaying='y', side='right', showgrid=False ), template='plotly_dark', paper_bgcolor=COLORS['card'], plot_bgcolor=COLORS['card'], font=dict(color=COLORS['text']), hovermode='x unified', legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1) ) # Chart 3: Customer Metrics fig3 = go.Figure() # New customers bar fig3.add_trace(go.Bar( x=filtered_df['Year'], y=filtered_df['New_Customers'], name='New Customers', marker_color=COLORS['accent'], hovertemplate='Year: %{x}<br>New Customers: %{y:,}<extra></extra>' )) # Customer satisfaction line fig3.add_trace(go.Scatter( x=filtered_df['Year'], y=filtered_df['Satisfaction'], name='Customer Satisfaction', mode='lines+markers', yaxis='y2', line=dict(color=COLORS['accent2'], width=3), marker=dict(size=8, color=COLORS['accent2']), hovertemplate='Year: %{x}<br>Satisfaction Score: %{y:.1f}/100<extra></extra>' )) fig3.update_layout( title="Customer Acquisition and Satisfaction", xaxis_title="Year", yaxis_title="New Customers", yaxis2=dict( title="Satisfaction Score", overlaying='y', side='right', range=[0, 100], showgrid=False ), template='plotly_dark', paper_bgcolor=COLORS['card'], plot_bgcolor=COLORS['card'], font=dict(color=COLORS['text']), hovermode='x unified', legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1) ) # Chart 4: Operational Metrics fig4 = go.Figure() # Employees area chart fig4.add_trace(go.Scatter( x=filtered_df['Year'], y=filtered_df['Employees'], name='Employees', mode='lines+markers', fill='tozeroy', line=dict(color=COLORS['accent3'], width=3), marker=dict(size=8, color=COLORS['accent3']), hovertemplate='Year: %{x}<br>Employees: %{y}<extra></extra>' )) # Market share line fig4.add_trace(go.Scatter( x=filtered_df['Year'], y=filtered_df['Market_Share'], name='Market Share (%)', mode='lines+markers', yaxis='y2', line=dict(color=COLORS['accent4'], width=3, dash='dash'), marker=dict(size=8, color=COLORS['accent4']), hovertemplate='Year: %{x}<br>Market Share: %{y:.1f}%<extra></extra>' )) fig4.update_layout( title="Workforce and Market Presence", xaxis_title="Year", yaxis_title="Number of Employees", yaxis2=dict( title="Market Share (%)", overlaying='y', side='right', showgrid=False ), template='plotly_dark', paper_bgcolor=COLORS['card'], plot_bgcolor=COLORS['card'], font=dict(color=COLORS['text']), hovermode='x unified', legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1) ) # Chart 5: Efficiency Metrics fig5 = go.Figure() # Profit margin fig5.add_trace(go.Bar( x=filtered_df['Year'], y=filtered_df['Profit_Margin'], name='Profit Margin (%)', marker_color=COLORS['accent'], hovertemplate='Year: %{x}<br>Profit Margin: %{y:.1f}%<extra></extra>' )) # Revenue growth fig5.add_trace(go.Scatter( x=filtered_df['Year'], y=filtered_df['Revenue_Growth'], name='Revenue Growth (%)', mode='lines+markers', yaxis='y2', line=dict(color=COLORS['accent2'], width=3), marker=dict(size=8, color=COLORS['accent2']), hovertemplate='Year: %{x}<br>Revenue Growth: %{y:.1f}%<extra></extra>' )) fig5.update_layout( title="Profitability and Growth Metrics", xaxis_title="Year", yaxis_title="Profit Margin (%)", yaxis2=dict( title="Revenue Growth (%)", overlaying='y', side='right', showgrid=False ), template='plotly_dark', paper_bgcolor=COLORS['card'], plot_bgcolor=COLORS['card'], font=dict(color=COLORS['text']), hovermode='x unified', legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1) ) return fig1, fig2, fig3, fig4, fig5 # ============================================================================ # MAIN EXECUTION # ============================================================================ if __name__ == '__main__': # Run the app - compatible with Replit environment app.run_server( host='0.0.0.0', # Allows access from any network interface port=8050, # Default Plotly Dash port debug=True # Enable debug mode for development )