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app.py
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app.py
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import pandas as pd
import plotly.express as px
import streamlit as st
from streamlit_option_menu import option_menu
import yfinance as yf
import tensorflow as tf
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
from PIL import Image
from sklearn.preprocessing import MinMaxScaler
import numpy as np
st.set_page_config(
page_title="Stockulize",
page_icon=":bar_chart:",
layout="wide"
)
hide_st_style = """
<style>
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_st_style, unsafe_allow_html=True)
with st.sidebar:
selected_y = option_menu(
menu_title="Main Menu",
options=["Home Page","Download Stock Data(Nasdaq)", "Download Indian Stocks(NSE)", "Visualisation", "Upload Your Data", "Supported Companies", "ANN", "RNN", "RNN_2", "ANN for user", "RNN for user"],
menu_icon=["messenger"],
icons=["house-door","cloud-download-fill", "graph-up-arrow", "cloud-upload-fill", "building"],
default_index=0,
)
df = pd.read_csv(
'Google_Stock_Price_Test.csv',
engine = 'python',
)
# st.markdown('')
if selected_y == "Home Page":
image = Image.open('img/logo.png')
st.image(image, width=550, caption=None)
selected_nav = option_menu(
menu_title=None,
options=["Home", "About", "Github"],
icons=["house-door-fill", "info-circle-fill", "github"],
default_index=0,
orientation="horizontal",
)
if selected_nav == "Home":
st.header("Past,Present and Future")
st.subheader("See the history,current state and future of your favorite company.")
st.subheader("With easy to understand visual presentation of data and phenomenal prediction models,Make bold and confident decisions")
if selected_nav == "About":
st.header("This is about page")
if selected_nav == "Github":
st.header("Github Info")
if selected_y == "Visualisation":
st.markdown('')
st.subheader("Demo Graphs on Google Data")
selected = option_menu(
menu_title=None,
options=["Open|Close", "Low|High", "Adj Close|Volume"],
icons=["file-bar-graph", "file-bar-graph", "file-bar-graph"],
default_index=0,
orientation="horizontal",
)
fig_1 = px.line(df, x = 'Date', y = 'Open', title='Open')
fig_2 = px.line(df, x = 'Date', y = 'Close', title='Close')
fig_3 = px.line(df, x = 'Date', y = 'Low', title='Low')
fig_4 = px.line(df, x = 'Date', y = 'High', title='High')
fig_5 = px.line(df, x = 'Date', y = 'Adj Close', title='Adj Close')
fig_6 = px.line(df, x = 'Date', y = 'Volume', title='Volume')
left_column, right_column = st.columns(2)
if selected == "Open|Close":
left_column.plotly_chart(fig_1, use_container_width=True)
right_column.plotly_chart(fig_2, use_container_width=True)
if selected == "Low|High":
left_column.plotly_chart(fig_3, use_container_width=True)
right_column.plotly_chart(fig_4, use_container_width=True)
if selected == "Adj Close|Volume":
left_column.plotly_chart(fig_5, use_container_width=True)
right_column.plotly_chart(fig_6, use_container_width=True)
if selected_y == "Download Stock Data(Nasdaq)":
st.markdown('')
company = st.text_input('Company Name', placeholder="eg : GOOGL, AAPL, etc", type="default", autocomplete=None)
start_date = st.date_input('Start Date', value=None, min_value=None, max_value=None, key=None)
end_date = st.date_input('End Date', value=None, min_value=None, max_value=None, key=None)
if st.button("Fetch"):
data_df = yf.download(company, start_date, end_date)
name = company + '.csv'
d = data_df.to_csv(name)
with open(name, "rb") as file:
btn = st.download_button(
label="Download Dataset",
data=file,
file_name=name,
mime="text/csv",
)
if selected_y == "Download Indian Stocks(NSE)":
st.markdown('')
company = st.text_input('Company Name', placeholder="eg : RELIANCE, TATAMOTORS, etc", type="default", autocomplete=None)
company = company + ".NS"
start_date = st.date_input('Start Date', value=None, min_value=None, max_value=None, key=None)
end_date = st.date_input('End Date', value=None, min_value=None, max_value=None, key=None)
if st.button("Fetch"):
data_df = yf.download(company, start_date, end_date)
name = company + '.csv'
d = data_df.to_csv(name)
with open(name, "rb") as file:
btn = st.download_button(
label="Download Dataset",
data=file,
file_name=name,
mime="text/csv",
)
if selected_y == "Upload Your Data":
st.markdown('')
st.subheader("Let's Visualize Your Data")
data_file = st.file_uploader("Upload stock data as CSV",type=['csv'])
if st.button("Process"):
if data_file is not None:
file_details = {"Filename":data_file.name,"FileType":data_file.type,"FileSize":data_file.size}
st.write(file_details)
df1 = pd.read_csv(data_file)
st.dataframe(df1)
fig_u_1 = px.line(df1, x = 'Date', y = 'Open', title='Open')
fig_u_2 = px.line(df1, x = 'Date', y = 'Close', title='Close')
fig_u_3 = px.line(df1, x = 'Date', y = 'Low', title='Low')
fig_u_4 = px.line(df1, x = 'Date', y = 'High', title='High')
fig_u_5 = px.line(df1, x = 'Date', y = 'Adj Close', title='Adj Close')
fig_u_6 = px.line(df1, x = 'Date', y = 'Volume', title='Volume')
left_column_2, right_column_2 = st.columns(2)
left_column_2.plotly_chart(fig_u_1, use_container_width=True)
right_column_2.plotly_chart(fig_u_2, use_container_width=True)
left_column_2.plotly_chart(fig_u_3, use_container_width=True)
right_column_2.plotly_chart(fig_u_4, use_container_width=True)
left_column_2.plotly_chart(fig_u_5, use_container_width=True)
right_column_2.plotly_chart(fig_u_6, use_container_width=True)
if selected_y == "Supported Companies":
st.markdown('')
st.subheader("Demo Graphs on Google Data")
selected_support = option_menu(
menu_title=None,
options=["Nasdaq", "Nse", "Bse"],
icons=["file-bar-graph", "file-bar-graph", "file-bar-graph"],
default_index=0,
orientation="horizontal",
)
if selected_support == "Nasdaq":
comp = pd.read_csv('support/nasdaq.csv')
st.header("Supported Companies")
st.subheader("You can refer to the below data frame for getting the company symbol.")
col_list = list(comp["Name"])
col_list = tuple(col_list)
option = st.selectbox(
'Choose the company', col_list)
st.dataframe(comp)
if selected_support == "Nse":
comp = pd.read_csv('support/nse.csv')
st.header("Supported Companies")
st.subheader("You can refer to the below data frame for getting the company symbol.")
col_list = list(comp["NAME OF COMPANY"])
col_list = tuple(col_list)
option = st.selectbox(
'Choose the company', col_list)
st.dataframe(comp)
if selected_support == "Bse":
st.markdown('')
comp = pd.read_csv('support/bse.csv')
st.header("Supported Companies")
st.subheader("You can refer to the below data frame for getting the company symbol.")
col_list = list(comp["Security Code"])
col_list = tuple(col_list)
option = st.selectbox(
'Choose the company', col_list)
st.dataframe(comp)
if selected_y == "ANN":
try:
dataset_test = pd.read_csv('Google_Stock_Price_Test.csv')
X_test = dataset_test.iloc[:-1, 1:2].values
y_test = dataset_test.iloc[1:, 1:2].values
ann = tf.keras.models.load_model('ann.h5')
y_pred = ann.predict(X_test)
fig = Figure()
plt = fig.add_subplot(1, 1, 1)
plt.plot(y_test, color='red', label='Real Google Stock Price')
plt.plot(y_pred, color='blue', label='Predicted Google Stock Price')
plt.set_title('Google Stock Price Prediction')
plt.set_xlabel('Time')
plt.set_ylabel('Google Stock Price')
fig.savefig('img/annGraph.png')
print('ANN graph saved to img/annGraph.png')
except Exception as e:
print('An error occurred while running the ANN prediction:')
print(e)
image = Image.open('img/annGraph.png')
st.image(image, caption='ANN')
if selected_y == "RNN":
regressor = tf.keras.models.load_model('rnn_old.h5')
dataset_train = pd.read_csv('Google_Stock_Price_Train.csv')
# creating a numarray that contains the open price of the stock
trainig_set = dataset_train.iloc[:, 1:2].values
"""### Feature Scaling"""
# all stock prices will be between 0 and 1
sc = MinMaxScaler(feature_range=(0, 1))
training_set_scaled = sc.fit_transform(trainig_set)
dataset_train = pd.read_csv('Google_Stock_Price_Train.csv')
dataset_test = pd.read_csv('Google_Stock_Price_Test.csv')
real_stock_price = dataset_test.iloc[:, 1:2].values
"""### Getting the predicted stock price"""
dataset_total = pd.concat(
(dataset_train['Open'], dataset_test['Open']), axis=0)
inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values
inputs = inputs.reshape(-1, 1)
inputs = sc.transform(inputs)
X_test = []
for i in range(60, 80):
X_test.append(inputs[i-60:i, 0])
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
predicted_stock_price = regressor.predict(X_test)
predicted_stock_price = sc.inverse_transform(predicted_stock_price)
# plotting the figure
fig_rnn = Figure()
plt = fig_rnn.add_subplot(1, 1, 1)
plt.plot(real_stock_price, color='red', label='Real Google Stock Price')
plt.plot(predicted_stock_price, color='blue',
label='Predicted Google Stock Price')
plt.set_title('Google Stock Price Prediction')
plt.set_xlabel('Time')
plt.set_ylabel('Google Stock Price')
fig_rnn.savefig('img/RNN1Graph.png')
image = Image.open('img/RNN1Graph.png')
st.image(image, caption='RNN')
if selected_y == "RNN_2":
regressor = tf.keras.models.load_model('rnn_new.h5')
dataset_train = pd.read_csv('Google_Stock_Price_Train.csv')
# creating a numarray that contains the open price of the stock
my_train = dataset_train.iloc[:, 1:]
training_set = my_train.replace(",", "", regex=True)
from sklearn.preprocessing import MinMaxScaler
# all stock prices will be between 0 and 1
sc = MinMaxScaler(feature_range=(0, 1))
training_set_scaled = sc.fit_transform(training_set)
# print(training_set_scaled)
X_train = []
y_train = []
# we will select the first 60 values to predict the first value and so on
for i in range(60, 1258):
# will put first 60 value in x
X_train.append(training_set_scaled[i-60:i])
# will put the value we will predict
y_train.append(training_set_scaled[i, 0])
X_train, y_train = np.array(X_train), np.array(y_train)
dataset_test = pd.read_csv('Google_Stock_Price_Test.csv')
real_stock_price = dataset_test.iloc[:, 1:2].values
data_set1 = dataset_train.iloc[:, 1:]
data_set2 = dataset_test.iloc[:, 1:]
dataset_total = pd.concat((data_set1, data_set2), axis=0)
dataset_total = dataset_total.replace(",", "", regex=True)
inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values
inputs = sc.transform(inputs)
X_test = []
for i in range(60, 80):
X_test.append(inputs[i-60:i])
X_test = np.array(X_test)
# # X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
predicted_stock_price = regressor.predict(X_test)
# predicted_stock_price = sc.inverse_transform(predicted_stock_price)
predicted_stock_price = predicted_stock_price*(1/1.86025746e-03)
predicted_stock_price = predicted_stock_price+280
# plotting the figure
fig_rnnNew = Figure()
plt = fig_rnnNew.add_subplot(1, 1, 1)
plt.plot(real_stock_price, color='red', label='Real Google Stock Price')
plt.plot(predicted_stock_price, color='blue',
label='Predicted Google Stock Price')
plt.set_title('Google Stock Price Prediction')
plt.set_xlabel('Time')
plt.set_ylabel('Google Stock Price')
fig_rnnNew.savefig('img/RNN2Graph.png')
image = Image.open('img/RNN2Graph.png')
st.image(image, caption='RNN 2')
if selected_y == "ANN for user":
data_file_user = st.file_uploader("Upload stock data as CSV",type=['csv'])
if st.button("Predict"):
if data_file_user is not None:
file_details = {"Filename":data_file_user.name,"FileType":data_file_user.type,"FileSize":data_file_user.size}
st.write(file_details)
dataset_test = pd.read_csv(data_file_user)
X_test = dataset_test.iloc[:-1, 1:2].values
y_test = dataset_test.iloc[1:, 1:2].values
ann = tf.keras.models.load_model('ann.h5')
y_pred = ann.predict(X_test)
fig = Figure()
plt = fig.add_subplot(1, 1, 1)
plt.plot(y_test, color='red', label='Real Stock Price')
plt.plot(y_pred, color='blue', label='Predicted Stock Price')
plt.set_title('Stock Price Prediction')
plt.set_xlabel('Time')
plt.set_ylabel('Stock Price')
fig.savefig('user/annGraph.png')
image = Image.open('user/annGraph.png')
st.image(image, caption='Your ANN Graph')
if selected_y == "RNN for user":
data_file_user = st.file_uploader("Upload stock data as CSV",type=['csv'])
if st.button("Predict"):
if data_file_user is not None:
file_details = {"Filename":data_file_user.name,"FileType":data_file_user.type,"FileSize":data_file_user.size}
st.write(file_details)
regressor = tf.keras.models.load_model('rnn_old.h5')
dataset_train = pd.read_csv('Google_Stock_Price_Train.csv')
# creating a numarray that contains the open price of the stock
training_set = dataset_train.iloc[:, 1:2].values
"""### Feature Scaling"""
# all stock prices will be between 0 and 1
sc = MinMaxScaler(feature_range=(0, 1))
training_set_scaled = sc.fit_transform(training_set)
dataset_train = pd.read_csv('Google_Stock_Price_Train.csv')
dataset_test = pd.read_csv(data_file_user)
real_stock_price = dataset_test.iloc[:, 1:2].values
"""### Getting the predicted stock price"""
dataset_total = pd.concat(
(dataset_train['Open'], dataset_test['Open']), axis=0)
inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values
inputs = inputs.reshape(-1, 1)
inputs = sc.transform(inputs)
X_test = []
for i in range(60, 60+real_stock_price.size-1):
X_test.append(inputs[i-60:i, 0])
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
predicted_stock_price = regressor.predict(X_test)
predicted_stock_price = sc.inverse_transform(predicted_stock_price)
# plotting the figure
fig_rnn = Figure()
plt = fig_rnn.add_subplot(1, 1, 1)
plt.plot(real_stock_price, color='red', label='Real Stock Price')
plt.plot(predicted_stock_price, color='blue',
label='Predicted Stock Price')
plt.set_title('Stock Price Prediction')
plt.set_xlabel('Time')
plt.set_ylabel('Stock Price')
fig_rnn.savefig('user/RNN1Graph.png')
image = Image.open('user/RNN1Graph.png')
st.image(image, caption='RNN')