Create app.py

app.py

@@ -0,0 +1,102 @@
+import os
+import uuid
+import joblib
+import json
+
+import gradio as gr
+import pandas as pd
+
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+
+# Run the training script placed in the same directory as app.py
+# The training script will train and persist a logistic regression
+# model with the filename 'model.joblib'
+
+os.system("python train.py")
+
+# Load the freshly trained model from disk
+
+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
+
+scheduler = CommitScheduler(
+    repo_id="machine-failure-mlops-demo-logs",
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2
+)
+
+# 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,
+        'Process temperature [K]': process_temperature,
+        'Rotational speed [rpm]': rotational_speed,
+        'Torque [Nm]': torque,
+        'Tool wear [min]': tool_wear,
+        'Type': type
+    }
+    
+    data_point = pd.DataFrame([sample])
+    prediction = machine_failure_predictor.predict(data_point).tolist()
+
+    # While the prediction is made, log both the inputs and outputs to a local log file
+    # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
+    # access
+    
+    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]
+
+# 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")
+
+# Create the interface
+demo = gr.Interface(
+    fn=predict_machine_failure,
+    inputs=[air_temperature_input, process_temperature_input, rotational_speed_input, 
+            torque_input, tool_wear_input, type_input],
+    outputs=model_output,
+    title="Machine Failure Predictor",
+    description="This API allows you to predict the machine failure status of an equipment",
+    examples=[[300.8, 310.3, 1538, 36.1, 198, 'L'],
+              [296.3, 307.3, 1368, 49.5, 10, 'M'],
+              [298.6, 309.1, 1339, 51.1, 34, 'M'],
+              [302.4, 311.1, 1634, 34.2, 184, 'L'],
+              [297.9, 307.7, 1546, 37.6, 72, 'L']],
+    concurrency_limit=16
+)
+
+# Launch with a load balancer
+demo.queue()
+demo.launch(share=False)