migrate whole codebase

README.md

@@ -1,8 +1,8 @@
 ---
 title: House Price Predictor
 emoji: 🚀
-colorFrom: red
-colorTo: red
+colorFrom: green
+colorTo: green
 sdk: docker
 app_port: 8501
 tags:

src/House_Price_Predictor.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29e6c6be87e5a50b8adb9cf8f227ebac41abd3e5fef38befa4e2871f89b68b5f
+size 184446779

src/categories.json

@@ -0,0 +1 @@
+{"Location": ["Agra", "Ahmadnagar", "Ahmedabad", "Allahabad", "Aurangabad", "Badlapur", "Bangalore", "Belgaum", "Bhiwadi", "Bhiwandi", "Bhopal", "Bhubaneswar", "Chandigarh", "Chennai", "Coimbatore", "Dehradun", "Durgapur", "Ernakulam", "Faridabad", "Ghaziabad", "Goa", "Greater Noida", "Guntur", "Gurgaon", "Guwahati", "Gwalior", "Haridwar", "Hyderabad", "Indore", "Jabalpur", "Jaipur", "Jamshedpur", "Jodhpur", "Kalyan", "Kanpur", "Kochi", "Kolkata", "Kozhikode", "Lucknow", "Ludhiana", "Madurai", "Mangalore", "Mohali", "Mumbai", "Mysore", "Nagpur", "Nashik", "Navi Mumbai", "Navsari", "Nellore", "New Delhi", "Noida", "Palakkad", "Palghar", "Panchkula", "Patna", "Pondicherry", "Pune", "Raipur", "Rajahmundry", "Ranchi", "Satara", "Shimla", "Siliguri", "Solapur", "Sonipat", "Surat", "Thane", "Thrissur", "Tirupati", "Trichy", "Trivandrum", "Udaipur", "Udupi", "Vadodara", "Vapi", "Varanasi", "Vijayawada", "Visakhapatnam", "Vrindavan", "Zirakpur"], "Transaction": ["Other", "Rent/Lease", "Resale", "New Property"], "Ownership": ["Co-operative Society", "Freehold", "Leasehold", "No data", "Power Of Attorney"], "Furnishing": ["Unfurnished", "Semi-Furnished", "Furnished"], "Facing": ["East", "No data", "North", "North - East", "North - West", "South", "South - East", "South -West", "West"], "Overlooking": ["Garden", "Park", "Scenery", "Pool", "Lake", "Sea", "Main Road"]}

src/streamlit_app.py

@@ -1,7 +1,126 @@
 # import packages
 import streamlit as st
 import numpy as np
-from sklearn.ensemble import RandomForestRegressor
+import json
+import pickle
 
-# display message
-st.write("Hello World!!")
+# create custom styles
+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
+)
+
+# get unique categories of each feature
+with open("./src/categories.json", "r") as file:
+    categories = json.load(file)
+
+# load the model
+with open("./src/House_Price_Predictor.pkl", "rb") as file:
+    model = pickle.load(file)
+
+# function to transform given input to numeric
+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__":
+    # display header and description
+    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.")
+
+    # create form to get independent features
+    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()
+
+    # get prediction when button is pressed
+    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")