import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
import yfinance as yf
yf.pdr_override()
from pandas_datareader import data as pdr
import tensorflow as tf
from keras.models import  load_model
import streamlit as st 

# Start and the End dates and the stock ticker
start = '2005-01-01'
end = '2022-12-31'
stock_ticker = 'TATAPOWER.NS'

st.title("Stock Market Trend Predictor")
use_input = st.text_input('Enter stock Ticker', stock_ticker)
if st.button('Analyze'):
    df = pdr.get_data_yahoo(use_input, start)

    #describing data 
    st.subheader('Data From 2010-2023')
    st.write(df.describe())

    #maps 

    st.subheader('closing Price VS Time Chart ')
    fig = plt.figure(figsize=(10,5))
    plt.plot(df.Close , color = 'yellow')
    plt.legend()
    st.pyplot(fig)

    st.subheader('closing Price VS Time Chart with 100 moving Average  ')
    ma100= df.Close.rolling(100).mean()
    fig = plt.figure(figsize=(10,5))
    plt.plot(ma100, color = 'red')
    plt.plot(df.Close , color = 'yellow')
    plt.legend()
    st.pyplot(fig)


    st.subheader('closing Price VS Time Chart with 100 & 200 moving Average  ')
    ma100= df.Close.rolling(100).mean()
    ma200= df.Close.rolling(200).mean()
    fig = plt.figure(figsize=(10,5))
    plt.plot(ma100 , color = 'red')
    plt.plot(ma200, color = 'green')
    plt.plot(df.Close , color = 'yellow')
    plt.legend()
    st.pyplot(fig)


    #spltting data into train test 
    data_training = pd.DataFrame(df['Close'][0:int(len(df)*0.70)])
    data_testing = pd.DataFrame(df['Close'][int(len(df)*0.70):int(len(df))])

    print(' taining ', data_training.shape)
    print(' testing ', data_testing.shape)

    from sklearn.preprocessing import MinMaxScaler
    scaler = MinMaxScaler(feature_range = (0,1))

    data_training_array = scaler.fit_transform(data_training)




    #load Model 

    model = load_model('keras_model.h5')

    #testing past 
    pass_100_days = data_training.tail(100)

    final_df = pd.concat([pass_100_days, data_testing], ignore_index=True)

    input_data = scaler.fit_transform(final_df)

    x_test = []
    y_test = []

    for i in range(100 , input_data.shape[0]):
        x_test.append(input_data[i-100:i])
        y_test.append(input_data[i,0])
    x_test, y_test = np.array(x_test), np.array(y_test)

    y_predicted = model.predict(x_test)

    scaler = scaler.scale_
    scale_factor = 1/scaler[0]
    y_predicted = y_predicted*scale_factor
    y_test = y_test*scale_factor


    #final graph 
    def plot_transparent_graph():
        st.subheader('prediction vs Original')
        fig2 = plt.figure(figsize= (12,6))
        plt.plot(y_test , 'b', label = 'Original Price')
        plt.plot(y_predicted , 'r', label = 'prdicted Price')
        plt.style.use('dark_background')
        plt.xlabel('time')
        plt.ylabel('price')
        plt.legend()
        st.pyplot(fig2)


    def main():
        st.title('Stock Price Predicted Analysis')
        
        # Call the function to plot the transparent graph
        plot_transparent_graph()

        # Other interactive elements and text can be added here as needed
        # ...

    if __name__ == "__main__":
        main()