PyCafe - Dash - with import plotly.graph

GroceryDB_foods.csv
app.py
requirements.txt
app.py
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from dash import Dash, dcc, html, Input, Output, callback, dash_table
import pandas as pd
import plotly.graph_objs as go
import dash_ag_grid as dag

# Load the CSV file
df = pd.read_csv('GroceryDB_foods.csv')

# Strip spaces from column names
df.columns = df.columns.str.strip()

# Rename for easier access
df.rename(columns={'harmonized single category': 'category'}, inplace=True)

# Drop rows where category is missing
df = df.dropna(subset=['category'])

# Calculate per 100g values for each food item
category_summary = df.groupby('category').agg({
    'Protein': 'mean',     
    'Carbohydrate': 'mean',
    'Total Fat': 'mean',
    'price': 'mean'
}).reset_index()

# Merge category averages with original data
df = df.merge(category_summary, on='category', suffixes=('', '_avg'))

# Create Dash app
app = Dash(__name__)

# Get unique categories for dropdown
all_categories = category_summary['category'].unique()

# Layout
app.layout = html.Div([
    html.H1("🍔 Nutritional Content & Price by Category 🥗", style={'textAlign': 'center', 'color': '#333'}),
    
    html.Div([
        html.Label("Select Categories:", style={'fontSize': '16px', 'fontWeight': 'bold'}),
        dcc.Dropdown(
            id='category-dropdown',
            options=[{'label': cat, 'value': cat} for cat in all_categories],
            value=all_categories[:5].tolist(),  # Default to first 5 categories
            multi=True
        )
    ], style={'width': '70%', 'margin': '20px auto'}),
    
    dcc.Graph(id='nutrition-price-graph', style={'height': '600px'}),
    
    html.H2("Detailed Product Data", style={'textAlign': 'center', 'marginTop': '20px'}),
    dag.AgGrid(
        id='data-table',
        columnDefs=[
            {'headerName': 'Product Name', 'field': 'name'},
            {'headerName': 'Category', 'field': 'category'},
            {'headerName': 'Protein (g)', 'field': 'Protein'},
            {'headerName': 'Carbohydrates (g)', 'field': 'Carbohydrate'},
            {'headerName': 'Total Fat (g)', 'field': 'Total Fat'},
            {'headerName': 'Price (USD)', 'field': 'price'},
            #{'headerName': 'Avg Protein (g)', 'field': 'Protein_avg'},
            #{'headerName': 'Avg Carbohydrates (g)', 'field': 'Carbohydrate_avg'},
            #{'headerName': 'Avg Total Fat (g)', 'field': 'Total Fat_avg'},
            #{'headerName': 'Avg Price (USD)', 'field': 'price_avg'}
        ],
        rowData=[],
        defaultColDef={'sortable': True, 'filter': True, 'resizable': True},
        style={'height': '400px', 'width': '90%', 'margin': 'auto'}
    )
])

@callback(
    Output('nutrition-price-graph', 'figure'),
    Output('data-table', 'rowData'),
    Input('category-dropdown', 'value')
)
def update_graph_and_table(selected_categories):
    if not selected_categories:
        selected_categories = all_categories[:5].tolist()
    
    filtered_data = category_summary[category_summary['category'].isin(selected_categories)]
    filtered_products = df[df['category'].isin(selected_categories)][['category', 'name', 'Protein', 'Carbohydrate', 'Total Fat', 'price', 'Protein_avg', 'Carbohydrate_avg', 'Total Fat_avg', 'price_avg']].to_dict('records')
    
    fig = go.Figure()
    
    fig.add_trace(go.Bar(
        y=filtered_data['category'],
        x=filtered_data['Protein'],
        name='Protein',
        marker=dict(color='#410445'),
        orientation='h'
    ))
    
    fig.add_trace(go.Bar(
        y=filtered_data['category'],
        x=filtered_data['Carbohydrate'],
        name='Carbohydrates',
        marker=dict(color='#A5158C'),
        orientation='h'
    ))
    
    fig.add_trace(go.Bar(
        y=filtered_data['category'],
        x=filtered_data['Total Fat'],
        name='Total Fat',
        marker=dict(color='#FF2DF1'),
        orientation='h'
    ))
    
    fig.add_trace(go.Scatter(
        y=filtered_data['category'],
        x=filtered_data['price'],
        mode='markers',
        name='Average Price',
        xaxis='x2',
        marker=dict(color='gold', size=35, symbol='circle')
    ))
    
    fig.update_layout(
        title='Nutritional Content & Price by Category',
        xaxis={'title': 'Nutritional Value (g/100g)'},
        xaxis2={'title': 'Average Price (USD)', 'overlaying': 'x', 'side': 'top'},
        barmode='stack',
        showlegend=True,
        paper_bgcolor='white',
        plot_bgcolor='white'
    )
    
    return fig, filtered_products

if __name__ == '__main__':
    app.run(debug=True)
Py.Cafe

Nutritional Content & Price by Category

with import plotly.graph_objs as go
