Optimized output for modelfit_chat

main.py

@@ -366,21 +366,57 @@ async def _forward_and_get_body(path: str, method: str = "GET", json_body=None,
     return (r.status_code, {"text": r.text[:1000]})
 
 
+def _round_val(x, ndigits: int = 4):
+    """Round numbers to ndigits; leave non-numbers as-is."""
+    if isinstance(x, (int, float)) and not isinstance(x, bool):
+        return round(float(x), ndigits)
+    return x
+
+
+# Row labels for alpha/beta: Estimate, std.er., t_stat, p_val (backend order = _ROW4)
+_FIT_ROW_LABELS = ("Estimate", "std.er.", "t_stat", "p_val")
+
+
+def _format_fit_table(rows: list, ndigits: int = 4) -> list:
+    """
+    Transform backend table (list of 4 dicts, keys=columns) into chat-friendly format.
+    Row 0: column names -> rounded values (Estimate). Rows 1,2,3: row label -> value(s).
+    For single column: [{'col': v0}, {'std.er.': v1}, {'t_stat': v2}, {'p_val': v3}].
+    For multi-column: row 0 = {col: v}; rows 1,2,3 = {'std.er.': [v,...]}, etc.
+    """
+    if not isinstance(rows, list) or len(rows) != 4:
+        return rows
+    col_names = list(rows[0].keys()) if rows[0] else []
+    out = []
+    # Row 0: Estimate (column names as keys)
+    out.append({k: _round_val(rows[0].get(k)) for k in col_names})
+    # Rows 1,2,3: row name as key
+    for r in range(1, 4):
+        label = _FIT_ROW_LABELS[r]
+        vals = [rows[r].get(k) for k in col_names]
+        if len(col_names) == 1:
+            out.append({label: _round_val(vals[0])})
+        else:
+            out.append({label: [_round_val(v) for v in vals]})
+    return out
+
+
 def _build_modelfit_chat_minimal(body: dict) -> dict:
     """Extract formula, alpha, beta, and Performance subset (col 2, rows 3,4,5) for chat response."""
     out = {"ok": True, "formula": body.get("formula")}
-    out["alpha"] = body.get("alpha")
-    out["beta"] = body.get("beta")
+    alpha = body.get("alpha")
+    beta = body.get("beta")
+    out["alpha"] = _format_fit_table(alpha) if isinstance(alpha, list) else alpha
+    out["beta"] = _format_fit_table(beta) if isinstance(beta, list) else beta
     perf = body.get("Performance")
     if isinstance(perf, list) and len(perf) >= 5:
         keys = list(perf[0].keys()) if perf[0] else []
-        # Col 2 (1-based) = index 1; if only one column use index 0. Rows 3,4,5 = indices 2,3,4.
         col_idx = 1 if len(keys) > 1 else 0
         col2_name = keys[col_idx] if keys else None
         if col2_name is not None:
             row_names = ["R2 overall", "MSE", "MAE"]
-            values = [perf[i].get(col2_name) for i in [2, 3, 4]]
-            out["Performance_subset"] = {"metric_names": row_names, "column": col2_name, "values": values}
+            values = [_round_val(perf[i].get(col2_name)) for i in [2, 3, 4]]
+            out["Performance_subset"] = {"metric_names": row_names, "values": values}
         else:
             out["Performance_subset"] = None
     else: