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