Update agents/reasoner.py

agents/reasoner.py

@@ -1,6 +1,11 @@
 import pandas as pd
 import numpy as np
+import matplotlib.pyplot as plt
+import io
 
+# ============================================================
+#  SLOTING OPTIMIZATION — PHASE 1
+# ============================================================
 
 def run_slotting_analysis(message, slotting_df):
     reasoning_steps = []
@@ -30,7 +35,6 @@ def run_slotting_analysis(message, slotting_df):
     # ---------------------------------------------------------
     # 3️⃣ Compute Slotting Score
     # ---------------------------------------------------------
-    # Weighted: 60% velocity + 40% frequency
     df["Score"] = (0.6 * df["VelocityNorm"]) + (0.4 * df["FreqNorm"])
     reasoning_steps.append("Computed weighted slotting score (60% velocity, 40% frequency).")
 
@@ -39,7 +43,6 @@ def run_slotting_analysis(message, slotting_df):
     # ---------------------------------------------------------
     df = df.sort_values("Score", ascending=False).reset_index(drop=True)
 
-    # Prime aisles 1–5 → best locations
     aisles = np.arange(1, len(df) + 1)
     racks = np.linspace(1, 20, len(df)).astype(int)
 
@@ -56,3 +59,53 @@ def run_slotting_analysis(message, slotting_df):
     )
 
     return explanation, df
+
+
+# ============================================================
+#  PICKING ROUTE OPTIMIZATION — PHASE 1
+# ============================================================
+
+def run_picking_optimization(message, picking_df):
+    reasoning_steps = []
+
+    df = picking_df.copy()
+
+    # ---------------------------------------------------------
+    # 1️⃣ Convert Aisle–Rack to coordinates
+    # ---------------------------------------------------------
+    df["x"] = df["Aisle"]
+    df["y"] = df["Rack"]
+    reasoning_steps.append("Converted Aisle–Rack values into x–y coordinate grid.")
+
+    # ---------------------------------------------------------
+    # 2️⃣ Compute Manhattan distance
+    # ---------------------------------------------------------
+    df["Distance"] = df["x"].abs() + df["y"].abs()
+    df = df.sort_values("Distance").reset_index(drop=True)
+    reasoning_steps.append("Calculated Manhattan distance and sorted for optimal walk order.")
+
+    # ---------------------------------------------------------
+    # 3️⃣ Plot the route
+    # ---------------------------------------------------------
+    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")
+
+    # Save image to BytesIO buffer
+    buffer = io.BytesIO()
+    plt.savefig(buffer, format="png")
+    buffer.seek(0)
+    plt.close()
+
+    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, buffer