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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); |