Create app.py

app.py

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+# Cargamos librerías
+import pickle
+import gradio as gr
+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)
+# 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(model, open("model.pkl", "wb"))
+
+#create a function for gradio
+def RCS(JET, Suelo,SPT, WtoC, Presion, Velocidad):
+    x = np.array([JET, Suelo,SPT, WtoC, Presion, Velocidad])
+    prediction = modelodef.predict(x.reshape(1, -1))
+    return prediction
+
+outputs = gr.outputs.Textbox()
+app = gr.Interface(fn=RCS, inputs=['number','number','number','number','number','number'], outputs=outputs,description="UCS jet grouting")
+app.launch(share=True)