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import gradio as gr | |
import pickle | |
import sklearn | |
import pandas as pd | |
import numpy as np | |
import joblib | |
from sklearn.neural_network import MLPRegressor | |
from sklearn.preprocessing import StandardScaler | |
filename= './gdp_model.sav' | |
scalerfile= './gdp_scale.sav' | |
model = pickle.load(open(filename, 'rb')) | |
scaler = pickle.load(open(scalerfile, 'rb')) | |
example_niti = pd.read_csv('./NITIDATA.csv') | |
example_x = np.array(example_niti.iloc[:, 0:]) | |
example_y = example_niti.iloc[:, -1] | |
def predict(Capital_Receipts, | |
Aggregate_Receipts, | |
Social_Sector_Expenditure, | |
Interest_Payments, | |
Own_Tax_Revenues, | |
Fiscal_Deficits, | |
Outstanding_Liabilities, | |
Aggregate_Expenditure, | |
Revenue_Receipts, | |
Revenue_Expenditure, | |
Revenue_Deficits, | |
Capital_Expenditure, | |
expected_GDP_Only_for_examples): | |
X = scaler.transform([[Capital_Receipts, | |
Aggregate_Receipts, | |
Social_Sector_Expenditure, | |
Interest_Payments, | |
Own_Tax_Revenues, | |
Fiscal_Deficits, | |
Outstanding_Liabilities, | |
Aggregate_Expenditure, | |
Revenue_Receipts, | |
Revenue_Expenditure, | |
Revenue_Deficits, | |
Capital_Expenditure]]) | |
return np.round(model.predict(X), 2) | |
demo = gr.Interface( | |
predict, | |
[ | |
"number", | |
"number", | |
"number", | |
"number", | |
"number", | |
"number", | |
"number", | |
"number", | |
"number", | |
"number", | |
"number", | |
"number", | |
"number" | |
], | |
"number", | |
examples=[ | |
list(element) for element in example_x | |
], | |
).launch(share=True); |