Using the Scheduler

Uncommenting the scheduler code in app.py

app.py

@@ -24,14 +24,14 @@ log_folder = log_file.parent
 hf_token = os.environ.get('HF_TOKEN')
 print(hf_token)
 
-# Scheduler will log every 2 API calls:
-# scheduler = CommitScheduler(
-#     repo_id="machine-failure-logs",
-#     repo_type="dataset",
-#     folder_path=log_folder,
-#     path_in_repo="data",
-#     every=2
-# )
+Scheduler will log every 2 API calls:
+scheduler = CommitScheduler(
+    repo_id="machine-failure-logs",
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2
+)
 
 machine_failure_predictor = joblib.load('model_mf.joblib')
 
@@ -59,21 +59,21 @@ def predict_machine_failure(air_temperature, process_temperature, rotational_spe
     data_point = pd.DataFrame([sample])
     prediction = machine_failure_predictor.predict(data_point).tolist()
 
-    # Each time we get a prediction we will determine if we should log it to a hugging_face dataset according to the schedule definition outside this function
-    # with scheduler.lock:
-    #     with log_file.open("a") as f:
-    #         f.write(json.dumps(
-    #             {
-    #                 'Air temperature [K]': air_temperature,
-    #                 'Process temperature [K]': process_temperature,
-    #                 'Rotational speed [rpm]': rotational_speed,
-    #                 'Torque [Nm]': torque,
-    #                 'Tool wear [min]': tool_wear,
-    #                 'Type': type,
-    #                 'prediction': prediction[0]
-    #             }
-    #         ))
-    #         f.write("\n")
+    Each time we get a prediction we will determine if we should log it to a hugging_face dataset according to the schedule definition outside this function
+    with scheduler.lock:
+        with log_file.open("a") as f:
+            f.write(json.dumps(
+                {
+                    'Air temperature [K]': air_temperature,
+                    'Process temperature [K]': process_temperature,
+                    'Rotational speed [rpm]': rotational_speed,
+                    'Torque [Nm]': torque,
+                    'Tool wear [min]': tool_wear,
+                    'Type': type,
+                    'prediction': prediction[0]
+                }
+            ))
+            f.write("\n")
             
     return prediction[0]