Create app.py

app.py

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+import pandas as pd
+import joblib
+from huggingface_hub import HfApi
+import pickle
+import yfinance as yf
+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
+import matplotlib.pyplot as plt
+import streamlit as st
+
+#cpi.update()
+
+# from huggingface_hub import notebook_login
+# notebook_login()
+
+from huggingface_hub import hf_hub_download
+m = hf_hub_download(repo_id="marvmk/model-test", filename="model.pkl")
+model = pickle.load(open(m, 'rb'))
+
+# downloading the last 10 days to make the prediction
+from datetime import date
+
+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)
+predictions
+p = predictions[0][0]
+print(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)