Update app.py

app.py

@@ -113,7 +113,7 @@ def optimize_supply_tool(forecast_json: str) -> str:
         c[i_sell(p)] -= price[p]
         c[i_einv(p)] += 0.0
         bounds[i_prod(p)] = (0, None)
-        bounds[i_sell(p)] = (0, demand[p])
+        bounds[i_sell(p)] = (0, demand[p])  # demand as an upper bound (no backorders)
         bounds[i_einv(p)] = (0, None)
     for r in RMs:
         c[i_pur(r)]  += rm_cost[r]
@@ -221,26 +221,35 @@ SYSTEM_PLAN = (
     "Return: {'forecast': <json>, 'plan': <json>, 'md61': <json>}"
 )
 
-def run_agentic(horizon, plant, use_demo, file_obj):
+def run_agentic(h, plant, demo_flag, file_obj):
     agent = make_agent()
     if file_obj is not None:
         path = file_obj.name
         prompt = (f"{SYSTEM_PLAN}\n"
-                  f"Use forecast_tool(horizon_months={int(horizon)}, use_demo=False, history_csv_path='{path}'). "
+                  f"Use forecast_tool(horizon_months={int(h)}, use_demo=False, history_csv_path='{path}'). "
                   f"Then run the other two steps as specified. Return only the final JSON.")
     else:
         prompt = (f"{SYSTEM_PLAN}\n"
-                  f"Use forecast_tool(horizon_months={int(horizon)}, use_demo={bool(use_demo)}). "
+                  f"Use forecast_tool(horizon_months={int(h)}, use_demo={bool(demo_flag)}). "
                   f"Then run the other two steps as specified. Return only the final JSON.")
     return agent.run(prompt)
 
 
-# ==== UI Helpers (pretty outputs) ============================================
+# ==== UI Helpers (rounding + pretty) =========================================
+def _round_df(df: pd.DataFrame, places: int = 2) -> pd.DataFrame:
+    if df is None or df.empty:
+        return df
+    out = df.copy()
+    num_cols = out.select_dtypes(include=["number"]).columns
+    out[num_cols] = out[num_cols].astype(float).round(places)
+    return out
+
 def parse_forecast(json_str):
     df = pd.DataFrame(json.loads(json_str))
-    return df[["product_id","period_start","forecast_qty"]].rename(columns={
+    df = df[["product_id","period_start","forecast_qty"]].rename(columns={
         "product_id":"Product","period_start":"Period Start","forecast_qty":"Forecast Qty"
     })
+    return _round_df(df)
 
 def parse_plan(json_str):
     d = json.loads(json_str)
@@ -250,11 +259,11 @@ def parse_plan(json_str):
     prod = pd.DataFrame(d["products"])
     raw  = pd.DataFrame(d["raw_materials"])
     res  = pd.DataFrame(d["resources"])
-    return d["status"], kpis, prod, raw, res
+    return d["status"], _round_df(kpis), _round_df(prod), _round_df(raw), _round_df(res)
 
 def parse_md61(json_str):
     d = json.loads(json_str)
-    prev = pd.DataFrame(d.get("preview", []))
+    prev = _round_df(pd.DataFrame(d.get("preview", [])))
     path = d.get("csv_path", "")
     return d.get("status",""), prev, path
 
@@ -345,14 +354,31 @@ with gr.Blocks(title="Forecast → Optimize → SAP MD61") as demo:
             a_md61_prev = gr.Dataframe(label="MD61 Preview", interactive=False)
             a_md61_file = gr.File(label="Download MD61 CSV", interactive=False)
 
+        # Robust agent handler: accepts dict OR str and rounds outputs
         def do_agent(h, p, demo_flag, f):
+            def to_obj(x):
+                return x if isinstance(x, (dict, list)) else json.loads(x)
+            def to_str(x):
+                return x if isinstance(x, str) else json.dumps(x)
+
             try:
-                res = run_agentic(h, p, demo_flag, f)
-                out = json.loads(res)
-                f_df = parse_forecast(out["forecast"])
-                _, kpis, prod, raw, res_tbl = parse_plan(out["plan"])
-                _, prev, csv_path = parse_md61(out["md61"])
-                return (json.dumps(out, indent=2), f_df, kpis, prod, raw, res_tbl, prev, csv_path)
+                res = run_agentic(h, p, demo_flag, f)   # may be dict or str
+                out = to_obj(res)
+
+                forecast_json = to_str(out["forecast"])
+                plan_json     = to_str(out["plan"])
+                md61_json     = to_str(out["md61"])
+
+                f_df = parse_forecast(forecast_json)
+                _, kpis, prod, raw, res_tbl = parse_plan(plan_json)
+                _, prev, csv_path = parse_md61(md61_json)
+
+                pretty = {
+                    "forecast": json.loads(forecast_json),
+                    "plan": json.loads(plan_json),
+                    "md61": json.loads(md61_json),
+                }
+                return (json.dumps(pretty, indent=2), f_df, kpis, prod, raw, res_tbl, prev, csv_path)
             except Exception as e:
                 return (f"Agent error: {e}", pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame(),
                         pd.DataFrame(), pd.DataFrame(), None)
@@ -361,4 +387,7 @@ with gr.Blocks(title="Forecast → Optimize → SAP MD61") as demo:
                       outputs=[out_json, a_forecast_tbl, a_plan_kpis, a_plan_prod, a_plan_raw, a_plan_res, a_md61_prev, a_md61_file])
 
 if __name__ == "__main__":
+    # Needs OPENAI_API_KEY in env for agent tab; manual tab works without it.
+    if not os.environ.get("OPENAI_API_KEY"):
+        print("⚠️  Set OPENAI_API_KEY (Space secret) to use Agentic tab.")
     demo.launch()