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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 = 'Diamond price image.png' | |
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!") | |
app_design() | |
if __name__ == '__main__': | |
main() |