Create app.py

app.py

@@ -0,0 +1,38 @@
+import numpy as np
+import pandas as pd
+import statsmodels.api as sm
+from sklearn.preprocessing import StandardScaler
+import gradio as gr
+
+with open ("scaled_obj.pkl", "rb") as f:
+  sc_object = pickle.load(f)
+
+with open ("scaled_model.pkl", "rb") as f:
+  lin_model_object = pickle.load(f)
+
+def fn_predict(Total_Revenue,Operating_Cost,Total_Assets,Total_Liabilities,Stock_Price,Market_Cap,EBITDA,RD_Expenses,Number_of_Employees):
+  df = np.array([[Total_Revenue,Operating_Cost,Total_Assets,Total_Liabilities,Stock_Price,Market_Cap,EBITDA,RD_Expenses,Number_of_Employees]])
+  scaled_new_data = sc_object.transform(df)
+
+  predictions = lin_model_object.predict(scaled_new_data)
+  return predictions
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=fn_predict,
+    inputs=[
+        gr.Number(label="Total Revenue"),
+        gr.Number(label="Operating Cost"),
+        gr.Number(label="Total Assets"),
+        gr.Number(label="Total Liabilities"),
+        gr.Number(label="Stock Price"),
+        gr.Number(label="Market Cap"),
+        gr.Number(label="EBITDA"),
+        gr.Number(label="R&D Expenses"),
+        gr.Number(label="Number of Employees")
+    ],
+    outputs=gr.Textbox()
+)
+
+# Launch the application
+iface.launch()