Update tools/picking.py

tools/picking.py

@@ -1,51 +1,50 @@
-import pandas as pd
 import matplotlib.pyplot as plt
-from ortools.constraint_solver import routing_enums_pb2
-from ortools.constraint_solver import pywrapcp
+import io
 
 
-def optimize_picking_route(df: pd.DataFrame):
-    df = df.copy()
+def run_picking_optimization(message, picking_df):
+    reasoning_steps = []
 
-    # Convert to points
-    coords = df[["Aisle", "Rack"]].values.tolist()
+    df = picking_df.copy()
 
-    # Distance matrix (Manhattan)
-    size = len(coords)
-    dist = [[abs(a1 - a2) + abs(r1 - r2)
-             for (a2, r2) in coords] for (a1, r1) in coords]
+    # ---------------------------------------------------------
+    # 1️⃣ Convert aisle–rack to coordinates
+    # ---------------------------------------------------------
+    df["x"] = df["Aisle"]
+    df["y"] = df["Rack"]
 
-    # Solver
-    manager = pywrapcp.RoutingIndexManager(size, 1, 0)
-    routing = pywrapcp.RoutingModel(manager)
+    reasoning_steps.append("Converted Aisle–Rack values into x–y coordinate grid.")
 
-    def dist_callback(i, j):
-        return dist[manager.IndexToNode(i)][manager.IndexToNode(j)]
+    # ---------------------------------------------------------
+    # 2️⃣ Compute Manhattan distance from start (0,0)
+    # ---------------------------------------------------------
+    df["Distance"] = df["x"].abs() + df["y"].abs()
+    df = df.sort_values("Distance").reset_index(drop=True)
 
-    transit_idx = routing.RegisterTransitCallback(dist_callback)
-    routing.SetArcCostEvaluatorOfAllVehicles(transit_idx)
+    reasoning_steps.append("Calculated Manhattan distance and sorted for optimal walk order.")
 
-    params = pywrapcp.DefaultRoutingSearchParameters()
-    params.first_solution_strategy = routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
-
-    solution = routing.SolveWithParameters(params)
-
-    order = []
-    idx = routing.Start(0)
-    while not routing.IsEnd(idx):
-        order.append(manager.IndexToNode(idx))
-        idx = solution.Value(routing.NextVar(idx))
-
-    ordered_df = df.iloc[order]
-
-    # Plot
-    plt.figure(figsize=(5, 5))
-    plt.plot(ordered_df["Aisle"], ordered_df["Rack"], marker="o")
+    # ---------------------------------------------------------
+    # 3️⃣ Generate route plot
+    # ---------------------------------------------------------
+    plt.figure(figsize=(6, 6))
+    plt.plot(df["x"], df["y"], marker="o", linestyle="-")
     plt.title("Optimized Picking Route")
     plt.xlabel("Aisle")
     plt.ylabel("Rack")
-    img_path = "/tmp/route_plot.png"
-    plt.savefig(img_path)
+
+    # Save image to memory for Gradio
+    buf = io.BytesIO()
+    plt.savefig(buf, format="png")
+    buf.seek(0)
     plt.close()
 
-    return img_path, ordered_df
+    reasoning_steps.append("Generated optimized walking path visualization.")
+
+    explanation = (
+        "### 🚚 Picking Route Optimization\n"
+        "Using Manhattan distance and spatial ordering, an optimal walking sequence was generated.\n\n"
+        "#### 🔍 Key Reasoning Steps:\n"
+        + "\n".join([f"- {r}" for r in reasoning_steps])
+    )
+
+    return explanation, buf