Delete app.py

app.py

@@ -1,126 +0,0 @@
-import gradio as gr
-import pandas as pd
-import numpy as np
-import random
-
-regions = ["Riyadh", "Makkah", "Eastern", "Madinah", "Qassim", "Asir", "Tabuk", "Hail", "Northern",
-           "Jazan", "Najran", "Bahah", "Jawf"]
-income_bands = ["Low", "Mid", "High"]
-property_types = ["Land", "Off-plan", "Ready", "Self-build"]
-
-def default_subsidy_values():
-    base = 300_000
-    factor = 0.5
-    return {(r, i, p): int((base - 100_000 * income_bands.index(i)) * factor) for r in regions for i in income_bands for p in property_types}
-
-def monte_carlo_optimization(subsidies, budget_limit, target_contracts, supply_dict, interest_rate, demand_increase, n_trials=10000):
-    keys = list(subsidies.keys())
-    best_result = None
-    best_score = float('-inf')
-    fairness_penalty_weight = 10_000_000
-
-    demand_multiplier = 1 + (demand_increase / 100)
-    cost_multiplier = 1 + (interest_rate / 100)
-
-    for _ in range(n_trials):
-        contracts = {}
-        total_budget = 0
-        total_contracts = 0
-        shuffled_subs = {k: int(random.randint(150_000, 400_000) * cost_multiplier) for k in keys}
-        available_supply = {(r, p): supply_dict.get((p, r), 10000) for (p, r) in supply_dict}
-
-        for k in keys:
-            r, i, p = k
-            max_supply = available_supply.get((r, p), 10000)
-            max_demand = int(max_supply * demand_multiplier)
-            max_possible_contracts = int(min((budget_limit - total_budget) // shuffled_subs[k], max_demand)) if shuffled_subs[k] > 0 else 0
-            c = random.randint(0, max_possible_contracts) if max_possible_contracts > 0 else 0
-            contracts[k] = c
-            total_budget += shuffled_subs[k] * c
-            total_contracts += c
-
-        if total_budget > budget_limit:
-            continue
-
-        achieved_regions = set(r for (r, i, p) in contracts if contracts[(r, i, p)] > 0)
-        achieved_income = set(i for (r, i, p) in contracts if contracts[(r, i, p)] > 0)
-        achieved_props = set(p for (r, i, p) in contracts if contracts[(r, i, p)] > 0)
-        fairness_penalty = (
-            len(regions) - len(achieved_regions) +
-            len(income_bands) - len(achieved_income) +
-            len(property_types) - len(achieved_props)
-        ) * fairness_penalty_weight
-
-        score = total_contracts - fairness_penalty
-
-        if score > best_score:
-            best_score = score
-            best_result = (contracts, total_budget, total_contracts, shuffled_subs)
-
-    return best_result if best_result else ({}, 0, 0, subsidies)
-
-def build_app():
-    supply_costs = {(p, r): random.randint(1000, 10000) for p in ["Land", "Off-plan"] for r in regions}
-    with gr.Blocks() as demo:
-        gr.Markdown("# 🏨 Strategic Gears Housing Simulator – Auto Optimization")
-
-        with gr.Row():
-            ir = gr.Slider(0, 10, 5, label="Interest Rate (%)")
-            dp = gr.Slider(0, 100, 10, label="Demand Increase (%)")
-            n = gr.Slider(100, 10000, 1000, step=100, label="Number of Simulations")
-            budget_limit = gr.Slider(10_000_000, 1_000_000_000, 500_000_000, step=10_000_000, label="Budget Limit (SAR)")
-            target_contracts = gr.Slider(1000, 20000, 5000, step=100, label="Target Contracts")
-
-        supply_inputs = {}
-        with gr.Accordion("Supply Inputs by Region and Property Type", open=False):
-            for p in ["Land", "Off-plan"]:
-                with gr.Accordion(p, open=False):
-                    for r in regions:
-                        supply_inputs[(p, r)] = gr.Slider(minimum=1, maximum=10000, value=supply_costs[(p, r)], step=1, label=f"{r} {p} Supply")
-
-        run = gr.Button("Run Simulation")
-
-        summary = gr.Markdown("Optimization summary will appear here.")
-        df_subsidy_policy = gr.Dataframe(label="1️⃣ Recommended Subsidy Support (SAR) - Sorted High to Low")
-        df_contract_summary = gr.Dataframe(label="2️⃣ Contract Distribution - Sorted High to Low")
-        df_budget_summary = gr.Dataframe(label="3️⃣ Budget Distribution - Sorted High to Low")
-        df_discount_table = gr.Dataframe(label="Discount Table (Land & Off-plan)")
-
-        def run_sim(interest, demand, sims, budget, target, *supplies):
-            supply_dict = {(p, r): supplies[i] for i, (p, r) in enumerate(supply_inputs)}
-            result, total_bgt, total_con, final_subs = monte_carlo_optimization(
-                default_subsidy_values(), budget, target, supply_dict, interest, demand, sims)
-
-            df = pd.DataFrame([{
-                "Region": r, "Income Band": i, "Property Type": p,
-                "Contracts": result.get((r, i, p), 0),
-                "Subsidy (SAR)": final_subs[(r, i, p)],
-                "Budget (SAR)": result.get((r, i, p), 0) * final_subs[(r, i, p)]
-            } for (r, i, p) in final_subs])
-
-            subsidy_df = df[["Region", "Income Band", "Property Type", "Subsidy (SAR)"]].sort_values(by="Subsidy (SAR)", ascending=False)
-            contract_df = df[["Region", "Income Band", "Property Type", "Contracts"]].sort_values(by="Contracts", ascending=False)
-            budget_df = df[["Region", "Income Band", "Property Type", "Budget (SAR)"]].sort_values(by="Budget (SAR)", ascending=False)
-
-            df_discount = pd.DataFrame([{
-                "Region": r, "Property Type": p,
-                "Discount (SAR)": random.randint(50_000, 300_000)
-            } for p in ["Land", "Off-plan"] for r in regions])
-
-            summary_text = f"""
-            ### 🧾 Optimization Summary
-            - **Total Budget Used:** {total_bgt:,.0f} SAR  
-            - **Budget Utilization:** {(total_bgt / budget) * 100:.2f}%  
-            - **Total Contracts Signed:** {total_con:,}  
-            - **Target Achievement:** {(total_con / target) * 100:.2f}%  
-            """
-            return summary_text, subsidy_df, contract_df, budget_df, df_discount
-
-        run.click(fn=run_sim,
-                  inputs=[ir, dp, n, budget_limit, target_contracts] + list(supply_inputs.values()),
-                  outputs=[summary, df_subsidy_policy, df_contract_summary, df_budget_summary, df_discount_table])
-
-    return demo
-
-app = build_app()
-app.launch()