Update app.py

app.py

@@ -80,7 +80,7 @@ def convert_str_to_list(string):
         return string
 
 
-# Add Title and Logo
+# Extract Answer from LLM response
 def get_answer(text):
   text = response['result']
   helpful_answer_index = text.find('Helpful Answer:')
@@ -99,7 +99,8 @@ def response_generator(answer):
         time.sleep(0.05)
 
 
-# WebApp
+# ---- WebApp ----
+# Add Title and Logo
 title_container = st.container(border=False)        # Create a container to hold the tile and logo 
 col1, col2 = title_container.columns([0.2, 0.8], gap='medium')        # Create columns to display logo and title side-by-side
 col1.image("logo.png")        # Add logo to the 1st column
@@ -185,6 +186,7 @@ with market_analysis:
                 ])
             )
         )
+        # Update y-axis to allow vertical scrolling and dragging
         fig.update_yaxes(fixedrange=False)
         
         # Show chart on WebApp
@@ -225,6 +227,7 @@ with market_analysis:
                 ])
             )
         )
+        # Update y-axis to allow vertical scrolling and dragging
         fig.update_yaxes(fixedrange=False)
         
         # Show chart on WebApp
@@ -282,6 +285,7 @@ with market_analysis:
                 ])
             )
         )
+        # Update y-axis to allow vertical scrolling and dragging
         fig.update_yaxes(fixedrange=False)
         
         # Show chart on WebApp
@@ -333,6 +337,7 @@ with market_analysis:
                 ])
             )
         )
+        # Update y-axis to allow vertical scrolling and dragging
         fig.update_yaxes(fixedrange=False)
         st.plotly_chart(fig)
     # st.write(data2)
@@ -348,17 +353,21 @@ with news_analysis:
     # Load News Events data
     data_file_path = r"Events_SameDay.csv"  # Update this with your file path
     events = pd.read_csv(data_file_path, encoding="ISO-8859-1", lineterminator='\n')
-    print(events.columns)
 
     # Convert 'Date' column to datetime format
     events['Date'] = pd.to_datetime(events['Date'])
+
+    # Filter data based on the date selected by the user
     events = events[(events['Date'] >= start_date) & (events['Date'] <= end_date)]
-    print(events.shape, events.columns)
 
+    # Use convert_str_to_list utils function to restore list value data type
     cols = ['Raw_Headline', 'Bold_KW', 'Feature', 'Raw_News', 'Sources', 'Urls']
     for col in cols:
         events[col] = events[col].apply(convert_str_to_list)
-
+    
+    # Get unique features
+    events['SetFeature'] = events['Feature'].apply(lambda x: str(set(x)))
+    
     # Add a new column for positive values of column A
     events['Positive_Impacts'] = events[events['Events_Impact'] >= 4.7]['Events_Impact']
 
@@ -368,24 +377,17 @@ with news_analysis:
     # Fill NaN values in the new columns with 0
     events['Positive_Impacts'].fillna("", inplace=True)
     events['Negative_Impacts'].fillna("", inplace=True)
+
+    # Filter out subset dataframes to plot positive & negative impacts
     plot_sub_pos = events[events['Positive_Impacts']!='']
     plot_sub_neg = events[events['Negative_Impacts']!='']
-    def get_set(list_value):
-        set_value = set(list_value)
-        return str(set_value)
-
-    plot_sub_pos['SetFeature'] = plot_sub_pos['Feature'].apply(get_set)
-    plot_sub_neg['SetFeature'] = plot_sub_neg['Feature'].apply(get_set)
+    
     # Create the line trace for stock prices
     line_stock = go.Scatter(x=events['Date'], y=events['Price'], mode='lines', name='OGDCL Close Price',
         line=dict(dash='solid', color=cs.close_line_color, width=2),
-        # text=events['Cleaned_Headline'],
-        # hovertext=events['FeatureSentiment'],
-        customdata=events['Feature'],
+        customdata=events['SetFeature'],
         hovertemplate='%{x}<br>Close: %{y}<br>Feature: %{customdata}<br>',
-        # hoverlabel=dict(font=dict(color=events
-        # ['FeatureSentiment'].apply(lambda x: 'red' if x == 'Negative' else 'blue' if x == 'Neutral' else 'green'))),  # Customize the line style, color, and width
-    )
+        )
     title = 'OGDCL Close Price vs News Impact'
     layout = go.Layout(
         title=title,
@@ -405,14 +407,17 @@ with news_analysis:
     )
 
     # Add all traces to the figure
-
     figure = go.Figure(data=[line_stock], layout=layout)
+    # Add Positive impacts
     figure.add_scatter(x=plot_sub_pos['Date'], y=plot_sub_pos['Price'], mode='markers', 
         marker=dict(symbol='triangle-up', size=10, color=cs.pos_impacts_color), name='Positive Impact', 
         customdata=plot_sub_pos['SetFeature'], hovertemplate='%{x}<br>Close: %{y}<br>Feature: %{customdata}<br>')  
+    # Add Negative impacts
     figure.add_scatter(x=plot_sub_neg['Date'], y=plot_sub_neg['Price'], mode='markers', 
         marker=dict(symbol='triangle-down', size=10, color=cs.neg_impacts_color), name='Negative Impact', 
         customdata=plot_sub_neg['SetFeature'], hovertemplate='%{x}<br>Close: %{y}<br>Feature: %{customdata}<br>',)
+    
+    # Update Layout
     figure.update_layout(
         title={
             'text': title,
@@ -425,8 +430,8 @@ with news_analysis:
         hovermode='closest',
         margin=dict(l=40, r=40, t=80, b=40),
         modebar_add="togglespikelines",
-        # scrollZoom=True,
     )
+    # Add date range selection buttons to chart
     figure.update_xaxes(
         rangeslider_visible=True,
         rangeselector=dict(
@@ -471,7 +476,7 @@ with news_analysis:
         st.write(f'<span style="font-size: large;"><b>Date:</b> <u>{date}</u></span>', unsafe_allow_html=True)
         filtered_news = news[news['Date'] == date]
         print(filtered_news.shape)
-        features = filtered_news['Feature'].sum()
+        features = filtered_news['SetFeature'].sum()
         headlines = filtered_news['Raw_Headline'].sum()
         news_list = filtered_news['Raw_News'].sum() 
         sources = filtered_news['Sources'].sum()
@@ -526,8 +531,7 @@ with final_recs:
         fp[d] = following_price
 
     fp[rec_dates[-1]] = 'Not Available'
-    # date_value =  pd.to_datetime('2021-08-12')
-    # pred_date = st.date_input("Pick Test Date", value=date_value, min_value=date_value, max_value=date_limit)
+    
     role = st.radio(
     "Show recommendation summary as:",
     ["Active Trader", "Equity Analyst"], horizontal=True)
@@ -543,20 +547,7 @@ with final_recs:
         # Convert back to Dictionaries from strings
         trade_recs['Recommendations_Equity_Analyst'] = trade_recs['Recommendations_Equity_Analyst'].apply(convert_str_to_list)
         trade_recs.rename(columns={'Recommendations_Equity_Analyst': 'Recommendations'}, inplace=True)
-    # if 'Trading_Recommendations\r' in events.columns:
-    #     events.rename(columns={'Trading_Recommendations\r': 'Trading_Recommendations'}, inplace=True)
     
-    # print(events.columns)
-    
-    # # Extract date component from the datetime column
-    # trade_recs['Date'] = trade_recs['Date'].dt.date
-    
-    # dates = list(trade_recs['Date'].unique())
-    # dates = np.sort(dates)
-    # # Reverse the array to have the latest date at index 0
-    # dates = dates[::-1]
-    # num_dates = len(dates)
-    # st.subheader("GenAI Recommendations")
     trade_recs.reset_index(inplace=True, drop=True)
     genrec_container = st.container(border=False)
     rec_col1, rec_col2, rec_col3 = genrec_container.columns(3, gap='medium')
@@ -565,9 +556,8 @@ with final_recs:
     rec_col1.write(f'<span style="font-size: large;"><b>Current Date:</b> <u>{pred_date}</u></span>', unsafe_allow_html=True)
     rec_col2.write(f'<span style="font-size: large;"><b>Current Close Price:</b> {current_price}</span>', unsafe_allow_html=True)
     rec_col3.write(f'<span style="font-size: large;"><b>Following Close Price:</b> {fp[pred_date]}</span>', unsafe_allow_html=True)
+    
     genrec_container.subheader("Generated Recommendation")
-    # genrec_container.divider()
-    # genrec = 'Recommendation for following day will go here.'
     genrec_container.write(trade_recs['Recommendations'][0])
     
     # Create the line trace for stock prices