app.py

import numpy as np
import pandas as pd
import xgboost as xgb
import pandas as pd

import random
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split,cross_val_score
from sklearn.ensemble import RandomForestClassifier,RandomForestRegressor
from sklearn.metrics import classification_report,confusion_matrix,accuracy_score
from sklearn.neighbors import KNeighborsClassifier,KNeighborsRegressor
from sklearn.svm import SVC,SVR
from sklearn import datasets




df_strength=pd.read_csv("data_strength11.csv")

import random

x_strength=df_strength.iloc[:,:-1]
y_strength=df_strength.iloc[:,-1]


x_strength=df_strength.iloc[:,:-1]
y_strength=df_strength.iloc[:,-1]
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test=train_test_split(x_strength,y_strength, test_size=0.15, random_state=2)

clf = xgb.XGBRegressor(learning_rate=0.1788, max_depth=27, subsample=0.5146, n_estimators=100,seed = 40)
clf.fit(x_train, y_train)




import gradio as gr

def CirConStr(d, s, fyv, psv, fc):
    # Turning the arguments into a numpy array
    x = np.array([d, s, fyv, psv, fc])
    prediction = clf.predict(x.reshape(1, -1))
    return prediction

inputs = [
    gr.inputs.Number(label="Diameter of spiral stirrups (d, unit: mm)"),
    gr.inputs.Number(label="Spacing of stirrups (s, unit: mm)"),
    gr.inputs.Number(label="Yield strength of spiral stirrups (fyv, unit: MPa)"),
    gr.inputs.Number(label="Volumetric ratio of spiral stirrups(psv, unit: %)"),
    gr.inputs.Number(label="Compressive strength of concrete (fc, unit: MPa)")
]
outputs = gr.outputs.Textbox(label="Estimated Compressive Strength (fcc, unit: MPa)")

app = gr.Interface(fn=CirConStr, inputs=inputs, outputs=outputs, description="Compressive strength estimation for circular concrete column confined by spiral stirrups")
app.launch()