Update app_improved.py

app_improved.py

@@ -1,4 +1,4 @@
-# app_improved.py — Compatible with Gradio 5.0.0
+# app_improved.py — Redesigned UI
 
 import io
 import os
@@ -6,9 +6,7 @@ import json
 import tempfile
 import pandas as pd
 
-# --------------------
-# Compatibility shim for mismatched huggingface_hub versions
-# --------------------
+# Compatibility shim for huggingface_hub
 try:
     import huggingface_hub as _hf
 except Exception:
@@ -35,50 +33,69 @@ from solver import (
     make_template_dataframe,
 )
 
-TITLE = "Ride-Sharing Optimizer — Clean UI"
-FOOTER = "Made with ❤️ using Gradio — improved layout and UX"
+# ---------- Design notes
+# Goals:
+# - Sidebar controls (compact) and large visualization area
+# - Clear download buttons and readable metrics
+# - Presets + small help text
+
+TITLE = "Ride-Sharing Optimizer"
+FOOTER = "Gradio UI — improved layout"
 
 CUSTOM_CSS = r"""
-:root{--bg:#0f1724;--card:#0b1220;--muted:#94a3b8;--accent:#4f46e5;--accent-2:#06b6d4}
-body{background:linear-gradient(180deg,#071024 0%, var(--bg) 100%); color:#dbeafe}
-.gradio-container {padding:20px 14px}
-.header {display:flex;align-items:center;gap:16px}
-.brand {font-size:20px;font-weight:700;color:var(--accent)}
-.subtitle{color:var(--muted);margin-top:4px}
-.card{background:var(--card);padding:14px;border-radius:12px;box-shadow:0 6px 18px rgba(2,6,23,0.6)}
-.small{font-size:13px;color:var(--muted)}
-.note{font-size:12px;color:var(--muted)}
-.footer{margin-top:16px;color:var(--muted);font-size:13px}
+:root{--bg:#0b1220;--panel:#0f1724;--muted:#94a3b8;--accent:#7c3aed}
+body{background:linear-gradient(180deg,#061022 0%, var(--bg) 100%);color:#e6eef8}
+.gradio-container{padding:18px}
+.sidebar{background:var(--panel);padding:14px;border-radius:10px}
+.brand{font-weight:700;font-size:18px;color:var(--accent)}
+.small{color:var(--muted);font-size:13px}
+.controls .gr-button{width:100%}
+.metrics{background:rgba(255,255,255,0.02);padding:10px;border-radius:8px}
+.table-wrap{max-height:280px;overflow:auto}
 """
 
-def format_metrics(metrics_dict):
-    rows = []
-    for i, d in enumerate(metrics_dict.get("per_route_distance", []), 1):
-        load = metrics_dict.get("per_route_load", [])[i-1] if metrics_dict.get("per_route_load") else "-"
-        rows.append(f"| {i} | {d:.2f} | {load} |")
-    route_table = "\n".join(rows)
-
-    md = f"""
-### 🚗 Summary
-**Vehicles used:** {metrics_dict.get('vehicles_used',0)}  
-**Capacity:** {metrics_dict.get('capacity',0)}  
-**Total distance:** {metrics_dict.get('total_distance',0):.2f}  
-
-**Per-route**  
-| Route | Distance | Load |  
-|:-----|--------:|:----:|  
-{route_table}
-
-**Time windows**  
-- total lateness count: {metrics_dict.get('time_window_report',{}).get('total_lateness_count',0)}  
-- total lateness: {metrics_dict.get('time_window_report',{}).get('total_lateness',0):.2f}  
-- status: {metrics_dict.get('time_window_report',{}).get('status','-')}  
-
-**Note:** {metrics_dict.get('note','')}
-"""
-    return md
+# ---------- helpers
+
+def format_metrics(metrics):
+    if not metrics:
+        return "No metrics available"
+    lines = [f"**Vehicles used:** {metrics.get('vehicles_used',0)}  ",
+             f"**Capacity:** {metrics.get('capacity',0)}  ",
+             f"**Total distance:** {metrics.get('total_distance',0):.2f}  
+"]
+    per = metrics.get('per_route_distance', [])
+    if per:
+        lines.append("**Per-route distances:**  
+")
+        for i, d in enumerate(per, 1):
+            load = metrics.get('per_route_load', [])[i-1] if metrics.get('per_route_load') else "-"
+            lines.append(f"- Route {i}: {d:.2f} (load {load})  ")
+    tw = metrics.get('time_window_report', {})
+    if tw:
+        lines.append('
+**Time windows**  ')
+        lines.append(f"- lateness count: {tw.get('total_lateness_count',0)}  ")
+        lines.append(f"- total lateness: {tw.get('total_lateness',0):.2f}  ")
+        lines.append(f"- status: {tw.get('status','-')}  ")
+    return "
+".join(lines)
+
+# Safe file writer
+def _write_df_to_temp(df):
+    tf = tempfile.NamedTemporaryFile(delete=False, suffix='.csv')
+    df.to_csv(tf.name, index=False)
+    tf.flush(); tf.close()
+    return tf.name
+
+# ---------- core app functions (return types compatible with Gradio)
+
+def run_generate(n_clients, n_vehicles, capacity, spread, demand_min, demand_max, seed, speed, preset):
+    # presets tweak spread / capacities
+    if preset == 'City (dense)':
+        spread = max(20, spread/2)
+    elif preset == 'Suburban':
+        spread = max(80, spread*1.5)
 
-def run_generator(n_clients, n_vehicles, capacity, spread, demand_min, demand_max, seed, speed):
     df = generate_random_instance(
         n_clients=int(n_clients),
         n_vehicles=int(n_vehicles),
@@ -88,106 +105,88 @@ def run_generator(n_clients, n_vehicles, capacity, spread, demand_min, demand_ma
         demand_max=int(demand_max),
         seed=int(seed),
     )
-    depot = (0.0, 0.0)
-    sol = solve_vrp_tw(df, depot=depot, n_vehicles=int(n_vehicles), capacity=int(capacity), speed=float(speed))
-    img = plot_solution(df, sol, depot=depot)
-
-    tf = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
-    sol['assignments_table'].to_csv(tf.name, index=False)
-    tf.flush(); tf.close()
-
+    sol = solve_vrp_tw(df, depot=(0,0), n_vehicles=int(n_vehicles), capacity=int(capacity), speed=float(speed))
+    img = plot_solution(df, sol, depot=(0,0))
+    csv_path = _write_df_to_temp(sol['assignments_table'])
     metrics_md = format_metrics(sol.get('metrics', {}))
-    return img, sol['assignments_table'], metrics_md, tf.name
+    return img, sol['assignments_table'], metrics_md, csv_path
+
 
-def run_csv(file, n_vehicles, capacity, speed):
+def run_upload(file, n_vehicles, capacity, speed):
     if file is None:
-        raise gr.Error("Upload a CSV first")
+        raise gr.Error('Please upload a CSV')
+    # Gradio's File returns a tempfile-like object with .name
     df = parse_uploaded_csv(file.name if hasattr(file, 'name') else file)
-    depot = (0.0, 0.0)
-    sol = solve_vrp_tw(df, depot=depot, n_vehicles=int(n_vehicles), capacity=int(capacity), speed=float(speed))
-    img = plot_solution(df, sol, depot=depot)
-
-    tf = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
-    sol['assignments_table'].to_csv(tf.name, index=False)
-    tf.flush(); tf.close()
-
+    sol = solve_vrp_tw(df, depot=(0,0), n_vehicles=int(n_vehicles), capacity=int(capacity), speed=float(speed))
+    img = plot_solution(df, sol, depot=(0,0))
+    csv_path = _write_df_to_temp(sol['assignments_table'])
     metrics_md = format_metrics(sol.get('metrics', {}))
-    return img, sol['assignments_table'], metrics_md, tf.name
+    return img, sol['assignments_table'], metrics_md, csv_path
+
 
 def download_template():
     df = make_template_dataframe()
-    tf = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
-    df.to_csv(tf.name, index=False)
-    tf.flush(); tf.close()
-    return tf.name
+    return _write_df_to_temp(df)
 
+# ---------- UI
 with gr.Blocks(title=TITLE, css=CUSTOM_CSS) as demo:
-    with gr.Row(elem_id="header"):
+    with gr.Row():
         with gr.Column(scale=1):
-            gr.Markdown(f"<div class='header'><div class='brand'>{TITLE}</div></div>")
-            gr.Markdown("<div class='subtitle'>Generate sample scenarios or upload your own CSV. Capacity-based VRP with soft time windows.</div>")
+            with gr.Box(elem_classes='sidebar'):
+                gr.Markdown(f"<div class='brand'>{TITLE}</div>")
+                gr.Markdown("<div class='small'>Quickly generate scenarios or upload your data. Use presets for common patterns.</div>")
+                with gr.TabbedInterface():
+                    with gr.Tab('Generate'):
+                        preset = gr.Dropdown(['Default','City (dense)','Suburban'], value='Default', label='Preset')
+                        n_clients = gr.Slider(5, 200, value=30, step=1, label='Clients')
+                        n_vehicles = gr.Slider(1, 50, value=5, step=1, label='Vehicles')
+                        capacity = gr.Slider(1, 100, value=10, step=1, label='Capacity')
+                        spread = gr.Slider(10, 500, value=60, step=5, label='Spread')
+                        demand_min = gr.Slider(1, 5, value=1, step=1, label='Demand min')
+                        demand_max = gr.Slider(1, 10, value=3, step=1, label='Demand max')
+                        seed = gr.Number(value=42, label='Seed')
+                        speed = gr.Number(value=10.0, label='Vehicle speed')
+                        gen_btn = gr.Button('Generate & Solve')
+                        gen_dl = gr.File(label='Download last assignments')
+
+                    with gr.Tab('Upload'):
+                        upload_file = gr.File(label='Upload CSV (id,x,y,demand,tw_start,tw_end,service)')
+                        up_vehicles = gr.Slider(1, 100, value=5, step=1, label='Vehicles')
+                        up_capacity = gr.Slider(1, 200, value=15, step=1, label='Capacity')
+                        up_speed = gr.Number(value=10.0, label='Vehicle speed')
+                        up_btn = gr.Button('Optimize uploaded CSV')
+                        tmpl_btn = gr.Button('Download template CSV')
+                        up_dl = gr.File(label='Download assignments')
+
+                gr.Markdown('---')
+                gr.Markdown('*Tip: click a run button, then download CSV from the right panel.*', elem_classes='small')
 
-    with gr.Row():
         with gr.Column(scale=2):
-            with gr.Tabs() as main_tabs:
-                with gr.TabItem("Generate sample"):
-                    with gr.Row():
-                        with gr.Column(scale=1, min_width=300):
-                            n_clients = gr.Slider(5, 200, value=30, step=1, label="Number of riders")
-                            n_vehicles = gr.Slider(1, 20, value=4, step=1, label="Number of drivers")
-                            capacity = gr.Slider(1, 50, value=10, step=1, label="Vehicle capacity")
-                            spread = gr.Slider(10, 400, value=50, step=1, label="Spatial spread")
-                            demand_min = gr.Slider(1, 5, value=1, step=1, label="Min demand")
-                            demand_max = gr.Slider(1, 10, value=3, step=1, label="Max demand")
-                            seed = gr.Number(value=42, label="Random seed")
-                            speed = gr.Number(value=10.0, label="Vehicle speed (units/min)")
-                            run_btn = gr.Button("Generate & Optimize", variant="primary")
-
-                        with gr.Column(scale=1):
-                            img = gr.Image(type="pil", label="Route visualization")
-                            with gr.Row():
-                                dl_assign = gr.File(label="Download assignments (CSV)")
-                                export_btn = gr.Button("Export latest assignments")
-
-                    with gr.Row():
-                        route_df = gr.Dataframe(label="Route assignments", wrap=True)
-                        metrics = gr.Markdown(label="Metrics summary")
-
-                with gr.TabItem("Upload CSV"):
-                    with gr.Row():
-                        with gr.Column(min_width=300):
-                            file = gr.File(label="Upload CSV (id,x,y,demand,tw_start,tw_end,service)")
-                            dl_tmp = gr.Button("Download template CSV")
-                            n_vehicles2 = gr.Slider(1, 50, value=5, step=1, label="Number of drivers")
-                            capacity2 = gr.Slider(1, 200, value=15, step=1, label="Vehicle capacity")
-                            speed2 = gr.Number(value=10.0, label="Vehicle speed (units/min)")
-                            run_btn2 = gr.Button("Optimize uploaded data", variant="primary")
-                        with gr.Column():
-                            img2 = gr.Image(type="pil", label="Route visualization")
-
-                    with gr.Row():
-                        route_df2 = gr.Dataframe(label="Route assignments", wrap=True)
-                        metrics2 = gr.Markdown(label="Metrics summary")
-                        dl_assign2 = gr.File(label="Download assignments (CSV)")
-
-    gr.Markdown(f"---\n{FOOTER}")
-
-    run_btn.click(
-        fn=run_generator,
-        inputs=[n_clients, n_vehicles, capacity, spread, demand_min, demand_max, seed, speed],
-        outputs=[img, route_df, metrics, dl_assign],
+            with gr.Box():
+                out_img = gr.Image(type='pil', label='Route map')
+            with gr.Row():
+                with gr.Column():
+                    out_table = gr.Dataframe(headers=None, label='Assignments (table)', interactive=False)
+                with gr.Column(min_width=300):
+                    out_metrics = gr.Markdown('No results yet', label='Metrics', elem_classes='metrics')
+                    out_csv = gr.File(label='Download CSV')
+
+    gr.Markdown(f"---
+{FOOTER}")
+
+    gen_btn.click(
+        fn=run_generate,
+        inputs=[n_clients, n_vehicles, capacity, spread, demand_min, demand_max, seed, speed, preset],
+        outputs=[out_img, out_table, out_metrics, gen_dl],
     )
 
-    run_btn2.click(
-        fn=run_csv,
-        inputs=[file, n_vehicles2, capacity2, speed2],
-        outputs=[img2, route_df2, metrics2, dl_assign2],
+    up_btn.click(
+        fn=run_upload,
+        inputs=[upload_file, up_vehicles, up_capacity, up_speed],
+        outputs=[out_img, out_table, out_metrics, up_dl],
     )
 
-    def _tmpl():
-        return download_template()
-
-    dl_tmp.click(fn=_tmpl, outputs=[gr.File()])
+    tmpl_btn.click(fn=download_template, inputs=[], outputs=[up_dl])
 
 if __name__ == '__main__':
     demo.launch()