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import matplotlib.pyplot as plt
import io
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 from start (0,0)
# ---------------------------------------------------------
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 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")
# Save image to memory for Gradio
buf = io.BytesIO()
plt.savefig(buf, format="png")
buf.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, buf
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