Create app.py

app.py

@@ -0,0 +1,144 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+
+PRIMARY_COLOR = "#FF6A00"
+
+# ==============================
+# WAREHOUSE MODEL PARAMETERS
+# ==============================
+NUM_AISLES = 30
+NUM_RACKS = 20
+
+FAST_ZONE = range(1, 6)     # aisles 1–5
+MID_ZONE = range(6, 16)     # aisles 6–15
+SLOW_ZONE = range(16, 31)   # aisles 16–30
+
+
+# ==============================
+# SLOT RECOMMENDATION ENGINE
+# ==============================
+def recommend_slot(velocity):
+    """
+    Assign aisle zone based on SKU velocity.
+    """
+    velocity = str(velocity).lower()
+
+    if velocity in ["fast", "high", "f"]:
+        aisle = np.random.choice(list(FAST_ZONE))
+        reason = "Fast-moving SKU — Assigned close to dispatch to reduce pick time."
+
+    elif velocity in ["medium", "mid", "m"]:
+        aisle = np.random.choice(list(MID_ZONE))
+        reason = "Medium-moving SKU — Assigned to middle zone to balance travel distance."
+
+    else:
+        aisle = np.random.choice(list(SLOW_ZONE))
+        reason = "Slow-moving SKU — Assigned to back aisles to avoid congestion."
+
+    rack = np.random.randint(1, NUM_RACKS + 1)
+
+    return aisle, rack, reason
+
+
+# ==============================
+# MAIN OPTIMIZATION FUNCTION
+# ==============================
+def optimize_slotting(df):
+    if df is None or len(df) == 0:
+        return None, "⚠️ Please upload SKU data."
+
+    results = []
+
+    for _, row in df.iterrows():
+        sku = row["SKU"]
+        velocity = row["Velocity"]
+        frequency = row.get("Frequency", "")
+
+        aisle, rack, reason = recommend_slot(velocity)
+
+        results.append({
+            "SKU": sku,
+            "Velocity": velocity,
+            "Frequency": frequency,
+            "Suggested Aisle": aisle,
+            "Suggested Rack": rack,
+            "Reason": reason
+        })
+
+    output_df = pd.DataFrame(results)
+    return output_df, "✅ Optimization Completed"
+
+
+# ==============================
+# GRADIO UI
+# ==============================
+def build_interface():
+
+    with gr.Blocks(css=".gr-button {background-color: %s !important;}" % PRIMARY_COLOR) as demo:
+
+        gr.Markdown(
+            "<h1 style='color:%s'>Procelevate Inventory Slotting Optimizer</h1>"
+            "<h3>AI-powered SKU placement engine to reduce picking time & congestion.</h3>"
+            % PRIMARY_COLOR
+        )
+
+        gr.Markdown("### 📦 Upload SKU File or Enter Data")
+
+        template_df = pd.DataFrame({
+            "SKU": ["A123", "B555", "C888"],
+            "Velocity": ["Fast", "Medium", "Slow"],
+            "Frequency": [120, 60, 5]
+        })
+
+        sku_table = gr.Dataframe(
+            value=template_df,
+            headers=["SKU", "Velocity", "Frequency"],
+            row_count=5,
+            col_count=3,
+            wrap=True
+        )
+
+        optimize_btn = gr.Button("Optimize Slotting", size="lg")
+
+        result_table = gr.Dataframe(
+            headers=["SKU", "Velocity", "Frequency",
+                     "Suggested Aisle", "Suggested Rack", "Reason"],
+            row_count=5
+        )
+
+        business_insight = gr.Markdown("")
+
+        def run(df):
+            result, status = optimize_slotting(df)
+            if result is None:
+                return None, status
+
+            # Business insight summary
+            fast = len(result[result["Velocity"].str.lower() == "fast"])
+            medium = len(result[result["Velocity"].str.lower() == "medium"])
+            slow = len(result[result["Velocity"].str.lower() == "slow"])
+
+            insight = f"""
+### 📊 Business Insight Summary
+
+- **Fast Movers:** {fast} SKUs moved closest to dispatch for quicker turnaround.  
+- **Medium Movers:** {medium} SKUs distributed in central aisles to balance traffic.  
+- **Slow Movers:** {slow} SKUs placed in far aisles to reduce congestion.  
+
+This improves:
+- 🚶 **Walking distance reduction**
+- 🚚 **Faster order fulfillment**
+- 🎯 **Better space utilization**
+- 🔄 **Lower aisle congestion**
+            """
+
+            return result, insight
+
+        optimize_btn.click(run, inputs=[sku_table], outputs=[result_table, business_insight])
+
+    return demo
+
+
+demo = build_interface()
+demo.launch()