Update app.py

app.py

@@ -2,6 +2,8 @@ import streamlit as st
 import numpy as np
 import pickle
 import streamlit.components.v1 as components
+from sklearn.preprocessing import LabelEncoder
+le = import LabelEncoder()
 
 # Load the pickled model
 def load_model():
@@ -16,19 +18,27 @@ def app_design():
     # Add input fields for High, Open, and Low values
     image = '17.png'
     st.image(image, use_column_width=True)
+
+def transform(text):
+    text= le.fit_transform(text)
+    return text[0]
     
     st.subheader("Enter the following values:")
 
     Bhk = st.number_input("Bhk")
     Size = st.number_input("Size")
-    Area_Type = st.number_input("Area_Type")
-    Area_Locality = st.number_input("Area_Locality")
-    City = st.number_input("City")
-    Furnishing_Status = st.number_input("Furnishing_Status")
-    Tenant_Preferred = st.number_input("Tenant_Preferred")
+    Area_Type = st.text_input("Area Type")
+    Area_Type = transform(['Area_Type'])
+    Area_Locality = st.number_input("Area Locality")
+    City = st.text_input("City")
+    City = transform(['City'])
+    Furnishing_Status = st.text_input("Furnishing Status")
+    Furnishing_Status = transform(['Furnishing_Status'])
+    Tenant_Preferred = st.text_input("Tenant Preferred")
+    Tenant_Preferred = transform(['Tenant_Preferred'])
     Bathroom = st.number_input("Bathroom")
-    Point_of_Contact = st.number_input("Point_of_Contact")
-    
+    Point_of_Contact = st.text_input("Point of Contact")
+    Point_of_Contact = transform(['Point_of_Contact'])
  
     # Create a feature list from the user inputs
     features = [[Bhk,Size,Area_Type,Area_Locality,City,Furnishing_Status,Tenant_Preferred,Bathroom,Point_of_Contact]]