import gradio as gr import pandas as pd import numpy as np import pickle import sklearn model = pickle.load(open('random_forest_regression_model.pkl', 'rb')) import gradio as gr def car(Year,owners,sp,fuel,distance,tyype,trans): if fuel =="PETROL": p,d=1,0 if fuel =="DIESEL": p,d=0,1 if fuel =="CNG": p,d=0,0 year = 2022-Year if tyype=="Individual": st=1 else: st=0 if trans=="Manual": t=1 else: t=0 error = "ใ€ ๐—˜๐—ฟ๐—ฟ๐—ผ๐—ฟ ๐Ÿฐ๐Ÿฌ๐Ÿฐ : ๐—ฉ๐—ฎ๐—น๐˜‚๐—ฒ ๐— ๐—ถ๐˜€๐˜€๐—ถ๐—ป๐—ด ใ€‘" prediction=model.predict([[sp,distance,owners,year,d,p,st,t]]) output=round(prediction[0],2) ou= str(output) if Year==0 or distance==0 or sp==0: return error else: return "The Price of Will be โ‚น" + ou + "L !" # face = gr.Interface(fn=start, inputs=["text", "checkbox","N", gr.inputs.Slider(0, 100),gr.inputs.Radio(["add", "subtract", "multiply"])], outputs=["text", "number"]) # face.launch() ts= """ Used Car Price Prediction""" # ---------------------------------INPUTS :------------------------------ # in1=gr.inputs.Textbox(placeholder="En",label="MO") in2=gr.inputs.Number(label='Which Model (Year)ใ€*ใ€‘',default=0) in3= gr.inputs.Slider(0, 10,1,label="No. of Previous Owners eg.1,2,3") in4=gr.inputs.Number(label='Kilometeres Drivedใ€*ใ€‘',default=0) in5= gr.inputs.Radio(["PETROL", "DIESEL", "CNG"]) in6=gr.inputs.Dropdown(["Individual", "Dealer"],label="You Are") in7=gr.inputs.Dropdown(["Automatic", "Manual"],label="Transmission Type") in8=gr.inputs.Number(label='Showroom Price โ‚น(in LAKHS)ใ€*ใ€‘',default=0) interface = gr.Interface(fn=car, inputs=[in2,in3,in8,in5,in4,in6,in7], outputs=["text"],title=ts,theme="peach",css=""" .gradio_bg[theme=default] .gradio_interface .panel_button.submit { background-color: rgba(99, 102, 241, var(--tw-bg-opacity)); } .gradio_bg[theme=peach] .gradio_interface .panel_header { font-family: Arial, Helvetica, sans-serif;; font-size: 17px; } .gradio_page .title{ font-family: "Copperplate",Fantasy; font-size: 47px; }""" ) interface.launch(inline=False)