Update app.py

app.py

@@ -9,6 +9,16 @@ import pandas as pd
 from huggingface_hub import CommitScheduler
 from pathlib import Path
 
+# Run the training script in the same directory
+
+os.system("python train.py")
+
+# Load the freshly trained model
+
+machine_failure_predictor = joblib.load('model.joblib')
+
+# Prepare the logging functionality
+
 log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
 log_folder = log_file.parent
 
@@ -20,7 +30,7 @@ scheduler = CommitScheduler(
     every=2
 )
 
-machine_failure_predictor = joblib.load('model.joblib')
+# 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]')
@@ -34,6 +44,7 @@ type_input = gr.Dropdown(
 
 model_output = gr.Label(label="Machine failure")
 
+# 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 = {
         'Air temperature [K]': air_temperature,
@@ -63,6 +74,7 @@ def predict_machine_failure(air_temperature, process_temperature, rotational_spe
             
     return prediction[0]
 
+# Create the interface
 demo = gr.Interface(
     fn=predict_machine_failure,
     inputs=[air_temperature_input, process_temperature_input, rotational_speed_input, 
@@ -74,5 +86,6 @@ demo = gr.Interface(
     concurrency_limit=8
 )
 
+# Launch with a load balancer
 demo.queue()
 demo.launch(share=False)