Update app.py

app.py

@@ -27,12 +27,12 @@ Shown is the stock prediction of the next working day taking into account the la
 
 model = keras.models.load_model('model_stock_prices.h5')
 
-working_days = st.sidebar.slider("Working days to take into account in the prediction", min_value = 10, max_value=30)
+working_days = st.sidebar.slider("Working days to take into account in the prediction", 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=30)
+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")
@@ -40,7 +40,7 @@ hist = nasdaq.history(start=days_ago, end=today)
 hist = hist.drop(columns=['Dividends', 'Stock Splits'])
 
 # keeping the last 10 data points
-hist = hist[-working_days:]
+hist = hist[-10:]
 
 
 inflation = []
@@ -75,7 +75,7 @@ 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:working_days])
+ds.append(temp_df[0:10])
 ds = np.array(ds)
 
 
@@ -103,11 +103,22 @@ prediction.append(final_prediction)
 print(prediction)
 plt.figure(figsize = (20,10))
 plt.plot(prediction, label="Prediction")
-plt.plot(hist['Close'].to_list()[-working_days:], label="Previous")
+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.pyplot(plt)
+
+
+
+
+st.write("""
+# Historical prices data
+Shown is the historical data of the prices (can be adapted with the values from the sidebar)
+""")
+
+temp_df
+