import pandas as pd
import numpy as np
#from matplotlib import pyplot as plt
#import seaborn as sns
import sklearn
from sklearn.preprocessing import RobustScaler, StandardScaler, OneHotEncoder, OrdinalEncoder, PowerTransformer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import pickle
import streamlit as st

st.image("https://www.innomatics.in/wp-content/uploads/2023/01/Innomatics-Logo1.png")
st.title("Diamond Price Prediction")

carat = st.number_input("Enter the carat value")
cut = st.text_input("Enter the cut of the diamond")
color = st.text_input("Enter the color code of the diamond")
clarity = st.text_input("Enter the clarity code")
depth = st.number_input("Enter the depth of the diamond")
table = st.number_input("Enter the table value")
x = st.number_input("Enter the length of diamond")
y = st.number_input("Enter the width of the diamond")
z = st.number_input("Enter the z of the diamond")

model_1 = pickle.load(open(r"estimator1.pkl","rb")) #pickle file path
if st.button("Submit"):
    result = model_1.predict([[carat,cut,color,clarity,depth,table,x,y,z]])
    st.write(f"The predicted price of the diamond is {result}")