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import streamlit as st
import numpy as np
import pickle
import streamlit.components.v1 as components
# Load the pickled model
def load_model():
return pickle.load(open('Dogecoin_price_Prediction_li.pkl', 'rb'))#change
# Function for model prediction
def model_prediction(model, features):
predicted = str(list(model.predict(features)[0]))
return predicted
def app_design():
# Add input fields for High, Open, and Low values
image = '11.png' #change
st.image(image, use_column_width=True)
st.subheader("Enter the following values:") #change
Open = st.number_input("Open")
High = st.number_input("High")
Low = st.number_input("Low")
AdjClose = st.number_input("Adjacent Close")
Volume = st.number_input("Volume")
# Create a feature list from the user inputs
features = [[Open,High,Low,AdjClose,Volume]]
# 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 Dogecoin price is: {predicted_value}")
def about_hidevs():
components.html("""
<div>
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</div>
""",
height=600)
def main():
# Set the app title and add your website name and logo
st.set_page_config(
page_title="Dogecoin Prediction",
page_icon=":chart_with_upwards_trend:",
)
st.title("Welcome to our Dogecoin Prediction App!")
app_design()
st.header("About HiDevs Community")
about_hidevs()
if __name__ == '__main__':
main() |