Update app.py

app.py

@@ -12,15 +12,12 @@ from tensorflow.keras.layers import Activation, Dense, Dropout, LSTM
 from datetime import date, datetime, timedelta
 from stocknews import StockNews
 
-
-
 # --- SIDEBAR CODE
-ticker = st.sidebar.selectbox('Select your Crypto', ["BTC-USD", "ETH-USD", "BNB-USD","XRP-USD","ADA-USD","DOT-USD","DOGE-USD","SOL-USD"])
+ticker = st.sidebar.selectbox('Select your Crypto', ["BTC-USD", "ETH-USD", "BNB-USD", "XRP-USD", "ADA-USD", "DOT-USD", "DOGE-USD", "SOL-USD"])
 
 start_date = st.sidebar.date_input('Start Date', date.today() - timedelta(days=365))
 end_date = st.sidebar.date_input('End Date')
 
-
 # --- MAIN PAGE
 st.header('Cryptocurrency Prediction')
 
@@ -28,11 +25,10 @@ col1, col2, = st.columns([1,9])
 with col1:
   st.image('icons/'+ ticker +'.png', width=75)
 with col2:
-  st.write(f" ## { ticker}")
+  st.write(f" ## {ticker}")
 
 ticker_obj = yf.Ticker(ticker)
 
-
 # --- CODE
 
 model_data = ticker_obj.history(interval='1h', start=start_date, end=end_date)
@@ -60,7 +56,6 @@ def build_lstm_model(input_data, output_size, neurons, activ_func='linear', drop
 
     return model
 
-
 # Hyperparameters
 np.random.seed(245)
 window_len = 10
@@ -83,7 +78,6 @@ def extract_window_data(input_data, target_data, window_len):
 
 X, y = extract_window_data(input_data_normalized, target_data_normalized, window_len)
 
-
 # Split the data into training and testing sets
 split_ratio = 0.8  # Ratio of training set to total data
 split_index = int(split_ratio * len(X))
@@ -126,7 +120,6 @@ fig.update_layout(
     legend_title=" ")
 st.write(fig)
 
-
 # --- INFO BUBBLE
 
 about_data, news = st.tabs(["About", "News"])
@@ -153,7 +146,6 @@ with about_data:
   history_data.sort_values(by='Date', ascending=False, inplace=True)
   st.write(history_data)
 
-
 with news:
   sNews = StockNews(ticker, save_news=False)
   sNews_df = sNews.read_rss()
@@ -163,4 +155,4 @@ with news:
     st.subheader(f"{i+1} - {sNews_df['title'][i]}")
     st.write(sNews_df['summary'][i])
     date_object = datetime.strptime(sNews_df['published'][i], '%a, %d %b %Y %H:%M:%S %z')
-    st.write(f"_{date_object.strftime('%A')}, {date_object.date()}_")
+    st.write(f"_{date_object.strftime('%A')}, {date_object.date()}_")