Upload app.py

app.py

@@ -0,0 +1,72 @@
+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);