app.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt 
import yfinance as yf
import datetime as date
from keras.models import load_model
import streamlit as st


st.title('Stock trend Prediction')

user_input = st.text_input('Enter Stock Ticker',"SBIN.NS")
yf.pdr_override()
df = yf.download(user_input, start='2015-01-01', end=date.datetime.now())

#Describing Data
st.subheader('Data from 2015 - till now')
st.write(df.describe())

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

#Moving Averages
st.subheader('Closing Price vs Time Chart with 100MA')
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)
plt.plot(ma200)
plt.plot(df.Close)
st.pyplot(fig)


#Splitting Data into Training and Tensting(70%data for training & 30% data is for 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
#make sure model in the same folder
model = load_model('keras_model.h5')

#testing Part
past_100_days = data_training.tail(100)
final_df = pd.concat([past_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

st.subheader('Predictions Vs Original')
fig =  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(fig)