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| # +++ | |
| import os | |
| import uuid | |
| import joblib | |
| import json | |
| # IMPORTANT: I already installed the package "gradio" in my current Virtual Environment (VEnvDSDIL_gpu_Py3.12) as: pip install -q gradio_client | |
| # Do NOT install "gradio_client" package again in Anaconda otherwise it will mess up the package. | |
| import gradio as gr | |
| import pandas as pd | |
| # must install the package "huggingface_hub" first in the current python Virtual Environment, with pip, not with conda, as follows | |
| # pip install huggingface_hub | |
| # i.e., in the command line interface within the activated Virtual Environment: | |
| # (VEnvDSDIL_gpu_Py3.12) epalvarez@DSDILmStation01:~ $ pip install huggingface_hub | |
| from huggingface_hub import CommitScheduler | |
| from pathlib import Path | |
| log_file = Path("logs/") / f"data_{uuid.uuid4()}.json" | |
| log_folder = log_file.parent | |
| # Scheduler will log every 2 API calls: | |
| # 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_mf.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() | |
| # Each time we get a prediction we will determine if we should log it to a hugging_face dataset according to the schedule definition outside this function | |
| # 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 | |
| ) | |
| demo.queue() | |
| demo.launch(share=False) |