Update app.py

app.py

@@ -0,0 +1,44 @@
+import pandas as pd
+import gradio as gr
+import joblib
+
+
+le=joblib.load('le_col.pkl')
+std=joblib.load('std_col.pkl')
+lr=joblib.load('model.pkl')
+
+
+le_col=['Product_Category','Customer_Segment']
+std_col=['Price','Discount','Marketing_Spend','Units_Sold']
+
+
+def prediction_commerce_price(pc,p,d,cs,ms,us):
+    try:
+        input_data=pd.DataFrame({
+            'Product_Category':[pc],
+            'Price':[p],
+            'Discount':[d],
+            'Customer_Segment':[cs],
+            'Marketing_Spend':[ms],
+            'Units_Sold':[us]
+        })
+        for col in le_col:
+            input_data[col]=le[col].transform(input_data[col])
+        input_data[std_col]=std.transform(input_data[std_col])
+        prediction=lr.predict(input_data)
+        return prediction[0]
+    except Exception as e:
+        return str(e)
+gr.Interface(
+    inputs=[
+        gr.Dropdown(['Electronics','Sports','Toys','Home Decor','Fashion'],label='Product_Category'),
+        gr.Number(label='Price'),
+        gr.Number(label='Discount'),
+        gr.Dropdown(['Regular','Occasional','Premium'],label='Customer_Segment'),
+        gr.Number(label='Marketing_Spend'),
+        gr.Number(label='Units_Sold')
+    ],
+    fn=prediction_commerce_price,
+    outputs=gr.Textbox(label='Prediction'),
+    title='Prediction_program'
+).launch()