push

app.py

@@ -1,20 +1,26 @@
 import seaborn as sns
 import streamlit as st
+from datetime import datetime, timedelta
 from src import stock_data, model_train, train_test_split, model_predict
+
 st.image('images.jpg', caption='Image credit : Kelly Sikkema')
 # Function to display result, history, and data information
 def display_results(user_input,result, data_inf, train):
-    st.header(f'Here is the data for {user_input} the past 10 days.')
+    st.header(f'Here is the data for {user_input.upper()} the past 10 days.')
     st.write(data_inf)
+    plot = sns.lineplot(train)
+    st.pyplot(plot.get_figure())
+    future_date = datetime.now() + timedelta(days=1)
+    formatted_date = future_date.strftime('%Y-%m-%d')
     st.markdown(f'''
-        Stock Prediction Analysis for <span style="font-size:24px;">{user_input.upper()}</span>
+        ## Stock Prediction Analysis for <span style="font-size:24px;">{user_input.upper()}</span>
         
         <p style="font-size:24px;">
-        Tomorrow's {user_input.upper()} predicted price is : <b>{round(result,2)}</b>
+        {formatted_date} {user_input.upper()} predicted price is : <b>{round(result,2)}</b>
         </p>
     ''', unsafe_allow_html=True)
-    plot = sns.lineplot(train)
-    st.pyplot(plot.fig)
+    
+    
     
     
 # Main function to run the app

src/get_data.py

@@ -1,8 +1,8 @@
 import yfinance as yf
 from datetime import datetime, timedelta
-from sklearn.preprocessing import MinMaxScaler
 import numpy as np
 
+
 def stock_data (tick):
   data = yf.download(tick, start=datetime.now() - timedelta(days=365),end=datetime.now())
   return data.Close