Update app.py

app.py

@@ -1,42 +1,141 @@
-import streamlit as st
-import numpy as np
-import matplotlib.pyplot as plt
-from solver import clarke_wright
-
-st.set_page_config(page_title="Ride Sharing Optimizer", layout="wide")
-
-st.title("🚐 Ride Sharing Optimizer (VRP)")
-
-st.markdown("""
-Upload your customer data and optimize vehicle routes with capacity constraints.
-""")
-
-# Input params
-num_customers = st.slider("Number of Customers", 5, 30, 10)
-vehicle_capacity = st.slider("Vehicle Capacity", 5, 50, 15)
-
-# Random data generation
-np.random.seed(42)
-coords = [(50, 50)] + [(np.random.randint(0, 100), np.random.randint(0, 100)) for _ in range(num_customers)]
-demands = [0] + [np.random.randint(1, 5) for _ in range(num_customers)]
-
-if st.button("Optimize Routes"):
-    routes, dist = clarke_wright(coords, demands, vehicle_capacity)
-
-    fig, ax = plt.subplots(figsize=(6,6))
-    
-    colors = plt.cm.tab10.colors
-    for idx, route in enumerate(routes):
-        xs, ys = zip(*[coords[i] for i in route])
-        ax.plot(xs, ys, marker="o", color=colors[idx % len(colors)], label=f"Vehicle {idx+1}")
-        for i, (x,y) in enumerate(coords):
-            ax.text(x, y, str(i), fontsize=8, ha="right")
-    
-    ax.scatter(coords[0][0], coords[0][1], c="red", s=100, marker="s", label="Depot")
-    ax.set_title("Optimized Vehicle Routes")
-    ax.legend()
-    
-    st.pyplot(fig)
-    
-    total_distance = sum(sum(dist[route[i]][route[i+1]] for i in range(len(route)-1)) for route in routes)
-    st.success(f"✅ Total distance: {total_distance:.2f}")
+import io
+import json
+import gradio as gr
+import pandas as pd
+from solver import (
+    generate_random_instance,
+    solve_vrp,
+    plot_solution,
+    parse_uploaded_csv,
+    make_template_dataframe,
+)
+
+TITLE = "Ride-Sharing Optimizer (Capacitated VRP) — Gradio Demo"
+DESC = """
+This demo assigns **stops (riders)** to **drivers (vehicles)** with a simple, fast heuristic:
+**Sweep clustering** → **Greedy routing** → **2-opt improvement**.  
+You can **generate a sample** dataset or **upload a CSV** with columns:
+`id,x,y,demand,tw_start,tw_end,service`.
+
+- **Capacity** = max riders per vehicle (sum of `demand` per route).
+- **Time windows** are *soft* (violations are reported in metrics).
+- Distances are Euclidean on the X-Y plane for clarity.
+
+Tip: Start with the generator, then switch to your CSV.
+"""
+
+FOOTER = "Made with ❤️ using Gradio. No native dependencies; runs quickly on Spaces."
+
+def run_generator(
+    n_clients, n_vehicles, capacity, spread, demand_min, demand_max, seed
+):
+    df = generate_random_instance(
+        n_clients=n_clients,
+        n_vehicles=n_vehicles,
+        capacity=capacity,
+        spread=spread,
+        demand_min=demand_min,
+        demand_max=demand_max,
+        seed=seed,
+    )
+    depot = (0.0, 0.0)
+    sol = solve_vrp(
+        df, depot=depot, n_vehicles=n_vehicles, capacity=capacity, speed=1.0
+    )
+    fig = plot_solution(df, sol, depot=depot)
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png", bbox_inches="tight", dpi=160)
+    buf.seek(0)
+
+    # Route table
+    route_table = sol["assignments_table"]
+    metrics = json.dumps(sol["metrics"], indent=2)
+
+    return buf, route_table, metrics, df
+
+def run_csv(
+    file, n_vehicles, capacity
+):
+    if file is None:
+        raise gr.Error("Please upload a CSV first.")
+    try:
+        df = parse_uploaded_csv(file)
+    except Exception as e:
+        raise gr.Error(f"CSV parsing error: {e}")
+
+    depot = (0.0, 0.0)
+    sol = solve_vrp(
+        df, depot=depot, n_vehicles=n_vehicles, capacity=capacity, speed=1.0
+    )
+    fig = plot_solution(df, sol, depot=depot)
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png", bbox_inches="tight", dpi=160)
+    buf.seek(0)
+
+    route_table = sol["assignments_table"]
+    metrics = json.dumps(sol["metrics"], indent=2)
+    return buf, route_table, metrics
+
+def download_template():
+    df = make_template_dataframe()
+    return df
+
+with gr.Blocks(title=TITLE) as demo:
+    gr.Markdown(f"# {TITLE}")
+    gr.Markdown(DESC)
+
+    with gr.Tab("🔀 Generate sample"):
+        with gr.Row():
+            with gr.Column():
+                n_clients = gr.Slider(5, 200, value=30, step=1, label="Number of riders (clients)")
+                n_vehicles = gr.Slider(1, 20, value=4, step=1, label="Number of drivers (vehicles)")
+                capacity = gr.Slider(1, 50, value=10, step=1, label="Vehicle capacity (sum of demand)")
+                spread = gr.Slider(10, 200, value=50, step=1, label="Spatial spread (larger = wider map)")
+                demand_min = gr.Slider(1, 5, value=1, step=1, label="Min demand per stop")
+                demand_max = gr.Slider(1, 10, value=3, step=1, label="Max demand per stop")
+                seed = gr.Slider(0, 9999, value=42, step=1, label="Random seed")
+                run_btn = gr.Button("🚗 Generate & Optimize", variant="primary")
+            with gr.Column():
+                img = gr.Image(label="Route Visualization", interactive=False)
+        with gr.Row():
+            route_df = gr.Dataframe(label="Route assignments (per stop)", wrap=True)
+            metrics = gr.Code(label="Metrics (JSON)")
+        with gr.Accordion("Show generated dataset", open=False):
+            data_out = gr.Dataframe(label="Generated input data")
+
+        run_btn.click(
+            fn=run_generator,
+            inputs=[n_clients, n_vehicles, capacity, spread, demand_min, demand_max, seed],
+            outputs=[img, route_df, metrics, data_out],
+        )
+
+    with gr.Tab("📄 Upload CSV"):
+        with gr.Row():
+            with gr.Column():
+                file = gr.File(label="Upload CSV (id,x,y,demand,tw_start,tw_end,service)")
+                dl_tmp = gr.Button("Get CSV Template")
+                n_vehicles2 = gr.Slider(1, 50, value=5, step=1, label="Number of drivers (vehicles)")
+                capacity2 = gr.Slider(1, 200, value=15, step=1, label="Vehicle capacity (sum of demand)")
+                run_btn2 = gr.Button("📈 Optimize uploaded data", variant="primary")
+            with gr.Column():
+                img2 = gr.Image(label="Route Visualization", interactive=False)
+
+        with gr.Row():
+            route_df2 = gr.Dataframe(label="Route assignments (per stop)")
+            metrics2 = gr.Code(label="Metrics (JSON)")
+
+        run_btn2.click(
+            fn=run_csv,
+            inputs=[file, n_vehicles2, capacity2],
+            outputs=[img2, route_df2, metrics2],
+        )
+
+        def _tmpl():
+            return gr.File.update(value=None), download_template()
+
+        dl_tmp.click(fn=_tmpl, outputs=[file, route_df2], inputs=None)
+
+    gr.Markdown(f"---\n{FOOTER}")
+
+if __name__ == "__main__":
+    demo.launch()