Upload app.py

app.py

@@ -1,50 +1,78 @@
+import os
+import uuid
+import joblib
+import json
+
 import gradio as gr
 import pandas as pd
-import joblib 
-
-# Load your model
-model = joblib.load("model.joblib")
-
-# Define the function to process inputs
-def predict_charges(age, sex, bmi, children, smoker, region):
-    # Create a DataFrame with the inputs
-    input_data = pd.DataFrame({
-        "age": [age],
-        "sex": [sex],
-        "bmi": [bmi],
-        "children": [children],
-        "smoker": [smoker],
-        "region": [region]
-    })
-    
-    charges = model.predict(input_data)[0]
-    
-    # For demonstration, assume charges as a mock value
-    charges = 1000 + (age * 10) + (bmi * 5) + (children * 50) + (500 if smoker == "yes" else 0)
-    
-    return f"Estimated charges: ${charges:.2f}"
-
-# Gradio interface setup
-inputs = [
-    gr.inputs.Number(label="Age", default=30),
-    gr.inputs.Radio(["male", "female"], label="Sex"),
-    gr.inputs.Number(label="BMI", default=25),
-    gr.inputs.Number(label="Children", default=0),
-    gr.inputs.Radio(["yes", "no"], label="Smoker"),
-    gr.inputs.Dropdown(["northeast", "northwest", "southeast", "southwest"], label="Region")
-]
-
-outputs = gr.outputs.Textbox(label="Estimated Charges")
-
-# Initialize the Gradio interface
-app = gr.Interface(
-    fn=predict_charges,
-    inputs=inputs,
-    outputs=outputs,
-    title="Insurance Charges Prediction",
-    description="Enter the details to estimate health insurance charges based on input parameters."
+
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+
+log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+log_folder = log_file.parent
+
+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.joblib')
+
+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")
+
+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()
+
+    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]
+
+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",
+    allow_flagging="auto",
+    concurrency_limit=8
 )
 
-# Launch the app
-if __name__ == "__main__":
-    app.launch()
+demo.queue()
+demo.launch(share=False)