support upload npy file, improve UI

app.py

@@ -1,256 +1,250 @@
-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
-
-def plot_from_npy(npy_data):
-    fig = go.Figure()
-
-    fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[10, 11, 12, 13], mode='lines', name='New York'))
-    fig.update_layout(title_text="Facility Distribution in Cities")
-    fig.update_xaxes(title_text="Time")
-    fig.update_yaxes(title_text="Facility Count")
-    
-
-    actual_fig = fig  # Replace this line with your actual_fig
-    solution_fig = fig  # Replace this line with your solution_fig
-    
-    return actual_fig, solution_fig
-
-def solver_plot(data_npy, boost=False):
-    actual_fig = go.Figure()
-    solution_fig = go.Figure()
-    actual_ac = 0  # Replace this line with your actual_ac
-    solution_ac = 0  # Replace this line with your solution_ac
-    return actual_fig, solution_fig, actual_ac, solution_ac
-    
-def demo_plot(city, facility):
-    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}.npy')
-    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=f'Facility {i+1}'
-        ))
-        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=f'Facility {i+1}'
-        ))
-    
-        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_plot(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)]
-        print(row)
-        
-        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(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
-    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 get_example():
-    return [
-        ("40.71 -73.93 213 0\n\
-            40.72 -73.99 15 1\n\
-            40.65 -73.88 365 1\n\
-            40.57 -73.96 629 0\n\
-            40.70 -73.97 106 0\n\
-            40.61 -73.95 189 1"),
-        ("40.71 -73.93 213 124 0 1\n\
-            40.72 -73.99 15 43 1 0\n\
-            40.65 -73.88 365 214 1 0\n\
-            40.57 -73.96 629 431 0 1\n\
-            40.70 -73.97 106 241 0 1\n\
-            40.61 -73.95 189 264 1 0")
-        ]
-    
-    
-
-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='Relocation Facility Distribution')
-        with gr.Row():
-            actual_ac = gr.Textbox(label='Real-world Access Cost')
-            solution_ac = gr.Textbox(label='Relocation 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 npy file",
-            file_count="multiple", 
-            file_types=[".npy"])
-        with gr.Row():
-            boost = gr.Checkbox(label="Turbo Boost", value=False)
-            btn2 = gr.Button(value="🚀 Generate")
-        with gr.Row():
-            actual_map = gr.Plot(label='Real-world Facility Distribution')
-            solution_map = gr.Plot(label='Relocation Facility Distribution')
-        with gr.Row():
-            actual_ac = gr.Textbox(label='Real-world Access Cost')
-            solution_ac = gr.Textbox(label='Relocation Access Cost')
-    data_file.upload(fn=solver_plot, inputs=[data_file, boost], outputs=[actual_map, solution_map, actual_ac, solution_ac])
-    btn2.click(fn=solver_plot, inputs=[data_npy, boost], outputs=[actual_map, solution_map, actual_ac, solution_ac])
-    
-    gr.Examples(
-        examples=get_example(),
-        inputs=[data_npy],
-        fn=plot_from_npy,
-        outputs=[actual_map, solution_map],
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+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
+
+def plot_from_npy(npy_data):
+    fig = go.Figure()
+
+    fig.add_trace(go.Scatter(x=[1, 2, 3, 4], y=[10, 11, 12, 13], mode='lines', name='New York'))
+    fig.update_layout(title_text="Facility Distribution in Cities")
+    fig.update_xaxes(title_text="Time")
+    fig.update_yaxes(title_text="Facility Count")
+    
+
+    actual_fig = fig  # Replace this line with your actual_fig
+    solution_fig = fig  # Replace this line with your solution_fig
+    
+    return actual_fig, solution_fig
+
+def solver_plot(data_npy, boost=False):
+    actual_fig = go.Figure()
+    solution_fig = go.Figure()
+    actual_ac = 0  # Replace this line with your actual_ac
+    solution_ac = 0  # Replace this line with your solution_ac
+    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}.npy')
+    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_plot(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)]
+        print(row)
+        
+        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
+    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 get_example():
+    return [
+        ('40.71 -73.93 213 0\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\n40.61 -73.95 189 264 1 0")
+        ]
+
+
+def load_npy_file(file_obj):
+    data = np.load(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 npy file",
+            file_count="single", 
+            file_types=[".npy"])
+        with gr.Row():
+            gr.Examples(
+                examples=get_example(),
+                inputs=[data_npy],
+                fn=plot_from_npy,
+                outputs=[actual_map, solution_map],
+            )
+        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()