app.py

# -*- coding: utf-8 -*-
"""
Created on Mon Jun 22 14:51:24 2020

@author: sumit joshi
"""


from PIL import Image
from nsepy import get_history
from datetime import date
import datetime
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import streamlit as st
from keras.models import load_model

def main():
    
    image = Image.open('sm.jpg')
    
    st.image(image,use_column_width=True)
    

    st.title("NSE Real-Time Stocks Analysis and Predictions")
    
    
    st.header("Select the stock and check its next day predicted value")
    
    choose_stock = st.sidebar.selectbox("Choose the Stock!",["NONE","Reliance", "PowerMech Solns.", 'RepcoHomes'])
    

    if(choose_stock == "Reliance"):
        df1 = get_history(symbol='reliance', start=date(2010,1,1), end=date.today())
        df1['Date'] = df1.index
        st.header("Reliance India NSE Last 5 Days DataFrame:")
       
         # Insert Check-Box to show the snippet of the data.
        if st.checkbox('Show Raw Data'):
            
            st.subheader("Showing raw data---->>>")
            st.dataframe(df1.tail())
             ## Predictions and adding it to Dashboard
            new_close_col = df1.filter(['Close'])
            mm_scale = MinMaxScaler(feature_range=(0, 1))
            mm_scale_data = mm_scale.fit_transform(new_close_col)
            new_close_col_val = new_close_col[-30:].values
            new_close_col_val_scale = mm_scale.transform(new_close_col_val)
            X_test = []
            X_test.append(new_close_col_val_scale)
            X_test = np.array(X_test)
            X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
            network = load_model("Reliance.model")
            new_preds = network.predict(X_test)
            new_preds = mm_scale.inverse_transform(new_preds)
        #print(new_preds[0])
          # next day
            NextDay_Date = datetime.date.today() + datetime.timedelta(days=1)

            st.subheader("Predictions for the next upcoming day Close Price : " + str(NextDay_Date))
            st.markdown(new_preds[0][0])

            st.subheader("Close Price VS Date Interactive chart for analysis:")
            st.area_chart(df1['Close'])

            st.subheader("Line chart of Open and Close for analysis:")
            st.area_chart(df1[['Open','Close']])
            st.subheader("Line chart of High and Low for analysis:")
            st.line_chart(df1[['High','Low']])
       
         
            
          
            
       



if __name__=='__main__':
    main()