Update app.py

app.py

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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 = ''
+ 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()