app.py

import pandas as pd
import joblib
from huggingface_hub import HfApi
import pickle
import yfinance as yf
import keras
from datetime import datetime, timedelta
from forex_python.converter import get_rate
import pandas as pd
import numpy as np
import cpi
from sklearn.preprocessing import MinMaxScaler
from huggingface_hub import hf_hub_download
import gradio as gr
from huggingface_hub import notebook_login
notebook_login()
import hopsworks
from datetime import date
import matplotlib.pyplot as plt
import streamlit as st


st.write("""
# Stock Price Prediction
Shown is the stock prediction of the next working day taking into account the last 10 working days
""")

model = keras.models.load_model('model_stock_prices.h5')

working_days = st.sidebar.slider("Show the historical data of the following last working days", min_value = 10, max_value=20)
working_days = int(working_days)
# downloading the last 10 days to make the prediction

today = date.today()
days_ago = today - timedelta(days=20)

# we get the last 20 days and keep just the last 10 working days, which have prices
nasdaq = yf.Ticker("^IXIC")
hist = nasdaq.history(start=days_ago, end=today)
hist = hist.drop(columns=['Dividends', 'Stock Splits'])

# keeping the last 10 data points
hist = hist[-10:]


inflation = []
for t in hist.index:
  inflation.append(get_rate("USD", "EUR", t))

cpi_items_df = cpi.series.get(seasonally_adjusted=False).to_dataframe()
cpi_items_df = cpi_items_df[cpi_items_df['period_type']=='monthly']
cpi_items_df['date'] = pd.to_datetime(cpi_items_df['date'])
cpi_items_df = cpi_items_df.set_index('date')
cpi_df = cpi_items_df['value'].loc['2022':'2023']

cpi_col = []
for x in hist.index:
  # ts = datetime(x.year, x.month, 1)

  # just adding the latest inflation rate
  cpi_col.append(cpi_df[-1])

hist['Inflation'] = inflation
hist['CPI'] = cpi_col
hist['Quarter_end'] = np.where(hist.index.month%3==0,1,0)




s = hf_hub_download(repo_id="marvmk/scalable_project", filename="scaler.save", repo_type='dataset')
scaler = joblib.load(s)

inp = scaler.transform(hist.to_numpy())

df = inp
temp_df = pd.DataFrame(inp, columns = ['Open','High','Low','Close','Volume','Inflation', 'CPI', 'Quarter_end'])
ds = []
ds.append(temp_df[0:10])
ds = np.array(ds)



predictions = model.predict(ds)
p = predictions[0][0][0]
p = float(p)
a = np.array([0,0,0,p,0,0,0,0])
a = scaler.inverse_transform(a.reshape(1,-1))


final_prediction = a[-1][3]



prediction = []
#prediction.append(final_prediction)
close = hist['Close'].to_list()
print(close)
for c in close:
  prediction.append(c)


prediction.append(final_prediction)
print(prediction)
plt.figure(figsize = (20,10))
plt.plot(prediction, label="Prediction")
plt.plot(hist['Close'].to_list()[-10:], label="Previous")
plt.ylabel('Price US$', fontsize = 15 )
plt.xlabel('Working Days', fontsize = 15 )
plt.title("NASDAQ Stock Prediction", fontsize = 20)
plt.legend()
plt.grid()

st.pyplot(plt)




st.write("""
# Historical prices data
Shown is the historical data of the prices (can be adapted with the values from the sidebar)
""")

today = date.today()
days_ago = today - timedelta(days=25)

# we get the last 30 days and keep just the last working days, which have prices
nasdaq = yf.Ticker("^IXIC")
hist = nasdaq.history(start=days_ago, end=today)
hist = hist.drop(columns=['Dividends', 'Stock Splits'])

# keeping the last working days data points
hist = hist[-working_days:]


inflation = []
for t in hist.index:
  inflation.append(get_rate("USD", "EUR", t))

cpi_items_df = cpi.series.get(seasonally_adjusted=False).to_dataframe()
cpi_items_df = cpi_items_df[cpi_items_df['period_type']=='monthly']
cpi_items_df['date'] = pd.to_datetime(cpi_items_df['date'])
cpi_items_df = cpi_items_df.set_index('date')
cpi_df = cpi_items_df['value'].loc['2022':'2023']

cpi_col = []
for x in hist.index:
  # ts = datetime(x.year, x.month, 1)

  # just adding the latest inflation rate
  cpi_col.append(cpi_df[-1])

hist['Inflation'] = inflation
hist['CPI'] = cpi_col
hist['Quarter_end'] = np.where(hist.index.month%3==0,1,0)

hist