initial commit

app.py

@@ -13,7 +13,6 @@ def fetch_stock_data(ticker_symbol, start_date, end_date):
     stock_data = yf.download(ticker_symbol, start=start_date, end=end_date)
     df = stock_data[['Adj Close']].reset_index()
     df = df.rename(columns={'Date': 'ds', 'Adj Close': 'y'})
-    # df.to_csv(f"{ticker_symbol}.csv")
     return df
 
 def train_prophet_model(df):
@@ -90,20 +89,11 @@ def main():
         st.warning(f'The last available adjusted closing price for {ticker_symbol} on {end_date.strftime("%d %B %Y")} is **{actual_last_price:.2f}**.')
 
         if sentiment == 'Positive':
-            st.success(f'Overall predication indicates positive sentiment.')
+            st.success(f'**Predicted Price:** {latest_forecast['yhat']:.2f}. **Range:** {latest_forecast['yhat_lower']:.2f} -  {latest_forecast['yhat_upper']:.2f} \nOverall predication indicates positive sentiment for {forecast_horizon.lower()} time frame.')
         elif sentiment == 'Negative':
-            st.error(f'Overall predication indicates negative sentiment.')
+            st.error(f'f'**Predicted Price:** {latest_forecast['yhat']:.2f}. **Range:** {latest_forecast['yhat_lower']:.2f} -  {latest_forecast['yhat_upper']:.2f} \nOverall predication indicates negative sentiment for {forecast_horizon.lower()} time frame.')
         else:
-            st.info(f'Overall predication indicates neutral sentiment.')        
-
-        st.markdown(f"""
-            **Prediction for {forecast_horizon.lower()}:**
-            
-            - **Date:** {latest_forecast['ds'].strftime("%d %B %Y")}
-            - **Predicted Price:** {latest_forecast['yhat']:.2f}
-            - **Lower Bound:** {latest_forecast['yhat_lower']:.2f}
-            - **Upper Bound:** {latest_forecast['yhat_upper']:.2f}
-        """)
+            st.info(f'f'**Predicted Price:** {latest_forecast['yhat']:.2f}. **Range:** {latest_forecast['yhat_lower']:.2f} -  {latest_forecast['yhat_upper']:.2f} \nOverall predication indicates neutral sentiment for {forecast_horizon.lower()} time frame.')      
 
         st.markdown(f"""
             **Find below the prediction Data for the {forecast_horizon.lower()}:**