Create main.py

main.py

@@ -0,0 +1,105 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from PIL import Image
+import requests
+
+import hopsworks
+import joblib
+
+st.set_page_config(
+    page_title='Car Prices Predictive Analysis',
+    page_icon='',
+    layout='wide'
+)
+
+project = hopsworks.login()
+fs = project.get_feature_store()
+
+mr = project.get_model_registry()
+model = mr.get_model("car_prices", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/car_prices_model.pkl")
+
+header = st.container()
+
+model_train = st.container()
+
+# mileage, engine, max_power, seats, age, seller_type, fuel_type, transmission_type
+with header:
+    st.title("Car Prices Predictive analysis")
+    col_a, col_b = st.columns(2)
+    km = col_a.number_input("Kilometers Driven", 1000, 1000000, 10000, 1000)
+    engine = col_b.number_input("Engine size (in CC)", 600, 6000, 1200, 100)
+    power = col_a.number_input("Maximum Power in BHP", 10.0, 1000.0, 80.0, 2.0)
+    seats = col_b.slider("Number of Seats", 2, 10, 5, 1)
+    age = col_a.slider("Age of the car in years", 1, 10, 2)
+    seller = col_b.selectbox(
+        "Seller Type", ["Individual", "Dealer", "Trustmark Dealer"])
+    fuel = col_a.selectbox(
+        "Fuel Type", ["Petrol", "Diesel", "CNG", "LPG", "Electric"])
+    transmission = col_b.selectbox(
+        "Transmission Type", ["Manual", "Automatic"])
+
+input_list = [km, 12, engine, power, seats, age, seller, fuel, transmission]
+
+if (input_list[6] == "Dealer"):
+    input_list.pop(6)
+    input_list.insert(6, 1)
+    input_list.insert(7, 0)
+    input_list.insert(8, 0)
+if (input_list[6] == "Individual"):
+    input_list.pop(6)
+    input_list.insert(6, 0)
+    input_list.insert(7, 1)
+    input_list.insert(8, 0)
+if (input_list[6] == "Trustmark Dealer"):
+    input_list.pop(6)
+    input_list.insert(6, 0)
+    input_list.insert(7, 0)
+    input_list.insert(8, 1)
+
+if (input_list[9] == "CNG"):
+    input_list.pop(9)
+    input_list.insert(9, 1)
+    input_list.insert(10, 0)
+    input_list.insert(11, 0)
+    input_list.insert(12, 0)
+if (input_list[9] == "Diesel"):
+    input_list.pop(9)
+    input_list.insert(9, 0)
+    input_list.insert(10, 1)
+    input_list.insert(11, 0)
+    input_list.insert(12, 0)
+if (input_list[9] == "Electric"):
+    input_list.pop(9)
+    input_list.insert(9, 0)
+    input_list.insert(10, 0)
+    input_list.insert(11, 1)
+    input_list.insert(12, 0)
+if (input_list[9] == "Petrol"):
+    input_list.pop(9)
+    input_list.insert(9, 0)
+    input_list.insert(10, 0)
+    input_list.insert(11, 0)
+    input_list.insert(12, 1)
+
+if (input_list[13] == "Automatic"):
+    input_list.pop(13)
+    input_list.insert(13, 1)
+    input_list.insert(14, 0)
+if (input_list[13] == "Manual"):
+    input_list.pop(13)
+    input_list.insert(13, 0)
+    input_list.insert(14, 1)
+
+df = pd.DataFrame(input_list)
+res = model.predict(df.T)[0].round(4)
+
+# col_but = st.columns(5)
+with model_train:
+    disp = st.columns(5)
+    pred_button = disp[2].button('Evaluate price')
+    if pred_button:
+        with st.spinner():
+            st.write(f'#### Evaluated price of the car: ₹ {res:,.2f}')