commit inicial

.gitignore

@@ -0,0 +1,126 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+pytestdebug.log
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule files
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# profiling files
+.prof
+
+# VSCode settings
+.vscode/
+
+# Data files
+*.joblib
+*.pkl

README.md

@@ -9,4 +9,34 @@ app_file: app.py
 pinned: false
 ---
 
+# Cluster Prediction Model Deployment
+
+## Overview
+This application uses a machine learning model to predict the operation cluster of machinery based on input features. It is built using Gradio and hosted on Hugging Face Spaces for easy access and interaction.
+
+## Features
+The application accepts the following features to make predictions:
+- RPM (Revoluciones Por Minuto del equipo)
+- Aceleración_LA (Aceleración Lado del Acople)
+- Velocidad_LA (Velocidad Lado del Acople)
+- Envolvente_LA (Envolvente Lado del Acople)
+- Aceleración_LI (Aceleración Lado del Impulsor)
+- Velocidad_LI (Velocidad Lado del Impulsor)
+- Envolvente_LI (Envolvente Lado del Impulsor)
+- Horas_Op (OHoras de Operación)
+
+## Cluster Definitions
+- 0: Operación Crítica (Critical Operation)
+- 1: Operación Sub-óptima (Sub-optimal Operation)
+- 2: Operación Normal (Normal Operation)
+
+## How to Use
+Enter the values for each feature as instructed in the interface and click the 'Submit' button to see the predicted operation condition.
+
+## Deployment
+This application is hosted on Hugging Face Spaces, allowing for easy access and interactive use through a web-based interface.
+
+Enjoy interacting with the model!
+
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# -*- coding: utf-8 -*-
+"""Clustering Use_model_cloud.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/19dFamN4Td92NqjpOmSrTE7L1kyiZtmTk
+"""
+
+import os
+import joblib
+
+"""# **Web/API deployment**
+
+## **Serialize the Model**
+"""
+
+saved_model_path = "/OperationCondition/files/prueba_clustering.joblib"
+
+os.path.getsize(saved_model_path)
+
+saved_model = joblib.load("/OperationCondition/files/prueba_clustering.joblib")
+
+saved_model
+
+"""## **Write a Model Server**"""
+
+
+"""### Define Function for Prediction"""
+
+import numpy as np
+
+def predict_cluster(RPM, Aceleración_LA, Velocidad_LA, Envolvente_LA, Aceleración_LI, Velocidad_LI, Envolvente_LI, Horas_Op):
+    # Asume que el modelo necesita un array de numpy con una fila de features
+    input_features = np.array([RPM, Aceleración_LA, Velocidad_LA, Envolvente_LA, Aceleración_LI, Velocidad_LI, Envolvente_LI, Horas_Op]).reshape(1, -1)
+    cluster_number = saved_model.predict(input_features)[0]
+    # Mapeo de número de cluster a nombre de cluster
+    cluster_names = {0: "Operación Crítica", 1: "Operación Sub-óptima", 2: "Operación Normal"}
+    cluster_name = cluster_names.get(cluster_number, "Cluster no identificado")
+    return f"El cluster predicho es: {cluster_name}"
+
+"""### Create Interface"""
+
+import gradio as gr
+
+iface = gr.Interface(
+    fn=predict_cluster,
+    inputs=[
+        gr.Number(label="RPM"),
+        gr.Number(label="Aceleración_LA"),
+        gr.Number(label="Velocidad_LA"),
+        gr.Number(label="Envolvente_LA"),
+        gr.Number(label="Aceleración_LI"),
+        gr.Number(label="Velocidad_LI"),
+        gr.Number(label="Envolvente_LI"),
+        gr.Number(label="Horas_Op")
+    ],
+    outputs="text",
+    title="Predicción de Condición de Operación del Activo",
+    description="Introduce los valores de las características del motor para predecir la condición."
+)
+
+# Lanzar la aplicación de Gradio
+iface.launch()

requirements.txt

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+gradio
+numpy
+scikit-learn
+joblib