diff --git a/india_forecast_app/app.py b/india_forecast_app/app.py
index d1a4e41..13047d4 100644
--- a/india_forecast_app/app.py
+++ b/india_forecast_app/app.py
@@ -275,7 +275,7 @@ def app(timestamp: dt.datetime | None, write_to_db: bool, log_level: str):
     app_run(timestamp=timestamp, write_to_db=write_to_db, log_level=log_level)
 
 
-def app_run(timestamp: dt.datetime | None, write_to_db: bool=False, log_level: str= "info"):
+def app_run(timestamp: dt.datetime | None, write_to_db: bool = False, log_level: str = "info"):
     """
     Main function for running forecasts for sites in India
     """
@@ -308,7 +308,7 @@ def app_run(timestamp: dt.datetime | None, write_to_db: bool=False, log_level: s
 
         # 2. Load data/models
         all_model_configs = get_all_models(client_abbreviation=os.getenv("CLIENT_NAME", "ruvnl"))
-        models = []
+        successful_runs = 0
         for model_config in all_model_configs.models:
 
             asset_sites = pv_sites if model_config.asset_type == "pv" else wind_sites
@@ -337,39 +337,35 @@ def app_run(timestamp: dt.datetime | None, write_to_db: bool=False, log_level: s
                 )
                 ml_model.site_uuid = site.site_uuid
 
-                # TODO should we make this an object?
-                models.append(ml_model)
                 log.info(f"{asset_type} model loaded")
 
-        successful_runs = 0
-        for model in models:
-            # 3. Run model for each site
-            site_id = model.site_uuid
-            asset_type = model.asset_type
-            log.info(f"Running {asset_type} model for site={site_id}...")
-            forecast_values = run_model(model=model, site_id=site_id, timestamp=timestamp)
-
-            if forecast_values is None:
-                log.info(f"No forecast values for site_id={site_id}")
-            else:
-                # 4. Write forecast to DB or stdout
-                log.info(f"Writing forecast for site_id={site_id}")
-                forecast = {
-                    "meta": {
-                        "site_id": site_id,
-                        "version": version,
-                        "timestamp": timestamp,
-                    },
-                    "values": forecast_values,
-                }
-                save_forecast(
-                    session,
-                    forecast=forecast,
-                    write_to_db=write_to_db,
-                    ml_model_name=model.name,
-                    ml_model_version=version,
-                )
-                successful_runs += 1
+                # 3. Run model for each site
+                site_id = ml_model.site_uuid
+                asset_type = ml_model.asset_type
+                log.info(f"Running {asset_type} model for site={site_id}...")
+                forecast_values = run_model(model=ml_model, site_id=site_id, timestamp=timestamp)
+
+                if forecast_values is None:
+                    log.info(f"No forecast values for site_id={site_id}")
+                else:
+                    # 4. Write forecast to DB or stdout
+                    log.info(f"Writing forecast for site_id={site_id}")
+                    forecast = {
+                        "meta": {
+                            "site_id": site_id,
+                            "version": version,
+                            "timestamp": timestamp,
+                        },
+                        "values": forecast_values,
+                    }
+                    save_forecast(
+                        session,
+                        forecast=forecast,
+                        write_to_db=write_to_db,
+                        ml_model_name=ml_model.name,
+                        ml_model_version=version,
+                    )
+                    successful_runs += 1
 
         log.info(f"Completed forecasts for {successful_runs} runs for {len(sites)} sites")
         if successful_runs == len(sites):