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import gradio as gr
import pandas as pd
from agents.brain import AutoWarehouseAgent
agent = AutoWarehouseAgent()
# -------------------------------------------------------------
# Convert UI DataFrame input to Pandas
# -------------------------------------------------------------
def df_from_input(df):
return pd.DataFrame(df) if df else pd.DataFrame()
# -------------------------------------------------------------
# MAIN AGENT RUNNER
# -------------------------------------------------------------
def run_agent(message, slotting_df, picking_df):
print("📥 INPUT MESSAGE:", message)
print("📦 SLOT DATA RECEIVED:", slotting_df)
print("🚚 PICKING DATA RECEIVED:", picking_df)
try:
raw = agent.run(message, slotting_df, picking_df)
print("🔍 RAW RESULT FROM AGENT:", raw)
# -----------------------------------------------------
# NORMALIZE OUTPUT DICTIONARY
# -----------------------------------------------------
if isinstance(raw, str):
# If only text returned → wrap it
result = {
"report": raw,
"route_image": None,
"slotting_table": pd.DataFrame(),
}
elif isinstance(raw, dict):
# Normalize missing fields
result = {
"report": raw.get("report", "No report generated."),
"route_image": raw.get("route_image", None),
"slotting_table": raw.get("slotting_table", pd.DataFrame()),
}
else:
# Unexpected type
result = {
"report": "⚠️ Invalid agent output format.",
"route_image": None,
"slotting_table": pd.DataFrame(),
}
# UI expects EXACTLY 3 outputs
return (
result["report"],
result["route_image"],
result["slotting_table"],
)
except Exception as e:
import traceback
print("❌ ERROR OCCURRED:")
traceback.print_exc()
return (
f"### ❌ Error\n{str(e)}",
None,
pd.DataFrame(),
)
# -------------------------------------------------------------
# BUILD UI
# -------------------------------------------------------------
def build_ui():
with gr.Blocks() as demo:
# Title
gr.Markdown(
"""
<h1 style='color:#FF6A00;text-align:center;'>
Procelevate Autonomous Warehouse Operator (v2.0)
</h1>
<p style='text-align:center;font-size:16px;'>
Slotting • Picking • Forecasting • Replenishment • Rebalancing • Workforce • Dock Scheduling
</p>
"""
)
# User input
query = gr.Textbox(
label="Enter your warehouse request",
placeholder="Examples: 'Rebalance inventory', 'Forecast next 7 days', 'Optimize slotting', 'How many workers needed?'"
)
# Slotting input table
gr.Markdown("### 📦 Slotting Data (SKU Master)")
slot_df = gr.Dataframe(
headers=["SKU", "Velocity", "Frequency"],
value=[
["A123", "Fast", 120],
["B555", "Medium", 60],
["C888", "Slow", 8],
],
)
# Picking input table
gr.Markdown("### 🚚 Picking Data")
pick_df = gr.Dataframe(
headers=["Aisle", "Rack"],
value=[[5, 14], [3, 10], [12, 7]],
)
# Run button
btn = gr.Button("Run Warehouse Agent", variant="primary")
# Outputs
out_report = gr.Markdown(label="📄 Report")
out_route = gr.Image(label="📊 Charts / Diagrams",type="pil")
out_slot = gr.Dataframe(label="📘 Output Table")
# Execution wiring
btn.click(
run_agent,
inputs=[query, slot_df, pick_df],
outputs=[out_report, out_route, out_slot],
)
return demo
# -------------------------------------------------------------
# LAUNCH APP
# -------------------------------------------------------------
demo = build_ui()
demo.launch()
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