add computed field back into data_models (this will fail the CI checks, need to find better solution)

Author: DaltheCow

README.md

@@ -185,7 +185,13 @@ npm run build
 npm run serve
 ```
 
-This will start a local server (e.g., at http://localhost:3000). Then, in your GuideLLM config or CLI flags, point to this local server as the asset base for report generation. You can set the Environment to LOCAL before running your benchmarks to accomplish this.
+This will start a local server (e.g., at http://localhost:3000). Then set the Environment to LOCAL before running your benchmarks.
+
+```bash
+export GUIDELLM__ENV=local
+
+Alternatively, in config.py update the ENV_REPORT_MAPPING used as the asset base for report generation to the LOCAL option.
+```
 
 ## Resources
 

src/guidellm/presentation/data_models.py

@@ -3,7 +3,7 @@
 from math import ceil
 from typing import TYPE_CHECKING, Optional, Union
 
-from pydantic import BaseModel
+from pydantic import BaseModel, computed_field
 
 if TYPE_CHECKING:
     from guidellm.benchmark.benchmark import GenerativeBenchmark
@@ -189,6 +189,7 @@ class TabularDistributionSummary(DistributionSummary):
     `percentile_rows` helper.
     """
 
+    @computed_field
     @property
     def percentile_rows(self) -> list[dict[str, float]]:
         rows = [