push changes to production

src/webapp/databricks.py

@@ -34,8 +34,7 @@
 MEDALLION_LEVELS = ["silver", "gold", "bronze"]
 
 # The name of the deployed pipeline in Databricks. Must match directly.
-PDP_INFERENCE_JOB_NAME = "github_sourced_pdp_inference_pipeline"
-PDP_H2O_INFERENCE_JOB_NAME = "edvise_github_sourced_pdp_inference_pipeline"
+PDP_INFERENCE_JOB_NAME = "edvise_github_sourced_pdp_inference_pipeline"
 
 
 class DatabricksInferenceRunRequest(BaseModel):
@@ -203,12 +202,7 @@ def run_pdp_inference(
 
         db_inst_name = databricksify_inst_name(req.inst_name)
 
-        if req.model_type == "sklearn":
-            pipeline_type = PDP_INFERENCE_JOB_NAME
-        elif req.model_type == "h2o":
-            pipeline_type = PDP_H2O_INFERENCE_JOB_NAME
-        else:
-            raise ValueError("Invalid model framework assigned to institution model")
+        pipeline_type = PDP_INFERENCE_JOB_NAME
         try:
             job = next(w.jobs.list(name=pipeline_type), None)
             if not job or job.job_id is None:

src/webapp/routers/data.py

@@ -1771,7 +1771,15 @@ def get_model_cards(
         )
 
     try:
-        volume_path = f"/Volumes/staging_sst_01/{databricksify_inst_name(query_result[0][0].name)}_gold/gold_volume/model_cards/model-card-{model_name}.pdf"
+        env = str(env_vars["ENV"]).strip().upper()
+        SCHEMAS = {"DEV": "dev_sst_02", "STAGING": "staging_sst_01"}
+        if env not in SCHEMAS:
+            raise ValueError(
+                f"Unsupported ENV {env_vars.get('ENV')!r}; expected DEV or STAGING"
+            )
+        env_schema = SCHEMAS[env]
+
+        volume_path = f"/Volumes/{env_schema}/{databricksify_inst_name(query_result[0][0].name)}_gold/gold_volume/model_cards/model-card-{model_name}.pdf"
         LOGGER.info(f"Attempting to download from {volume_path}")
         response = w.files.download(volume_path)
         stream = cast(IO[bytes], response.contents)