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| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| import pandas as pd | |
| import requests | |
| import hopsworks | |
| import joblib | |
| 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") | |
| def car(km_driven, mileage, engine, max_power, seats, age, seller_type, fuel_type, transmission_type): | |
| input_list = [] | |
| input_list.append(km_driven) | |
| input_list.append(mileage) | |
| input_list.append(engine) | |
| input_list.append(max_power) | |
| input_list.append(seats) | |
| input_list.append(age) | |
| input_list.append(seller_type) | |
| input_list.append(fuel_type) | |
| input_list.append(transmission_type) | |
| 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] | |
| return res | |
| demo = gr.Interface( | |
| fn=car, | |
| title="Car Price Predictive Analytics", | |
| description="Experiment with car details to predict the price of your car.", | |
| allow_flagging="never", | |
| inputs=[ | |
| gr.inputs.Number(default=10000.0, label="Kilometers driven"), | |
| gr.inputs.Number(default=12.0, label="Mileage (in KM/L)"), | |
| gr.inputs.Number(default=1199.0, label="Engine Size (in cc)"), | |
| gr.inputs.Number(default=70.0, label="Max Power (in BHP)"), | |
| gr.inputs.Number(default=5, label="Number of Seats"), | |
| gr.inputs.Number(default=4.0, label="Age of the car"), | |
| gr.inputs.Dropdown(choices=["Dealer", "Individual", "Trustmark Dealer"]), | |
| gr.inputs.Dropdown(choices=["Petrol", "Diesel", "Electric", "CNG"]), | |
| gr.inputs.Dropdown(choices=["Automatic", "Manual"]), | |
| ], | |
| outputs=gr.Number()) | |
| demo.launch() |