| |
| import streamlit as st |
| import numpy as np |
| import json |
| import pickle |
|
|
| |
| st.markdown( |
| ''' |
| <style> |
| div.stButton > button p { |
| font-size: 21px !important; |
| forn-weight: bold !important; |
| } |
| </style> |
| ''', |
| unsafe_allow_html=True |
| ) |
| st.markdown( |
| ''' |
| <style> |
| div[data-testid="stSelectbox"] label[data-testid="stWidgetLabel"] p { |
| font-size: 22px !important; |
| font-weight: bold !important; |
| } |
| </style> |
| ''', |
| unsafe_allow_html=True |
| ) |
| st.markdown( |
| ''' |
| <style> |
| div[data-testid="stNumberInput"] label[data-testid="stWidgetLabel"] p { |
| font-size: 22px !important; |
| font-weight: bold !important; |
| } |
| </style> |
| ''', |
| unsafe_allow_html=True |
| ) |
| st.markdown( |
| ''' |
| <style> |
| .st-key-Pill1 label[data-testid="stWidgetLabel"] p, |
| .st-key-Pill2 label[data-testid="stWidgetLabel"] p { |
| font-size: 22px !important; |
| font-weight: bold !important; |
| } |
| </style> |
| ''', |
| unsafe_allow_html=True |
| ) |
|
|
| |
| with open("./src/categories.json", "r") as file: |
| categories = json.load(file) |
|
|
| |
| with open("./src/House_Price_Predictor.pkl", "rb") as file: |
| model = pickle.load(file) |
|
|
| |
| def to_numeric(input_dict): |
| feature_vals = [] |
| feature_vals.append(categories["Location"].index(input_dict["location"])) |
| feature_vals.append(categories["Transaction"].index(input_dict["transaction_type"])) |
| feature_vals.append(categories["Ownership"].index(input_dict["ownership_type"])) |
| feature_vals.append(categories["Furnishing"].index(input_dict["furnish_type"])) |
| feature_vals.append(categories["Facing"].index(input_dict["face_direction"])) |
|
|
| if "Garden" in input_dict["overlookings"] or "Park" in input_dict["overlookings"] or "Scenery" in input_dict["overlookings"]: |
| feature_vals.append(1) |
| else: |
| feature_vals.append(0) |
| |
| if "Main Road" in input_dict["overlookings"]: |
| feature_vals.append(1) |
| else: |
| feature_vals.append(0) |
| |
| if "Pool" in input_dict["overlookings"] or "Lake" in input_dict["overlookings"] or "Sea" in input_dict["overlookings"]: |
| feature_vals.append(1) |
| else: |
| feature_vals.append(0) |
| |
| feature_vals.append(input_dict["user_floor"] / input_dict["total_floors"]) |
| feature_vals.append(input_dict["total_floors"]) |
| feature_vals.append(input_dict["num_bathroom"]) |
| feature_vals.append(input_dict["num_balcony"]) |
| feature_vals.append(input_dict["num_parking"]) |
| feature_vals.append(input_dict["carpet_area"]) |
|
|
| return np.array(feature_vals)[np.newaxis, :] |
|
|
|
|
| if __name__ == "__main__": |
| |
| st.title(":house: House Price Predictor") |
| st.write("This model predicts indian house prices by acting like a large committee of real estate experts, where each makes a guess based on property details like size and location.") |
|
|
| |
| input_dict = {} |
| input_dict["location"] = st.selectbox("Location:", options=categories["Location"]) |
| input_dict["transaction_type"] = st.selectbox("Type of Transaction:", options=categories["Transaction"]) |
| input_dict["ownership_type"] = st.selectbox("Type of Ownership:", options=categories["Ownership"]) |
| input_dict["furnish_type"] = st.selectbox("Type of Furnishing:",options=categories["Furnishing"]) |
| input_dict["face_direction"] = st.pills("Face Direction:", options=categories["Facing"], selection_mode="single", key="Pill1") |
| input_dict["overlookings"] = st.pills("Overlooking:", options=categories["Overlooking"], selection_mode="multi", key="Pill2") |
| input_dict["total_floors"] = st.number_input("Total Floors:", min_value=1, value="min", step=1) |
| input_dict["user_floor"] = st.number_input("Your Floor:", min_value=1, max_value=input_dict['total_floors'], value="min", step=1) |
| input_dict["num_bathroom"] = st.number_input("Number of Bathrooms:", min_value=0, value="min", step=1) |
| input_dict["num_balcony"] = st.number_input("Number of Balconies:", min_value=0, value="min", step=1) |
| input_dict["num_parking"] = st.number_input("Number of Car Parkings:", min_value=0, value="min", step=1) |
| input_dict["carpet_area"] = st.number_input("Total Carpet Area:", value=0.0, format="%.2f") |
| st.divider() |
|
|
| |
| if st.button("Get Price", type="primary"): |
| input_values = to_numeric(input_dict) |
| predicted_price = model.predict(input_values)[0] |
|
|
| st.write("Predicted Price:") |
| if predicted_price < 1: |
| st.header(f"\u20B9 {round(predicted_price * 100, 2)} Lac") |
| else: |
| st.header(f"\u20B9 {round(predicted_price, 2)} Cr") |
|
|
