Enhance team analysis functionality with sidebar options for team selection and prediction settings. Updated init_team_data function to incorporate win/loss predictions and display projected kills and deaths based on user input.

app.py

@@ -20,17 +20,55 @@ def init_conn():
     
 db, team_names, player_names = init_conn()
 
+# Create sidebar container for options
+with st.sidebar:
+    st.header("Team Analysis Options")
+    selected_team = st.selectbox(
+        "Select Team",
+        options=team_names,
+        index=team_names.index("T1") if "T1" in team_names else 0
+    )
+    
+    st.subheader("Prediction Settings")
+    win_loss = st.selectbox(
+        "Select Win/Loss",
+        options=["Win", "Loss"],
+        index=0
+    )
+
+    kill_prediction = st.number_input(
+        "Predicted Team Kills",
+        min_value=0,
+        max_value=100,
+        value=15
+    )
+    
+    death_prediction = st.number_input(
+        "Predicted Team Deaths",
+        min_value=0,
+        max_value=100,
+        value=10
+    )
+
 @st.cache_data(ttl = 60)
-def init_team_data(team):  
+def init_team_data(team, win_loss, kill_prediction, death_prediction):  
         
         collection = db["gamelogs"] 
         cursor = collection.find({"teamname": team})
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['playername', 'teamname', 'playername_avg_kill_share_win', 'playername_avg_death_share_win', 'playername_avg_assist_share_win', 'playername_avg_cs_share_win', 'playername_avg_kill_share_loss', 'playername_avg_death_share_loss', 'playername_avg_assist_share_loss', 'playername_avg_cs_share_loss']]
-
+        raw_display = raw_display.rename(columns = {'playername_avg_kill_share_win': 'wKill%', 'playername_avg_death_share_win': 'wDeath%', 'playername_avg_assist_share_win': 'wAssist%', 'playername_avg_cs_share_win': 'wCS%', 'playername_avg_kill_share_loss': 'lKill%', 'playername_avg_death_share_loss': 'lDeath%', 'playername_avg_assist_share_loss': 'lAssist%', 'playername_avg_cs_share_loss': 'lCS%'})
         team_data = raw_display.drop_duplicates(subset = ['playername'])
 
+        if win_loss == "Win":
+            team_data['Kill_Proj'] = team_data['wKill%'] * kill_prediction
+            team_data['Death_Proj'] = team_data['wDeath%'] * death_prediction
+        else:
+            team_data['Kill_Proj'] = team_data['lKill%'] * kill_prediction
+            team_data['Death_Proj'] = team_data['lDeath%'] * death_prediction
+
         return team_data
 
-st.table(init_team_data("T1"))
+if st.button("Run"):
+    st.dataframe(init_team_data(selected_team, win_loss, kill_prediction, death_prediction))