First upload of the app.py without the scheduler

The scheduler code has been commented out on the app.py file for this initial version.

app.py

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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)

model_mf.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e8745b4d4e2c0da514f0edb99c23932b1a12b6af2b97fbcf517af800f4ad5088
+size 4238

requirements.txt

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+#scikit-learn==1.2.2
+scikit-learn==1.5.0
+joblib==1.4.0
+pandas==2.2.2
+numpy==2.0.0