app.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_datareader as data
from keras.models import load_model
import streamlit as st

start= '2010-01-01'
end = '2020-12-31'

st.title('Stock Prediction')


user_input=st.text_input('Enter Stock Ticker','AAPL')
df= data.DataReader(user_input,'yahoo',start,end)

#discribing data
st.subheader('Data from 2010 - 2019')
st.write(df.describe())

#visualization
st.subheader('Closing Price vs Time Chart')
fig = plt.figure(figsize = (12,6))
plt.plot(df.Close)
st.pyplot(fig)

st.subheader('Closing Price vs Time Chart With 100 MA')
ma100=df.Close.rolling(100).mean()
fig = plt.figure(figsize=(12, 6))
plt.plot(ma100)
plt.plot(df.Close)
st.pyplot(fig)

st.subheader('Closing Price vs Time Chart With 100MA & 200MA')
ma100 = df.Close.rolling(100).mean()
ma200 = df.Close.rolling(200).mean()
fig = plt.figure(figsize = (12,6))
plt.plot(ma100,'r')
plt.plot(ma200,'g')
plt.plot(df.Close , 'b')
st.pyplot(fig)


#splitting Data into Training and Testing

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))])

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

data_training_array= scaler.fit_transform(data_training)



#load my model
model = load_model('keras_model.h5')

#testing part

past_100_days=data_training.tail(100)
final_df=past_100_days.append(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

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='Predicted Price')
plt.xlabel('Time')
plt.ylabel('Price')
plt.legend()
st.pyplot(fig2)