Upload app.py

app.py

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+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.experimental import enable_iterative_imputer
+from sklearn.impute import IterativeImputer
+from sklearn.model_selection import KFold
+from sklearn.ensemble import ExtraTreesRegressor
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.ensemble import StackingRegressor
+from sklearn.ensemble import RandomForestRegressor
+
+filename = 'DatabaseFinal0.csv'
+names0 = ['LL',"IP" ,"e0",'w', 'cc']
+dataset=pd.read_csv(filename, names=names0)
+
+y = dataset['cc']
+X0 = dataset.drop('cc', axis=1)
+
+impute_it = IterativeImputer()
+X2=impute_it.fit_transform(X0)
+X = pd.DataFrame(X2, columns=['LL',"IP" ,"e0",'w'])
+
+
+validation_size = 0.2
+seed = 10
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=validation_size, random_state=seed)
+
+
+model1 =ExtraTreesRegressor(max_depth=15, max_features=None, n_estimators=500,random_state=100,min_samples_split=12)
+model1= model1.fit(X_train, y_train)
+
+model2 =GradientBoostingRegressor(learning_rate=0.007, max_depth=2,n_estimators=1650, random_state=100,min_samples_split=9,max_features= 'log2')
+model2= model2.fit(X_train, y_train)
+
+model3 =RandomForestRegressor(n_estimators= 1000,min_samples_split= 11, min_samples_leaf= 1,
+                                max_features= "auto",max_depth= 6,bootstrap= True,random_state=100)
+model3= model3.fit(X_train, y_train)
+
+
+level1 = list()
+level1.append(('ET', model1))
+level1.append(('GBR', model2))
+level2 = model3
+cv = KFold(n_splits=10, random_state=100,shuffle=True)
+modelodef = StackingRegressor(estimators=level1, final_estimator=level2, cv=cv, passthrough=True)
+modelodef.fit(X_train, y_train)
+
+pickle.dump(modelodef, open("modelodef.pkl", "wb"))
+
+
+def cc(LL,IP,e0,w):
+    modelodef = pickle.load(open("modelodef.pkl", "rb"))
+    prediction0 = modelodef.predict([[LL,IP,e0,w]])
+    prediction = np.round(prediction0,3)
+    return prediction
+
+title = "A SUPER-LEARNER MACHINE LEARNING MODEL FOR A GLOBAL PREDICTION OF COMPRESSION INDEX IN CLAYS"
+description = "This app corresponds to the research paper: A super-learner machine learning model for a global prediction of compression index in clays"
+article = """
+            Notes:
+            - Click submit/enviar button to obtain the Compression index prediction
+            - Click clear/limpiar button to refresh text
+            - Please note the application ranges of the variables in the above-referenced paper (in publication process). Outside these ranges, the predictions may not be reliable
+            - As a decimal separator you can use either a point or a comma
+           """        
+                 
+app = gr.Interface(
+    cc,
+    inputs=[
+        gr.Number(value=1, label="Liquid limit (%)"),
+        gr.Number(value=1, label="Plasticity index (%)"),
+        gr.Number(value=1, label="Initial void ratio"),
+        gr.Number(value=1, label="Natural water content (%)"),
+        
+    ],
+    outputs=[gr.Text(label="Compression index")],
+    title=title,
+    description=description,
+    article = article,
+    theme="dark-seafoam"
+)
+
+
+app.launch()