app.py

import streamlit as st
import numpy as np
import pickle
import streamlit.components.v1 as components
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()

# Load the pickled model
def load_model():
 return pickle.load(open('Diamond_Price_Prediction_LinearRegression.pkl', 'rb'))

# Function for model prediction
def model_prediction(model, features):
 predicted = str(model.predict(features)[0])
 return predicted
 
def transform(text):
 text = le.fit_transform(text)
 return text[0]

def app_design():
 # Add input fields for High, Open, and Low values
 image = ''
 st.image(image, use_column_width=True)
 
 st.subheader("Enter the following values:")

 Carat = st.number_input("Carat(Weight of Daimond)")
 Cut = st.text_input("Cut(Quality) ('Ideal','Premium','Good','Very Good','Fair')")
 Cut = transform([Cut])
 Color = st.text_input("Color ('E','I','J','H','F','G','D')")
 Color=transform([Color])
 Clarity = st.text_input("Clarity ('SI2','SI1','VS1','VS2','VVS2','VVS1','I1','IF')")
 Clarity=transform([Clarity])
 Depth = st.number_input("Depth")
 Table = st.number_input("Table")
 X_length = st.number_input("X length")
 Y_width = st.number_input("Y width")
 Z_depth = st.number_input("Z depth")
 
 # Create a feature list from the user inputs
 features = [[Carat,Cut,Color,Clarity,Depth,Table,X_length,Y_width,Z_depth]]
 
 # Load the model
 model = load_model()
 
 # Make a prediction when the user clicks the "Predict" button
 if st.button('Predict Price'):
 predicted_value = model_prediction(model, features)
 st.success(f"The Price is: {predicted_value}") 



def main():

 # Set the app title and add your website name and logo
 st.set_page_config(
 page_title="Diamond Price Prediction",
 page_icon=":chart_with_upwards_trend:",
 )
 
 st.title("Welcome to our Diamond Price Prediction App!")
 

if __name__ == '__main__':
 main()