Update app.py

app.py

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+
+import streamlit as st
+import pickle
+import pandas as pd
+import numpy as np
+
+st.set_page_config(page_title="Viz Demo")
+
+file_df = r"C:/Users/jraij/Downloads/df.pkl"
+file_pipe = r"C:/Users/jraij/Downloads/pipeline.pkl"
+
+with open(file_df,'rb') as file:
+    df = pickle.load(file)
+
+with open(file_pipe,'rb') as file:
+    pipeline = pickle.load(file)
+
+
+st.header('Enter your inputs')
+
+# property_type
+property_type = st.selectbox('Property Type',['flat','house'])
+
+# sector
+sector = st.selectbox('Sector',sorted(df['sector'].unique().tolist()))
+
+bedrooms = float(st.selectbox('Number of Bedroom',sorted(df['bedRoom'].unique().tolist())))
+
+bathroom = float(st.selectbox('Number of Bathrooms',sorted(df['bathroom'].unique().tolist())))
+
+balcony = st.selectbox('Balconies',sorted(df['balcony'].unique().tolist()))
+
+property_age = st.selectbox('Property Age',sorted(df['agePossession'].unique().tolist()))
+
+built_up_area = float(st.number_input('Built Up Area'))
+
+servant_room = float(st.selectbox('Servant Room',[0.0, 1.0]))
+store_room = float(st.selectbox('Store Room',[0.0, 1.0]))
+
+furnishing_type = st.selectbox('Furnishing Type',sorted(df['furnishing_type'].unique().tolist()))
+luxury_category = st.selectbox('Luxury Category',sorted(df['luxury_category'].unique().tolist()))
+floor_category = st.selectbox('Floor Category',sorted(df['floor_category'].unique().tolist()))
+
+if st.button('Predict'):
+
+    # form a dataframe
+    data = [[property_type, sector, bedrooms, bathroom, balcony, property_age, built_up_area, servant_room, store_room, furnishing_type, luxury_category, floor_category]]
+    columns = ['property_type', 'sector', 'bedRoom', 'bathroom', 'balcony',
+               'agePossession', 'built_up_area', 'servant room', 'store room',
+               'furnishing_type', 'luxury_category', 'floor_category']
+
+    # Convert to DataFrame
+    one_df = pd.DataFrame(data, columns=columns)
+
+    #st.dataframe(one_df)
+
+    # predict
+    base_price = np.expm1(pipeline.predict(one_df))[0]
+    low = base_price - 0.22
+    high = base_price + 0.22
+
+    # display
+    st.text("The price of the flat is between {} Cr and {} Cr".format(round(low,2),round(high,2)))