Adding wildcard

app.py

@@ -141,7 +141,8 @@ def submit_prediction(
         predicted_motm,
         bid_points,
         min_bid_points,
-        max_bid_points
+        max_bid_points,
+        wildcard_used
         ):
 
     # Validation for user selection
@@ -176,6 +177,7 @@ def submit_prediction(
         'predicted_winner': predicted_winner,
         'predicted_motm': predicted_motm,
         'bid_points': bid_points,
+        'wildcard_used': wildcard_used if wildcard_used != "None" else None,
         'prediction_date': prediction_time  # Include the prediction time
     }
     
@@ -251,6 +253,18 @@ def user_selection_and_prediction():
     if user_name != "Select a user...":
         min_bid_points, max_bid_points = calculate_min_max_bid_points(user_name)
         st.write(f"Bid points range you can submit: {min_bid_points} to {max_bid_points}")
+        
+    # Load user wildcard status
+    user_wildcards = users.get(user_name, {}).get('wildcard', [0, 0, 0])
+    available_wildcards = []
+
+    if user_wildcards[0] == 0:
+        available_wildcards.append("PowerMoM")
+    if user_wildcards[1] == 0:
+        available_wildcards.append("TripleE")
+    if user_wildcards[2] == 0:
+        available_wildcards.append("SwitchHit")
+    available_wildcards = ["None"] + available_wildcards
 
     matches = get_today_matches()
     if matches:
@@ -272,9 +286,11 @@ def user_selection_and_prediction():
             step=1,
             format="%d"
         )
+        
+        wildcard_used = st.selectbox("Select Wildcard (Optional)", available_wildcards)
 
         if st.button("Submit Prediction"):
-            submit_prediction(user_name, match_id, predicted_winner, predicted_motm, bid_points, min_bid_points, max_bid_points)
+            submit_prediction(user_name, match_id, predicted_winner, predicted_motm, bid_points, min_bid_points, max_bid_points, wildcard_used)
     else:
         st.write("No matches are scheduled for today.")
 
@@ -329,10 +345,20 @@ def display_leaderboard():
                     last_5_results = " ".join(points_dict.get("last_5_results", ["⚪"] * 5))  # Default: 5 white circles
                     bonus = points_dict.get("redistributed_bonus", 0)
                     bonus_display = f"+{bonus}" if bonus > 0 else ""
+                    wildcard_flags = points_dict.get("wildcard", [0, 0, 0])
+                    wildcard_display = []
+
+                    if wildcard_flags[0] == 1:
+                        wildcard_display.append("🟡PM")  # PowerMoM
+                    if wildcard_flags[1] == 1:
+                        wildcard_display.append("🔺3E")  # TripleE
+                    if wildcard_flags[2] == 1:
+                        wildcard_display.append("🔁SH")  # SwitchHit
                     users_data.append({
                         'User': user,
                         'Points': points,
                         'TOLBOG Wallet': bonus_display,
+                        'Wildcards Used': ", ".join(wildcard_display),
                         'Last 5 Bids': last_5_results
                     })
             else:
@@ -347,7 +373,7 @@ def display_leaderboard():
             leaderboard['Rank'] = range(1, len(leaderboard) + 1)
             
             # Select and order the columns for display
-            leaderboard = leaderboard[['Rank', 'User', 'Points', 'TOLBOG Wallet', 'Last 5 Bids']]
+            leaderboard = leaderboard[['Rank', 'User', 'Points', 'TOLBOG Wallet', 'Wildcards Used', 'Last 5 Bids']]
             
             st.dataframe(leaderboard, hide_index=True)
         except Exception as e:
@@ -482,23 +508,41 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, ou
             user_data = users_df[user_name][0]
             user_points = user_data['points']
             user_initial_points = user_points
+            user_wildcards = user_data.get('wildcard', [0, 0, 0])
 
             if user_name in submitted_users:
                 prediction = predictions[predictions['user_name'] == user_name].iloc[0]
                 predicted_winner = prediction['predicted_winner']
                 predicted_motm = prediction['predicted_motm']
                 bid_points = prediction['bid_points']
+                wildcard_used = prediction.get('wildcard_used')
+
+                earned_points = 0
 
                 if predicted_winner == winning_team:
-                    user_points += 2000 + bid_points
+                    earned_points += 2000 + bid_points
                     result_indicator = "🟢"
                     if predicted_motm == man_of_the_match:
-                        user_points += 500
+                        earned_points += 500
+                        if wildcard_used == "PowerMoM":
+                            earned_points += 1000  # MOM bonus tripled (500 -> 1500)
+                            # Extra performance logic placeholder (e.g., 1000 for century etc.)
+                    if wildcard_used == "TripleE":
+                        earned_points *= 3
                 else:
-                    user_points -= 200 + bid_points
+                    earned_points -= 200 + bid_points
                     result_indicator = "🔴"
                     if user_name in top3_usernames:
                         lost_points_by_top3 += (200 + bid_points)
+
+                if wildcard_used == "PowerMoM" and predicted_motm != man_of_the_match:
+                    user_wildcards[0] = 1  # Mark PowerMoM used anyway
+                elif wildcard_used == "TripleE":
+                    user_wildcards[1] = 1
+                elif wildcard_used == "SwitchHit":
+                    user_wildcards[2] = 1
+
+                user_points += earned_points
             else:
                 penalty = int(0.10 * user_points)
                 user_points -= penalty
@@ -513,6 +557,8 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, ou
                 "initial_points": user_initial_points
             }
 
+            users_df[user_name][0]['wildcard'] = user_wildcards
+
         # Step 2: Build new leaderboard after applying outcome
         new_leaderboard = [(u, d["updated_points"]) for u, d in user_outcomes.items()]
         new_leaderboard.sort(key=lambda x: x[1], reverse=True)