Update solver.py

solver.py

@@ -45,37 +45,37 @@ def parse_uploaded_csv(file) -> pd.DataFrame:
 
 
 
-    def generate_random_instance(
-        n_clients=15,
-        n_vehicles=4,
-        capacity=7,
-        spread=10,           # smaller city area
-        demand_min=1,
-        demand_max=3,
-        seed=42,
-    ):
-        rng = np.random.default_rng(seed)
-        xs = rng.uniform(-spread, spread, size=n_clients)
-        ys = rng.uniform(-spread, spread, size=n_clients)
-        demands = rng.integers(demand_min, demand_max + 1, size=n_clients)
-    
-        # realistic and tight time windows
-        tw_start = rng.integers(0, 40, size=n_clients)
-        tw_end = tw_start + rng.integers(15, 25, size=n_clients)
-    
-        # short service time (1 minute)
-        service = np.ones(n_clients, dtype=int)
-    
-        df = pd.DataFrame({
-            "id": [f"C{i+1}" for i in range(n_clients)],
-            "x": xs,
-            "y": ys,
-            "demand": demands,
-            "tw_start": tw_start,
-            "tw_end": tw_end,
-            "service": service
-        })
-        return df
+def generate_random_instance(
+    n_clients=15,
+    n_vehicles=4,
+    capacity=7,
+    spread=10,           # smaller city area
+    demand_min=1,
+    demand_max=3,
+    seed=42,
+):
+    rng = np.random.default_rng(seed)
+    xs = rng.uniform(-spread, spread, size=n_clients)
+    ys = rng.uniform(-spread, spread, size=n_clients)
+    demands = rng.integers(demand_min, demand_max + 1, size=n_clients)
+
+    # realistic and tight time windows
+    tw_start = rng.integers(0, 40, size=n_clients)
+    tw_end = tw_start + rng.integers(15, 25, size=n_clients)
+
+    # short service time (1 minute)
+    service = np.ones(n_clients, dtype=int)
+
+    df = pd.DataFrame({
+        "id": [f"C{i+1}" for i in range(n_clients)],
+        "x": xs,
+        "y": ys,
+        "demand": demands,
+        "tw_start": tw_start,
+        "tw_end": tw_end,
+        "service": service
+    })
+    return df