app.py

from cProfile import label
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
from datetime import date

start = '2015-01-01'
end = date.today().strftime("%Y-%m-%d")
st.title('Stock Prediction')
user_input = st.text_input('Enter Stock Ticker','AMZN')
df = data.DataReader(user_input,'yahoo',start,end)

st.subheader('Data from 2015-2022')
st.write(df.describe())

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

st.subheader('Closing Price vs Time Chart with 50MA')
ma50 = df.Close.rolling(50).mean()
fig = plt.figure(figsize = (12,6))
plt.plot(ma50)
plt.plot(df.Close)
st.pyplot(fig)

st.subheader('Closing Price vs Time Chart with 50MA&100MA')
ma50 = df.Close.rolling(50).mean()
ma100 = df.Close.rolling(100).mean()
fig = plt.figure(figsize = (12,6))
plt.plot(ma100)
plt.plot(ma50)
plt.plot(df.Close)
st.pyplot(fig)

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)

model = load_model('keras_model.h5')

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

st.subheader('Predictions 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)