app_improved.py

# app_improved.py — Redesigned UI

import io
import os
import json
import tempfile
import pandas as pd

# Compatibility shim for huggingface_hub
try:
    import huggingface_hub as _hf
except Exception:
    _hf = None

if _hf is not None:
    if not hasattr(_hf, "HfFolder"):
        class _HfFolder:
            @staticmethod
            def path(*args, **kwargs):
                return None
        _hf.HfFolder = _HfFolder
    if not hasattr(_hf, "whoami"):
        def _whoami(token=None):
            return {}
        _hf.whoami = _whoami

import gradio as gr
from solver import (
    generate_random_instance,
    solve_vrp_tw,
    plot_solution,
    parse_uploaded_csv,
    make_template_dataframe,
)

# ---------- 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:#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}
"""

# ---------- 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)

    df = generate_random_instance(
        n_clients=int(n_clients),
        n_vehicles=int(n_vehicles),
        capacity=int(capacity),
        spread=float(spread),
        demand_min=int(demand_min),
        demand_max=int(demand_max),
        seed=int(seed),
    )
    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, csv_path


def run_upload(file, n_vehicles, capacity, speed):
    if file is None:
        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)
    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, csv_path


def download_template():
    df = make_template_dataframe()
    return _write_df_to_temp(df)

# ---------- UI
with gr.Blocks(title=TITLE, css=CUSTOM_CSS) as demo:
    with gr.Row():
        with gr.Column(scale=1):
            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.Column(scale=2):
            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],
    )

    up_btn.click(
        fn=run_upload,
        inputs=[upload_file, up_vehicles, up_capacity, up_speed],
        outputs=[out_img, out_table, out_metrics, up_dl],
    )

    tmpl_btn.click(fn=download_template, inputs=[], outputs=[up_dl])

if __name__ == '__main__':
    demo.launch()