Update app.py

app.py

@@ -1,42 +1,37 @@
-# Cargamos librerías
 import pickle
 import numpy as np
 import gradio as gr
-#import sklearn
+import sklearn
 import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.ensemble import ExtraTreesRegressor
 
-# You can use this block to train and save a model.
-
-
-# Load the data
 filename = 'Dataset_RCS_3.csv'
 names0 = ['JET', "Suelo",'SPT', 'WtoC', 'Presion', 'Velocidad','RCS']
 dataset=pd.read_csv(filename, names=names0)
-# Dividimos el dataset en entrenamiento y test
+
 y = dataset['RCS']
 X = dataset.drop('RCS', axis=1)
-# Categorical data
+
 categorical_cols = ['JET', "Suelo"] 
 df = pd.get_dummies(X, columns = categorical_cols)
 
-# Split the data into training and testing sets
+
 validation_size = 0.20
 seed = 10
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=validation_size, random_state=seed)
 
-# Train the model
+
 modelodef=ExtraTreesRegressor( 
                                      n_estimators=1000,
                                      max_depth=9, 
                                      min_samples_leaf=1,
                                      random_state=seed)
 modelodef.fit(X_train, y_train)
-# Save the model
+
 pickle.dump(modelodef, open("modelodef.pkl", "wb"))
 
-#create a function for gradio
+
 def RCS(JET, Suelo,SPT, WtoC, Presion, Velocidad):
     modelodef = pickle.load(open("modelodef.pkl", "rb"))
     prediction0 = modelodef.predict([[JET, Suelo,SPT, WtoC, Presion, Velocidad]])