Delete app.py

app.py

@@ -1,129 +0,0 @@
-import gradio as gr
-import pandas as pd
-
-from agents.brain import AutoWarehouseAgent
-
-
-PRIMARY_COLOR = "#FF6A00"
-
-
-# ----------------------------------------------------------------------
-# Load agent
-# ----------------------------------------------------------------------
-agent = AutoWarehouseAgent()
-
-
-# ----------------------------------------------------------------------
-# Utility: Convert Gradio DataFrame to Pandas
-# ----------------------------------------------------------------------
-def df_from_gr_input(df):
-    if df is None:
-        return pd.DataFrame()
-    return pd.DataFrame(df)
-
-
-# ----------------------------------------------------------------------
-# Main function called by Gradio
-# ----------------------------------------------------------------------
-def run_agent(user_query, slotting_table, picking_table):
-    slotting_df = df_from_gr_input(slotting_table)
-    picking_df = df_from_gr_input(picking_table)
-
-    result = agent.run(
-        user_query=user_query,
-        slotting_df=slotting_df,
-        picking_df=picking_df
-    )
-
-    return result
-
-
-# ----------------------------------------------------------------------
-# UI Layout
-# ----------------------------------------------------------------------
-def build_ui():
-
-    with gr.Blocks() as demo:
-
-        gr.Markdown(
-            f"""
-            <h1 style='color:{PRIMARY_COLOR}; font-weight:900;'>Procelevate Autonomous Warehouse Operator</h1>
-            <p style='font-size:18px'>
-            Enterprise-grade Agentic AI that optimizes slotting, picking, and warehouse operations.
-            </p>
-            """,
-        )
-
-        # ------------------- USER INPUT -------------------- #
-        user_query = gr.Textbox(
-            label="Ask the Warehouse Operator",
-            placeholder="e.g., Generate full warehouse operations report",
-        )
-
-        gr.Markdown("### Slotting Data")
-        slotting_table = gr.Dataframe(
-            headers=["SKU", "Velocity", "Frequency"],
-            datatype=["str", "str", "number"],
-            row_count=5,
-            col_count=3,
-            value=[
-                ["A123", "Fast", 120],
-                ["B555", "Medium", 60],
-                ["C888", "Slow", 8],
-            ],
-        )
-
-        gr.Markdown("### Picking Path Data")
-        picking_table = gr.Dataframe(
-            headers=["Aisle", "Rack"],
-            datatype=["number", "number"],
-            row_count=5,
-            col_count=2,
-            value=[
-                [5, 14],
-                [3, 10],
-                [12, 7],
-            ],
-        )
-
-        run_button = gr.Button(
-            "Run Agent",
-            variant="primary",
-            elem_id="run-btn"
-        )
-
-        output_report = gr.Markdown(
-            value="",
-            elem_id="report-area"
-        )
-
-        # ------------------- CSS -------------------- #
-        gr.HTML(
-            f"""
-            <style>
-                #run-btn {{
-                    background-color: {PRIMARY_COLOR} !important;
-                    color: white !important;
-                    height: 60px;
-                    font-size: 20px;
-                    border-radius: 8px;
-                }}
-            </style>
-            """
-        )
-
-        # ------------------- LINK BUTTON -------------------- #
-        run_button.click(
-            fn=run_agent,
-            inputs=[user_query, slotting_table, picking_table],
-            outputs=[output_report],
-        )
-
-    return demo
-
-
-# ----------------------------------------------------------------------
-# Launch App
-# ----------------------------------------------------------------------
-demo = build_ui()
-demo.launch()