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requirements.txt.txt

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+pandas
+streamlit
+plotly

visualization_tool.py

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+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import numpy as np
+
+def rgb_to_hex(r, g, b):
+    return f'#{r:02X}{g:02X}{b:02X}'
+
+def interpolate_color(start_color, end_color, factor):
+    r1, g1, b1 = start_color
+    r2, g2, b2 = end_color
+    r = int(r1 + (r2 - r1) * factor)
+    g = int(g1 + (g2 - g1) * factor)
+    b = int(b1 + (b2 - b1) * factor)
+    return rgb_to_hex(r, g, b)
+
+def get_warm_to_cold_colors(n):
+    hot = (124, 21, 77)  # Hot color RGB
+    medium = (0, 66, 76)  # Medium color RGB
+    cold = (197, 246, 235)  # Cold color RGB
+    
+    colors = []
+    for i in range(n):
+        if i < n // 2:
+            factor = i / (n // 2)
+            color = interpolate_color(hot, medium, factor)
+        else:
+            factor = (i - n // 2) / (n // 2)
+            color = interpolate_color(medium, cold, factor)
+        colors.append(color)
+    
+    return colors
+
+PREDEFINED_COLORS = {
+    'SC_Q_Origin': '#A7BCC6', #specified random color
+    'SC_Q_H_state':'#A7BCC6', #specified random color
+    'SC_Q_H_scope': '#A7BCC6', #specified random color
+    'H_sector': '#1A636B',
+    'SC_Q_H_sector': '#1A636B',
+    "H_companysize": '#A7BCC6', #specified random color
+    'H_revenue': '#49677B',
+    'H_employee': '#125F51',
+    'SC_Q_H_employee': '#125F51',
+    'I_importance': '#074057',
+    'IB_imp_weighted': '#5B8394',
+    'I_importance_fut': '#01626F',
+    'IB_imp_fut_weighted': '#39808B',
+    'I_invest_share': '#7B8D24',
+    'IB_invest_share_weighted': '#BCCFD6',
+    'I_invest_share_fut': '#16936D',
+    'IB_invest_share_fut_weighted': '#1D6073',
+    'I_eneffincrease_fut': '#007B86',
+    'H_energyuse': '#245B60',
+    'IB_energyuse_fut': '#587081',
+    'IB_energyuse_weighted': '#146153',
+    'IB_energyuse_fut_weighted': '#035263',
+    'H_energyuse_classes': '#66889A',
+    'SC_Q_H_energyuse_classes': '#66889A',
+    'SC_Q_S23_turnover_energycost': '#245B60', #specified random color  
+    'H_energyintensity': '#186B77',
+    'Des_Gesamtumsatz': '#4A8A95',
+    'Prod_Erw': '#064B55',
+    'Prod_BDB': '#A7BCC6'
+}
+
+column_name_map = {
+    'H_sector': 'Sector',
+    'SC_Q_H_sector': 'Sector',
+    'SC_Q_Origin': 'Origin', 
+    'SC_Q_H_state': 'State',
+    'SC_Q_H_scope': 'SC_Q_H_scope',
+    'H_revenue': 'Revenue',
+    'H_employee': 'Employees',
+    'SC_Q_H_employee': 'Employees', 
+    "H_companysize": 'Company Size',
+    'I_importance': 'Importance',
+    'IB_imp_weighted': 'Weighted Importance',
+    'I_importance_fut': 'Future Importance',
+    'IB_imp_fut_weighted': 'Future Weighted Importance',
+    'I_invest_share': 'Investment Share (Past)',
+    'IB_invest_share_weighted': 'Weighted Investment Share (Past)',
+    'I_invest_share_fut': 'Investment Share (Future)',
+    'IB_invest_share_fut_weighted': 'Weighted Investment Share (Future)',
+    'I_eneffincrease_fut': 'Future Energy Efficiency Increase',
+    'H_energyuse': 'Energy Use',
+    'SC_Q_S23_turnover_energycost': 'Turn over Energy Cose', #must be reviewed
+    'IB_energyuse_fut': 'Future Energy Use',
+    'IB_energyuse_weighted': 'Weighted Energy Use',
+    'IB_energyuse_fut_weighted': 'Weighted Future Energy Use',
+    'H_energyuse_classes': 'Energy Use Classes',
+    "SC_Q_H_energyuse_classes": 'Energy Use Classes',
+    'H_energyintensity': 'Energy Intensity',
+    'Des_Gesamtumsatz': 'Total Revenue',
+    'Prod_Erw': 'Product Development',
+    'Prod_BDB': 'Product BDB',
+}
+
+def create_visualizations(df, selected_column):
+    # Line graph
+    fig_line = px.line(df, y=selected_column, title=f"Line Graph for {selected_column}")
+    line_color = PREDEFINED_COLORS.get(selected_column, 'black')
+    fig_line.update_traces(line=dict(color=line_color))
+    fig_line.update_layout(width=800, height=400)
+    st.plotly_chart(fig_line)
+    
+    # Bar chart
+    fig_bar = px.bar(df, y=selected_column, title=f"Bar Chart for {selected_column}")
+    bar_color = PREDEFINED_COLORS.get(selected_column, 'blue')
+    fig_bar.update_traces(marker_color=bar_color)
+    fig_bar.update_layout(width=800, height=400)
+    st.plotly_chart(fig_bar)
+    
+    # Pie chart (for categorical data or numerical data with few unique values)
+    if df[selected_column].dtype == 'object' or df[selected_column].nunique() < 10:
+        value_counts = df[selected_column].value_counts()
+        fig_pie = px.pie(names=value_counts.index, values=value_counts.values, title=f"Distribution of {selected_column}")
+        
+        # Use predefined colors for the pie chart slices
+        pie_colors = [PREDEFINED_COLORS.get(c, 'grey') for c in value_counts.index]
+        fig_pie.update_traces(marker=dict(colors=pie_colors))
+        
+        fig_pie.update_layout(width=800, height=400)
+        st.plotly_chart(fig_pie)
+    else:
+        st.write(f"Pie chart not applicable for {selected_column} due to high number of unique values.")
+
+# Streamlit app
+st.title('Fraunhofer Database')
+
+# File uploader
+uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+
+if uploaded_file is not None:
+    # Load the data
+    df = pd.read_csv(uploaded_file)
+    
+    # Get column names
+    columns = df.columns.tolist()
+
+    df_renamed = df.rename(columns=column_name_map)
+
+    # Sidebar for user input
+    st.sidebar.header('Options')
+    view_option = st.sidebar.radio('Select View Option', 
+                               ['View All Data', 'Select Columns', 'Filter Data', 'Natural Language Query', 'Visualize Data', 'General Visualization'])
+
+    if view_option == 'View All Data':
+        st.write(df)
+
+    elif view_option == 'Select Columns':
+        selected_columns = st.sidebar.multiselect('Select Columns', columns)
+        if selected_columns:
+            st.write(df[selected_columns])
+        else:
+            st.write('Please select at least one column.')
+
+    elif view_option == 'Filter Data':
+        filter_column = st.sidebar.selectbox('Select Column to Filter', columns)
+        filter_operator = st.sidebar.selectbox('Select Operator', ['==', '>', '<', '>=', '<=', '!='])
+        filter_value = st.sidebar.text_input('Enter Filter Value')
+        
+        if st.sidebar.button('Apply Filter'):
+            if filter_value:
+                column_type = df[filter_column].dtype
+                if column_type == 'int64':
+                    filter_value = int(filter_value)
+                elif column_type == 'float64':
+                    filter_value = float(filter_value)
+                
+                # Apply filter
+                filtered_df = df.query(f"`{filter_column}` {filter_operator} @filter_value")
+                st.write(filtered_df)
+            else:
+                st.write('Please enter a filter value.')
+
+    elif view_option == 'Natural Language Query':
+        query = st.text_input('Enter your query (e.g., "Show me rows where age is greater than 30")')
+        if query:
+            column, operator, value = parse_query(query, df)
+            if column and operator and value is not None:
+                filtered_df = df.query(f"`{column}` {operator} @value")
+                st.write(filtered_df)
+            else:
+                st.write("Couldn't understand the query. Please try rephrasing.")
+
+    elif view_option == 'Visualize Data':
+        st.sidebar.subheader('Visualization Options')
+        chart_type = st.sidebar.selectbox('Select Chart Type', ['Bar Chart', 'Stacked Bar Chart', 'Line Graph', 'Pie Chart'])
+        
+        # Sliders for figure size
+        width = st.sidebar.slider('Select Figure Width', 400, 1200, 800)
+        height = st.sidebar.slider('Select Figure Height', 300, 800, 600)
+    
+        # Bar Chart Example
+        if chart_type == 'Bar Chart':
+            x_axis = st.sidebar.selectbox('Select X-axis', columns)
+            y_axis = st.sidebar.selectbox('Select Y-axis', columns)
+            if x_axis and y_axis:
+                fig = px.bar(df, x=x_axis, y=y_axis, title=f'{column_name_map.get(y_axis, "Undefined Y-axis")} by {x_axis}')
+                
+                # Apply predefined colors
+                if y_axis in PREDEFINED_COLORS:
+                    fig.update_traces(marker_color=PREDEFINED_COLORS[y_axis])
+                fig.update_layout(
+                    title=f'{column_name_map.get(y_axis, "Undefined Y-axis")} by {column_name_map.get(x_axis, "Undefined X-axis")}',
+                    xaxis_title=column_name_map.get(x_axis, "Undefined X-axis"),
+                    yaxis_title=column_name_map.get(y_axis, "Undefined Y-axis"),
+                    width=width,
+                    height=height
+                )
+                st.plotly_chart(fig)
+    
+        elif chart_type == 'Stacked Bar Chart':
+            x_axis = st.sidebar.selectbox('Select X-axis', columns)
+            y_axis = st.sidebar.selectbox('Select Y-axis', columns)
+            secondary_y_axis = st.sidebar.selectbox('Select Secondary Y-axis (for stacking)', columns)
+            if x_axis and y_axis and secondary_y_axis:
+                fig = go.Figure(data=[
+                    go.Bar(name=f'{column_name_map.get(y_axis, "Undefined Y-axis")}', x=df[x_axis], y=df[y_axis], marker_color=PREDEFINED_COLORS.get(y_axis, 'blue')),
+                    go.Bar(name=f'{column_name_map.get(secondary_y_axis, "Undefined Secondary Y-axis")}', x=df[x_axis], y=df[secondary_y_axis], marker_color=PREDEFINED_COLORS.get(secondary_y_axis, 'green'))
+                ])
+                fig.update_layout(barmode='stack', title=f'{column_name_map.get(y_axis, "Undefined Y-axis")} and {column_name_map.get(secondary_y_axis, "Undefined Secondary Y-axis")} by {column_name_map.get(x_axis, "Undefined X-axis")}', width=width, height=height)
+                st.plotly_chart(fig)
+    
+        elif chart_type == 'Line Graph':
+            x_axis = st.sidebar.selectbox('Select X-axis for Line Graph', columns)
+            y_axes = st.sidebar.multiselect('Select Y-axes for Line Graph', columns)
+            if x_axis and y_axes:
+                fig = go.Figure()
+                
+                for y_axis in y_axes:
+                    fig.add_trace(go.Scatter(
+                        x=df[x_axis], 
+                        y=df[y_axis], 
+                        mode='lines', 
+                        name=column_name_map.get(y_axis, y_axis),  # Use mapped name for the legend
+                        line=dict(color=PREDEFINED_COLORS.get(y_axis, 'black'))
+                    ))
+                
+                # Update layout with custom titles
+                fig.update_layout(
+                    title=f'{", ".join(column_name_map.get(y_axis, y_axis) for y_axis in y_axes)} over {column_name_map.get(x_axis, x_axis)}',
+                    xaxis_title=column_name_map.get(x_axis, "Undefined X-axis"),
+                    yaxis_title="Values",  # You can customize this or use a different mapping
+                    width=width,
+                    height=height
+                )
+                
+                st.plotly_chart(fig)
+    
+        # Pie Chart
+        elif chart_type == 'Pie Chart':
+            category_column = st.sidebar.selectbox('Select Category for Pie Chart', columns)
+            values_column = st.sidebar.selectbox('Select Values for Pie Chart', columns)
+            if category_column and values_column:
+                st.subheader(f'Pie Chart: {column_name_map.get(values_column, values_column)} by {column_name_map.get(category_column, category_column)}')
+                
+                # Get the value counts
+                value_counts = df.groupby(category_column)[values_column].sum().sort_values(ascending=False)
+                
+                # Generate warm to cold colors based on the number of slices
+                n_slices = len(value_counts)
+                color_map = get_warm_to_cold_colors(n_slices)
+                
+                # Create the pie chart using the generated color map
+                fig = px.pie(df, names=value_counts.index, values=value_counts.values, 
+                             title=f'{column_name_map.get(values_column, values_column)} distribution by {column_name_map.get(category_column, category_column)}',
+                             color_discrete_sequence=color_map)
+                
+                fig.update_layout(width=width, height=height)
+                st.plotly_chart(fig)
+
+    elif view_option == 'General Visualization':
+        st.subheader('General Visualization')
+        
+        # Function to get columns starting with a specific prefix
+        def get_columns_with_prefix(df, prefix):
+            return [col for col in df.columns if col.startswith(prefix)]
+        
+        # Get columns for each category
+        h_columns = get_columns_with_prefix(df, 'H_')
+        i_columns = get_columns_with_prefix(df, 'I_')
+        ib_columns = get_columns_with_prefix(df, 'IB_')
+        
+        # Create visualizations for a selected column
+        def create_visualizations(df, selected_column):
+            # Line graph
+            fig_line = px.line(df, y=selected_column, 
+                               title=f"Line Graph for {column_name_map.get(selected_column, selected_column)}")
+            line_color = PREDEFINED_COLORS.get(selected_column, 'black')
+            fig_line.update_traces(line=dict(color=line_color))
+            fig_line.update_layout(
+                xaxis_title=column_name_map.get('x_axis', 'Index'),  # Optional: update X-axis title if relevant
+                yaxis_title=column_name_map.get(selected_column, 'Y-axis'),  # Map Y-axis title
+                width=800, height=400
+            )
+            st.plotly_chart(fig_line)
+            
+            # Bar chart
+            fig_bar = px.bar(df, y=selected_column, 
+                             title=f"Bar Chart for {column_name_map.get(selected_column, selected_column)}")
+            bar_color = PREDEFINED_COLORS.get(selected_column, 'blue')
+            fig_bar.update_traces(marker_color=bar_color)
+            fig_bar.update_layout(
+                xaxis_title=column_name_map.get('x_axis', 'Index'),  # Optional: update X-axis title if relevant
+                yaxis_title=column_name_map.get(selected_column, 'Y-axis'),  # Map Y-axis title
+                width=800, height=400
+            )
+            st.plotly_chart(fig_bar)
+            
+            # Pie chart (for categorical data or numerical data with few unique values)
+            if df[selected_column].dtype == 'object' or df[selected_column].nunique() < 10:
+                value_counts = df[selected_column].value_counts()
+                fig_pie = px.pie(names=value_counts.index, values=value_counts.values, 
+                                 title=f"Distribution of {column_name_map.get(selected_column, selected_column)}")
+                
+                # Use predefined colors for the pie chart slices
+                pie_colors = [PREDEFINED_COLORS.get(c, 'grey') for c in value_counts.index]
+                fig_pie.update_traces(marker=dict(colors=pie_colors))
+                
+                fig_pie.update_layout(width=800, height=400)
+                st.plotly_chart(fig_pie)
+            else:
+                st.write(f"Pie chart not applicable for {column_name_map.get(selected_column, selected_column)} due to high number of unique values.")
+
+        
+        # Create tabs for different visualizations
+        tabs = st.tabs(["H Columns", "I Columns", "IB Columns"])
+        
+        with tabs[0]:
+            st.subheader("H Columns Visualization")
+            h_selected = st.selectbox("Select an H column to visualize", h_columns)
+            if h_selected:
+                create_visualizations(df, h_selected)
+        
+        with tabs[1]:
+            st.subheader("I Columns Visualization")
+            i_selected = st.selectbox("Select an I column to visualize", i_columns)
+            if i_selected:
+                create_visualizations(df, i_selected)
+        
+        with tabs[2]:
+            st.subheader("IB Columns Visualization")
+            ib_selected = st.selectbox("Select an IB column to visualize", ib_columns)
+            if ib_selected:
+                create_visualizations(df, ib_selected)
+        
+        # Summary statistics
+        st.subheader("Summary Statistics")
+        all_selected = h_columns + i_columns + ib_columns
+        if all_selected:
+            # Create a new DataFrame for summary statistics with mapped column names
+            summary_df = df[all_selected].describe().T  # Transpose for better readability
+            summary_df.index = [column_name_map.get(col, col) for col in summary_df.index]  # Update index names
+
+            st.write(summary_df)
+
+else:
+    st.write('Please upload your data.')
+
+st.sidebar.info('Designed by Taha Rasouli at Fraunhofer')