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c2af349 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | import streamlit as st
import numpy as np
import pickle
# Function to load the model from pickle
def load_model(path):
with open(path, 'rb') as f:
model = pickle.load(f)
return model
# Define the main function for predicting stock price
def predict_stock_price(open_val, high_val, low_val, close_val, adj_close_val, volume, model):
try:
# Prepare the input data as a numpy array
X = np.array([[open_val, high_val, low_val, close_val, adj_close_val]])
# Use the loaded model for prediction
predicted_price = model.predict(X)
return predicted_price[0]
except (ValueError, AttributeError) as e:
st.error(f'Error occurred: {str(e)}')
return None
# Define the Streamlit app
def main():
st.title('Stock Prediction')
# Load your trained model from pickle
model_path = 'model.pkl' # Replace with your actual path
model = load_model(model_path)
# Input fields for user to enter stock data
open_val = st.number_input('Open (Range: 0 to 100000)', min_value=0.0, max_value=100000.0, value=0.0)
high_val = st.number_input('High (Range: 0 to 100000)', min_value=0.0, max_value=100000.0, value=0.0)
low_val = st.number_input('Low (Range: 0 to 100000)', min_value=0.0, max_value=100000.0, value=0.0)
close_val = st.number_input('Close* (Range: 0 to 100000)', min_value=0.0, max_value=100000.0, value=0.0)
adj_close_val = st.number_input('Adj Close** (Range: 0 to 100000)', min_value=0.0, max_value=100000.0, value=0.0)
volume = st.number_input('Volume (Range: 0 to 1,000,000,000)', min_value=0.0, max_value=1000000000.0, value=0.0)
# Predict button
if st.button('Predict'):
if any([open_val == 0.0, high_val == 0.0, low_val == 0.0, close_val == 0.0, adj_close_val == 0.0, volume == 0.0]):
st.error('Please enter valid numeric values for all fields.')
else:
# Call prediction function
predicted_price = predict_stock_price(open_val, high_val, low_val, close_val, adj_close_val, volume, model)
if predicted_price is not None:
st.success(f'The predicted stock price is: {predicted_price:.2f}')
else:
st.error('Failed to predict stock price.')
if __name__ == '__main__':
main()
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