Update app.py

app.py

@@ -23,27 +23,13 @@ log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
 log_folder = log_file.parent
 
 scheduler = CommitScheduler(
-    repo_id="machine-failure-mlops-logs",
+    repo_id="machine-failure-logs",
     repo_type="dataset",
     folder_path=log_folder,
     path_in_repo="data",
     every=2
 )
 
-# Set up UI components for input and output
-
-air_temperature_input = gr.Number(label='Air temperature [K]')
-process_temperature_input = gr.Number(label='Process temperature [K]')
-rotational_speed_input = gr.Number(label='Rotational speed [rpm]')
-torque_input = gr.Number(label='Torque [Nm]')
-tool_wear_input = gr.Number(label='Tool wear [min]')
-type_input = gr.Dropdown(
-    ['L', 'M', 'H'],
-    label='Type'
-)
-
-model_output = gr.Label(label="Machine Failure Expected?")
-
 # Define the predict function that runs when 'Submit' is clicked or when a API request is made
 def predict_machine_failure(air_temperature, process_temperature, rotational_speed, torque, tool_wear, type):
     sample = {
@@ -54,13 +40,9 @@ def predict_machine_failure(air_temperature, process_temperature, rotational_spe
         'Tool wear [min]': tool_wear,
         'Type': type
     }
-    data_point = pd.DataFrame([sample])
-    prediction = machine_failure_predictor.predict(data_point).tolist()[0]
     
-    if prediction == 1:
-        prediction_label = 'yes'
-    else:
-        prediction_label = 'no' 
+    data_point = pd.DataFrame([sample])
+    prediction = machine_failure_predictor.predict(data_point).tolist()
 
     with scheduler.lock:
         with log_file.open("a") as f:
@@ -72,12 +54,26 @@ def predict_machine_failure(air_temperature, process_temperature, rotational_spe
                     'Torque [Nm]': torque,
                     'Tool wear [min]': tool_wear,
                     'Type': type,
-                    'prediction': prediction_label
+                    'prediction': prediction[0]
                 }
             ))
             f.write("\n")
             
-    return prediction_label
+    return prediction[0]
+
+# Set up UI components for input and output
+
+air_temperature_input = gr.Number(label='Air temperature [K]')
+process_temperature_input = gr.Number(label='Process temperature [K]')
+rotational_speed_input = gr.Number(label='Rotational speed [rpm]')
+torque_input = gr.Number(label='Torque [Nm]')
+tool_wear_input = gr.Number(label='Tool wear [min]')
+type_input = gr.Dropdown(
+    ['L', 'M', 'H'],
+    label='Type'
+)
+
+model_output = gr.Label(label="Machine Failure Expected?")
 
 # Create the interface
 demo = gr.Interface(