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#!/usr/bin/env python
# coding: utf-8
# # Importación de librerías y carga de datos
# In[1]:
import pickle
import numpy as np
import gradio as gr
import sklearn
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.gaussian_process import GaussianProcessRegressor
filename = 'DatosEP3.csv'
names0 = ['zB', 'FI', 'Q', 'IEP']
dataset=pd.read_csv(filename, names=names0)
y = dataset['IEP']
X = dataset.drop('IEP', axis=1)
validation_size = 0.20
seed = 0
X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=validation_size, random_state=seed)
modelodef=GaussianProcessRegressor(alpha=0.00001, normalize_y=False)
modelodef.fit(X_train, Y_train)
# In[2]:
pickle.dump(modelodef, open("modelodef.pkl", "wb"))
def IEPML(zB, FI, Q):
modelodef = pickle.load(open("modelodef.pkl", "rb"))
prediction0 = modelodef.predict([[zB, FI, Q]])
prediction = np.round(prediction0,3)
return prediction
title = "CALCULATION OF THE INFLUENCE FACTOR RELATING ELASTIC AND ELASTOPLASTIC SETTLEMENTS"
description = "This app corresponds to the research paper: COUPLING NUMERICAL MODELLING AND MACHINE LEARNING TO EVALUATE ELASTOPLASTIC SETTLEMENTS OF SHALLOW FOUNDATIONS"
article = """
Notes:
- Click submit/enviar button to obtain the Influence Factor prediction
- Click clear/limpiar button to refresh text
- Please note the application ranges of the variables in the above-referenced paper (in publishing process). Outside these ranges, the predictions may not be reliable
- The ranges of application are:
- Angle of internal friction: between 25º and 45º
- z/B: between 0.25 and 7.50
- Ratio between the applied pressure to the ultimate bearing capacity: between 0.2 and 0.9
- For cases in which there is no bedrock under the foundation, a limit value of z/B equal to 7.5 has to be used due according to the above referenced paper
- As a decimal separator you can use either a point or a comma
"""
app = gr.Interface(
IEPML,
inputs=[
gr.Number(value=1, label="z/B"),
gr.Number(value=30, label="Angle of internal friction (º)"),
gr.Number(value=0.33, label="Ratio between applied load and ultimate load (q/qult)"),
],
outputs=[gr.Text(label="Influence Factor")],
title=title,
description=description,
article = article,
theme="dark-seafoam"
)
app.launch() |