app.py

import gradio as gr
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from sklearn.metrics import pairwise_distances
import torch
from facility_location import multi_eval
import pickle



def solver_plot(data_npy, boost=False):
    multi_eval.main(data_npy, boost)
    all_solutions = pickle.loads(open('./facility_location/solutions.pkl', 'rb').read())
    
    data = data_npy.split('\n')
    n = len(data)
    p = int((len(data[0].split(' '))-2) / 2)
    
    positions = []
    demands = []
    actual_facilities = []
    for row in data:
        row = row.split(' ')
        row = [x for x in row if len(x)]
        
        positions.append([float(row[0]), float(row[1])])
        
        demand = []
        for i in range(2, 2+p):
            demand.append(float(row[i]))
        demands.append(demand)
        
        actual_facility = []
        for i in range(2+p, 2+2*p):
            actual_facility.append(bool(int(float(row[i]))))
        actual_facilities.append(actual_facility)
    positions = np.array(positions)
    demands = np.array(demands)
    actual_facilities = np.array(actual_facilities)
    solution_facilities =  np.array(all_solutions).T
    # print(solution_facilities)
    # print(actual_facilities)
        
    actual_fig = go.Figure()
    solution_fig = go.Figure()
    for i in range(p):
        actual_fig.add_trace(go.Scattermapbox(
            lat=positions[actual_facilities[:, i]][:, 0],
            lon=positions[actual_facilities[:, i]][:, 1],
            mode='markers',
            marker=go.scattermapbox.Marker(
                size=10,
                color=px.colors.qualitative.Plotly[i]
            ),
            name=f'Facility {i+1}'
        ))
        solution_fig.add_trace(go.Scattermapbox(
            lat=positions[solution_facilities[:, i]][:, 0],
            lon=positions[solution_facilities[:, i]][:, 1],
            mode='markers',
            marker=go.scattermapbox.Marker(
                size=10,
                color=px.colors.qualitative.Plotly[i]
            ),
            name=f'Facility {i+1}'
        ))
    
        actual_fig.update_layout(
        mapbox=dict(
            style='carto-positron',
            center=dict(lat=np.mean(positions[actual_facilities[:, i]][:, 0]), \
                lon=np.mean(positions[actual_facilities[:, i]][:, 1])),
            zoom=11.0
        ),
        margin=dict(l=0, r=0, b=0, t=0),)

        solution_fig.update_layout(
        mapbox=dict(
            style='carto-positron',
            center=dict(lat=np.mean(positions[solution_facilities[:, i]][:, 0]), \
                lon=np.mean(positions[solution_facilities[:, i]][:, 1])),
            zoom=11.0
        ),
        margin=dict(l=0, r=0, b=0, t=0),)
        # show legend
        actual_fig.update_layout(showlegend=True)
        solution_fig.update_layout(showlegend=True)
    
    positions = np.deg2rad(positions)
    dist = pairwise_distances(positions, metric='haversine') * 6371
    actual_ac = 0
    solution_ac = 0
    for i in range(p):
        ac_matrix = dist * demands[:, i][:, None]
        actual_ac += ac_matrix[:, actual_facilities[:, i]].min(axis=-1).sum()
        solution_ac += ac_matrix[:, solution_facilities[:, i]].min(axis=-1).sum()
        
    return actual_fig, solution_fig, actual_ac, solution_ac
    
def demo_plot(city, facility):
    facility_name = ["🏫 School", "🏥 Hospital", "🌳 Park"]
    all_facility = ["🏫 School", "🏥 Hospital", "🌳 Park"]
    for i in range(len(all_facility)):
        if all_facility[i] in facility:
            all_facility[i] = True
        else:
            all_facility[i] = False
    
    city_name = city.replace(' ', '_')
    data = np.loadtxt(f'demo/{city_name}.txt')
    positions = data[:, :2]
    demands = data[:, 2:5]
    actual_facility = data[:, 5:8]
    solution_facility = data[:, 8:11]
    
    actual_fig = go.Figure()
    solution_fig = go.Figure()
    for i in range(len(all_facility)):
        if not all_facility[i]:
            continue
        
        actual_fig.add_trace(go.Scattermapbox(
            lat=positions[actual_facility[:, i] == 1][:, 0],
            lon=positions[actual_facility[:, i] == 1][:, 1],
            mode='markers',
            marker=go.scattermapbox.Marker(
                size=10,
                color=px.colors.qualitative.Plotly[i]
            ),
            name=facility_name[i],
        ))
        solution_fig.add_trace(go.Scattermapbox(
            lat=positions[solution_facility[:, i] == 1][:, 0],
            lon=positions[solution_facility[:, i] == 1][:, 1],
            mode='markers',
            marker=go.scattermapbox.Marker(
                size=10,
                color=px.colors.qualitative.Plotly[i]
            ),
            name=facility_name[i],
        ))
    
        actual_fig.update_layout(
        mapbox=dict(
            style='carto-positron',
            center=dict(lat=np.mean(positions[actual_facility[:, i] == 1][:, 0]), \
                lon=np.mean(positions[actual_facility[:, i] == 1][:, 1])),
            zoom=11.0
        ),
        margin=dict(l=0, r=0, b=0, t=0),)

        solution_fig.update_layout(
        mapbox=dict(
            style='carto-positron',
            center=dict(lat=np.mean(positions[solution_facility[:, i] == 1][:, 0]), \
                lon=np.mean(positions[solution_facility[:, i] == 1][:, 1])),
            zoom=11.0
        ),
        margin=dict(l=0, r=0, b=0, t=0),)
        # show legend
        actual_fig.update_layout(showlegend=True)
        solution_fig.update_layout(showlegend=True)
    
    positions = np.deg2rad(positions)
    dist = pairwise_distances(positions, metric='haversine') * 6371
    actual_ac = 0
    solution_ac = 0
    for i in range(len(all_facility)):
        if not all_facility[i]:
            continue
        ac_matrix = dist * demands[:, i][:, None]
        actual_ac += ac_matrix[:, actual_facility[:, i] == 1].min(axis=-1).sum()
        solution_ac += ac_matrix[:, solution_facility[:, i] == 1].min(axis=-1).sum()
    
    return actual_fig, solution_fig, actual_ac, solution_ac


def solver_plot1(data_npy, boost=False):
    data = data_npy.split('\n')
    n = len(data)
    p = int((len(data[0].split(' '))-2) / 2)
    
    positions = []
    demands = []
    actual_facilities = []
    for row in data:
        row = row.split(' ')
        row = [x for x in row if len(x)]
        
        positions.append([float(row[0]), float(row[1])])
        
        demand = []
        for i in range(2, 2+p):
            demand.append(float(row[i]))
        demands.append(demand)
        
        actual_facility = []
        for i in range(2+p, 2+2*p):
            actual_facility.append(bool(int(float(row[i]))))
        actual_facilities.append(actual_facility)
    positions = np.array(positions)
    demands = np.array(demands)
    actual_facilities = np.array(actual_facilities)
    solution_facilities =  ~actual_facilities
        
    actual_fig = go.Figure()
    solution_fig = go.Figure()
    for i in range(p):
        actual_fig.add_trace(go.Scattermapbox(
            lat=positions[actual_facilities[:, i]][:, 0],
            lon=positions[actual_facilities[:, i]][:, 1],
            mode='markers',
            marker=go.scattermapbox.Marker(
                size=10,
                color=px.colors.qualitative.Plotly[i]
            ),
            name=f'Facility {i+1}'
        ))
        solution_fig.add_trace(go.Scattermapbox(
            lat=positions[solution_facilities[:, i]][:, 0],
            lon=positions[solution_facilities[:, i]][:, 1],
            mode='markers',
            marker=go.scattermapbox.Marker(
                size=10,
                color=px.colors.qualitative.Plotly[i]
            ),
            name=f'Facility {i+1}'
        ))
    
        actual_fig.update_layout(
        mapbox=dict(
            style='carto-positron',
            center=dict(lat=np.mean(positions[actual_facilities[:, i]][:, 0]), \
                lon=np.mean(positions[actual_facilities[:, i]][:, 1])),
            zoom=11.0
        ),
        margin=dict(l=0, r=0, b=0, t=0),)

        solution_fig.update_layout(
        mapbox=dict(
            style='carto-positron',
            center=dict(lat=np.mean(positions[solution_facilities[:, i]][:, 0]), \
                lon=np.mean(positions[solution_facilities[:, i]][:, 1])),
            zoom=11.0
        ),
        margin=dict(l=0, r=0, b=0, t=0),)
        # show legend
        actual_fig.update_layout(showlegend=True)
        solution_fig.update_layout(showlegend=True)
    
    positions = np.deg2rad(positions)
    dist = pairwise_distances(positions, metric='haversine') * 6371
    actual_ac = 0
    solution_ac = 0
    for i in range(p):
        ac_matrix = dist * demands[:, i][:, None]
        actual_ac += ac_matrix[:, actual_facilities[:, i]].min(axis=-1).sum()
        solution_ac += ac_matrix[:, solution_facilities[:, i]].min(axis=-1).sum()
        
    return actual_fig, solution_fig, actual_ac, solution_ac
    

def get_example():
    return [
        ('40.71 -73.93 213 1\n40.72 -73.99 15 1\n40.65 -73.88 365 1\n40.57 -73.96 629 0\n40.70 -73.97 106 0\n40.61 -73.95 189 1'),
        ("40.71 -73.93 213 124 0 1\n40.72 -73.99 15 43 1 0\n40.65 -73.88 365 214 1 0\n40.57 -73.96 629 431 0 1\n40.70 -73.97 106 241 0 1\n40.60 -73.92 129 214 1 0\n40.61 -73.95 189 264 0 1\n40.63 -73.94 124 164 1 0"),
        ]


def load_npy_file(file_obj):
    data = np.loadtxt(file_obj.name)
    string_array = '\n'.join([' '.join(map(str, row)) for row in data])
    return string_array
    

with gr.Blocks() as demo:
    gr.Markdown("## Demo")
    with gr.Column():
        city = gr.Radio(choices=["New York", "Boston", "Los Angeles", "Chicago"], value="New York", label="Select City:")
        facility = gr.CheckboxGroup(choices=["🏫 School", "🏥 Hospital", "🌳 Park"], value=["🏥 Hospital"], label="Select Facility:")
        btn = gr.Button(value="🚀 Generate")
        with gr.Row():
            actual_map = gr.Plot(label='Actual Facility Distribution')
            solution_map = gr.Plot(label='Relocated Facility Distribution')
        with gr.Row():
            actual_ac = gr.Textbox(label='Real-world Access Cost')
            solution_ac = gr.Textbox(label='Relocated Access Cost')
    demo.load(fn=demo_plot, inputs=[city, facility], outputs=[actual_map, solution_map, actual_ac, solution_ac])
    btn.click(fn=demo_plot, inputs=[city, facility], outputs=[actual_map, solution_map, actual_ac, solution_ac])
    
    gr.Markdown("## FLP & IUMFLP Solver")
    with gr.Column():
        with gr.Row():
            data_npy = gr.Textbox(label="Input")
            data_file = gr.UploadButton(
            label="📁 Upload a txt file",
            file_count="single", 
            file_types=[".txt"])
        with gr.Row():
            gr.Examples(
                examples=get_example(),
                inputs=[data_npy],
                fn=solver_plot1,
                outputs=[actual_map, solution_map, actual_ac, solution_ac],
            )
        with gr.Row():
            boost = gr.Checkbox(label="Turbo Boost (accelerate solution generation with fewer SWAP steps)", value=False)
            btn2 = gr.Button(value="🚀 Generate")
        with gr.Row():
            actual_map = gr.Plot(label='Initial Solution')
            solution_map = gr.Plot(label='Final Solution')
        with gr.Row():
            actual_ac = gr.Textbox(label='Initial Access Cost')
            solution_ac = gr.Textbox(label='Final Access Cost')
    data_file.upload(fn=load_npy_file, inputs=[data_file], outputs=[data_npy])
    btn2.click(fn=solver_plot, inputs=[data_npy, boost], outputs=[actual_map, solution_map, actual_ac, solution_ac])
    
if __name__ == "__main__":
    demo.launch()