corrected upload of files

app.py

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+import os
+import uuid
+import joblib
+import jsonI
+
+import gradio as gr
+import pandas as pd
+
+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 = macnine_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,
+					'Rotatipnal 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
+)
+
+demo.queue()
+demo.launch(share=False)

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a0db284be28e1303ab3612a3a6e35076ff8e9e32c035dd4e2ffdf9635b940780
+size 3838

requirements.txt

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+scikit-learn==1.2.2
+pandas==2.0.3
+numpy==1.25.2