Update app_v3.py

app_v3.py

@@ -147,45 +147,3 @@ def run_pipeline(file, scenario):
     if file is None:
         _err("No file uploaded")
     df_hist = load_csv(file); fc, errs = baseline_forecast(df_hist)
-    drivers_base  = {"rev_growth_pct":0.0,  "gm_bps":0,   "opex_infl_pct":0.0,  "fx_pct":0.0, "dso":60, "dpo":45, "dio":60, "dep":0.05, "interest":0.01, "tax_rate":0.25}
-    drivers_best  = {**drivers_base, "rev_growth_pct":0.10,  "gm_bps":200,  "opex_infl_pct":-0.05, "fx_pct":0.02}
-    drivers_worst = {**drivers_base, "rev_growth_pct":-0.10, "gm_bps":-200, "opex_infl_pct":0.10,  "fx_pct":-0.03}
-
-    if scenario == "Baseline":
-        out = apply_scenario(fc, drivers_base)
-        plot_img = Image.open(plot_series(out, "Baseline"))
-        meta = {"audit": audit_row(drivers_base, "Baseline"), "baseline_warnings": errs or None}
-        return plot_img, out.to_csv(index=False).encode(), json.dumps(meta, indent=2)
-
-    base_df  = apply_scenario(fc, drivers_base)
-    best_df  = apply_scenario(fc, drivers_best)
-    worst_df = apply_scenario(fc, drivers_worst)
-
-    ok_b = all(check_bounds(d)[0] for d in [drivers_base, drivers_best, drivers_worst])
-    ok_cons = all(check_conservation(d) for d in [base_df, best_df, worst_df])
-    ok_mono, eb, ca = check_monotonicity(base_df, worst_df, best_df)
-    status = {"bounds": ok_b, "conservation": ok_cons, "monotonicity": ok_mono, "baseline_warnings": errs or None}
-
-    out = base_df if scenario == "Base" else best_df if scenario == "Best" else worst_df
-    plot_img = Image.open(plot_series(out, f"Scenario: {scenario}"))
-    meta = {"scenario": scenario, "totals": {"EBITDA": eb, "Cash": ca}, "status": status}
-    return plot_img, out.to_csv(index=False).encode(), json.dumps(meta, indent=2)
-
-with gr.Blocks(title="Corporate Forecast Agent — Detailed Errors") as demo:
-    gr.Markdown("## Corporate Forecast Agent — Best / Base / Worst")
-    gr.Markdown("Upload CSV with columns: date,revenue,cogs,opex (monthly). Errors now include detailed JSON.")
-    file     = gr.File(file_types=[".csv"], label="Upload CSV")
-    scenario = gr.Radio(["Baseline","Best","Base","Worst"], value="Baseline", label="Scenario")
-    btn      = gr.Button("Run")
-    img  = gr.Image(type="pil", label="Plots")
-    dl   = gr.File(label="Download forecast.csv")
-    meta = gr.JSON(label="Validation & Diagnostics")
-    def _run(file_obj, scen):
-        plot_img, csv_bytes, meta_json = run_pipeline(file_obj.name if file_obj else None, scen)
-        return plot_img, ("forecast.csv", csv_bytes), meta_json
-    btn.click(_run, inputs=[file, scenario], outputs=[img, dl, meta])
-
-demo.queue()
-if __name__ == "__main__":
-    demo.launch()
-