main.py

import streamlit as st
from datetime import date
import pandas as pd
import yfinance as yf
import numpy as np
import plotly.graph_objs as go
from prophet import Prophet
from prophet.plot import plot_plotly

# Calculate Mean Absolute Percentage Error (MAPE)
def calculate_mape(actual, predicted):
    actual = np.array(actual)
    predicted = np.array(predicted)
    mape = np.mean(np.abs((actual - predicted) / actual)) * 100
    return mape

switch = st.checkbox("LIVE DATA")
if switch:
    st.write("LIVE UPDATES IN STOCKS...")

    START = "2010-01-01"
    TODAY = date.today().strftime("%Y-%m-%d")

    st.title('Stock Price Predictor')
    st.text("created by CECians...")

    stocks = ('GOOG', 'AAPL', 'MSFT', 'GME', 'TCS.NS','AMZN')
    selected_stock = st.selectbox('Select dataset for prediction', stocks)

    n_years = st.slider('Years of prediction:', 1, 5)
    period = n_years * 365 

    def load_data(ticker):
        data = yf.download(ticker, START, TODAY)
        data.reset_index(inplace=True)
        return data

    data_load_state = st.text('Loading data...')
    data = load_data(selected_stock)
    data_load_state.text('Loading data... done!')

    if st.checkbox('Show raw data'):
        st.subheader('Raw data')
        st.write(data)
    

    def plot_raw_data():
        fig = go.Figure()
        fig.add_trace(go.Scatter(x=data['Date'], y=data['Open'], name="stock_open"))
        fig.add_trace(go.Scatter(x=data['Date'], y=data['Close'], name="stock_close"))
        fig.layout.update(title_text='Time Series data with Rangeslider', xaxis_rangeslider_visible=True)
        st.plotly_chart(fig)

    plot_raw_data()

    # Predict forecast with Prophet.
    df_train = data[['Date','Close']]
    df_train = df_train.rename(columns={"Date": "ds", "Close": "y"})

    m = Prophet()
    m.fit(df_train)
    future = m.make_future_dataframe(periods=period)
    forecast = m.predict(future)


    if st.checkbox('Show forecast data'):
        st.subheader('forecast data')
        st.write(forecast)
        
    
    st.write('Forecasting closing of stock value for', selected_stock, 'for a period of:', str(n_years), 'year')
    fig1 = plot_plotly(m, forecast)
    st.plotly_chart(fig1)

    # Calculate and display MAPE
    actual_prices = data['Close'].values[-period:]
    predicted_prices = forecast['yhat'].values[-period:]
    mape = calculate_mape(actual_prices, predicted_prices)
    mae = np.mean(np.abs(predicted_prices - actual_prices))
    st.write('Mean Absolute Percentage Error (MAPE):', round(mape, 2), '%')
    st.write(f"Mean Absolute Error (MAE): {mae:.2f}")
    Accuracy=100-mape

    st.write('Accuracy :', round(Accuracy, 2), '%')
    st.write("Forecast components")
    fig2 = m.plot_components(forecast)
    st.write(fig2)

    

else:
    st.write("BASED ON HISTORICAL DATA...")

    st.title('Stock Price Predictor')
    st.text("created by CECians...")

    dataset = ("ADANIENT","AXISBANK","CANBK","BAJFINANCE","SUNTV","TATAMOTORS","TVSMOTOR","VGUARD","VOLTAS","WIPRO","SIEMENS")
    selected_stock = st.selectbox('Select dataset for prediction', dataset)
    DATA_URL = ('./HISTORICAL_DATA/'+selected_stock+'_data.csv')

    year = st.slider('Year of prediction:', 1, 5)
    period = year * 365
     
    def load_data():
        data = pd.read_csv(DATA_URL)
        return data

    data_load_state = st.text('Loading data...')
    data = load_data()
    data_load_state.text('Loading data... done!')

    def plot_raw_data():
        fig = go.Figure()
        fig.add_trace(go.Scatter(x=data['Date'], y=data['open'], name="stock_open"))
        fig.add_trace(go.Scatter(x=data['Date'], y=data['close'], name="stock_close"))
        fig.layout.update(title_text='Time Series data with Rangeslider', xaxis_rangeslider_visible=True)
        st.plotly_chart(fig)
   
    if st.checkbox('Show raw data'):
        st.subheader('Raw data')
        st.write(data)

    plot_raw_data()

    # preparing the data for Facebook-Prophet.
    data_pred = data[['Date','close']]
    data_pred=data_pred.rename(columns={"Date": "ds", "close": "y"})

    # code for Facebook Prophet prediction
    m = Prophet()
    m.fit(data_pred)
    future = m.make_future_dataframe(periods=period)
    forecast = m.predict(future)

    # plot forecast
    fig1 = plot_plotly(m, forecast)
    if st.checkbox('Show forecast data'):
        st.subheader('forecast data')
        st.write(forecast)
    st.write('Forecasting closing of stock value for', selected_stock, 'for a period of:', str(year), 'year')    
    st.plotly_chart(fig1)
    
    # Calculate and display MAPE
    actual_prices = data_pred['y'].values[-period:]
    predicted_prices = forecast['yhat'].values[-period:]
    mape = calculate_mape(actual_prices, predicted_prices)
    mae = np.mean(np.abs(predicted_prices - actual_prices))
    st.write('Mean Absolute Percentage Error (MAPE):', round(mape, 2), '%')
    st.write(f"Mean Absolute Error (MAE): {mae:.2f}")
    Accuracy=100-mape
    st.write('Accuracy :', round(Accuracy, 2), '%')

    # plot component wise forecast
    st.write("Component wise forecast")
    fig2 = m.plot_components(forecast)
    st.write(fig2)