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Update app.py
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app.py
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@@ -6,20 +6,20 @@ import json
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import gradio as gr
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import pandas as pd
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from pathlib import Path
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machine_failure_predictor = joblib.load('model.joblib')
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@@ -47,20 +47,20 @@ def predict_machine_failure(air_temperature, process_temperature, rotational_spe
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data_point = pd.DataFrame([sample])
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prediction = machine_failure_predictor.predict(data_point).tolist()
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return prediction[0]
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@@ -72,7 +72,7 @@ demo = gr.Interface(
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title="Machine Failure Predictor",
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description="This API allows you to predict the machine failure status of an equipment",
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allow_flagging="auto",
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concurrency_limit=
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)
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demo.queue()
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import gradio as gr
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import pandas as pd
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from huggingface_hub import CommitScheduler
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from pathlib import Path
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log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
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log_folder = log_file.parent
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scheduler = CommitScheduler(
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repo_id="machine-failure-logs",
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repo_type="dataset",
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folder_path=log_folder,
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path_in_repo="data",
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every=2
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)
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machine_failure_predictor = joblib.load('model.joblib')
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data_point = pd.DataFrame([sample])
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prediction = machine_failure_predictor.predict(data_point).tolist()
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with scheduler.lock:
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with log_file.open("a") as f:
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f.write(json.dumps(
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{
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'Air temperature [K]': air_temperature,
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'Process temperature [K]': process_temperature,
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'Rotational speed [rpm]': rotational_speed,
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'Torque [Nm]': torque,
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'Tool wear [min]': tool_wear,
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'Type': type,
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'prediction': prediction[0]
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}
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))
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f.write("\n")
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return prediction[0]
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title="Machine Failure Predictor",
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description="This API allows you to predict the machine failure status of an equipment",
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allow_flagging="auto",
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concurrency_limit=16
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)
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demo.queue()
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