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import gradio as gr
import pandas as pd
import numpy as np
import sklearn
import pickle
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 ๐๐ฟ๐ฟ๐ผ๐ฟ ๐ฐ๐ฌ๐ฐ : ๐ฉ๐ฎ๐น๐๐ฒ ๐ ๐ถ๐๐๐ถ๐ป๐ด 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 โน" + 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()
# ---------------------------------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.")
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 โน(in LAKHS)ใ*ใ',default=0)
interface = gr.Interface(fn=car,
inputs=[in2,in3,in8,in5,in4,in6,in7],
outputs=["text"],title="Car calculator"
)
interface.launch(inline=False) |