bonus

app.py

@@ -491,126 +491,11 @@ def redistribute_lost_points(match_id):
     updated_dataset.push_to_hub("Jay-Rajput/DIS_IPL_Leads", split="train")
 
 
-def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, outcome_only=False):
-    outcomes = load_dataset("Jay-Rajput/DIS_IPL_Outcomes", split="train")
-    outcomes_df = pd.DataFrame(outcomes)
-
-    # Update or add match outcome
-    outcome_exists = False
-    for idx, outcome in outcomes_df.iterrows():
-        if outcome['match_id'] == match_id:
-            outcomes_df.at[idx, 'winning_team'] = winning_team
-            outcomes_df.at[idx, 'man_of_the_match'] = man_of_the_match
-            outcome_exists = True
-            break
-    if not outcome_exists:
-        new_outcome = {"match_id": match_id, "winning_team": winning_team, "man_of_the_match": man_of_the_match}
-        outcomes_df = pd.concat([outcomes_df, pd.DataFrame([new_outcome])], ignore_index=True)
-        outcomes = Dataset.from_pandas(outcomes_df)
-
-    if not outcome_only:
-        predictions = fetch_latest_predictions(match_id)
-        users = load_dataset("Jay-Rajput/DIS_IPL_Leads", split="train")
-        users_df = pd.DataFrame(users)
-
-        # Capture previous leaderboard (top 5 users and their points)
-        prev_scores = [(user, users_df[user][0]['points']) for user in users_df.columns]
-        prev_scores.sort(key=lambda x: x[1], reverse=True)
-        prev_top_5 = prev_scores[:5]
-        top5_usernames = [user for user, _ in prev_top_5]
-
-        lost_points_by_top5 = 0
-        user_outcomes = {}
-
-        # Step 1: Apply current match outcomes
-        for user_name in users_df.columns:
-            user_data = users_df[user_name][0]
-            user_points = user_data['points']
-            user_initial_points = user_points
-
-            if user_name in set(predictions['user_name']):
-                prediction = predictions[predictions['user_name'] == user_name].iloc[0]
-                predicted_winner = prediction['predicted_winner']
-                predicted_motm = prediction['predicted_motm']
-                bid_points = prediction['bid_points']
-
-                if predicted_winner == winning_team:
-                    user_points += 2000 + bid_points
-                    result_indicator = "🟢"
-                    if predicted_motm == man_of_the_match:
-                        user_points += 500
-                else:
-                    user_points -= 200 + bid_points
-                    result_indicator = "🔴"
-                    if user_name in top5_usernames:
-                        lost_points_by_top5 += (200 + bid_points)
-            else:
-                user_points -= 200
-                result_indicator = "⚪"
-                if user_name in top5_usernames:
-                    lost_points_by_top5 += 200
-
-            user_points = max(user_points, 0)
-            user_outcomes[user_name] = {
-                "updated_points": user_points,
-                "result_indicator": result_indicator,
-                "initial_points": user_initial_points
-            }
-
-        # 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)
-        
-        # Step 3: Redistribute lost points with reverse leaderboard weighting
-        bonus_distribution = {}
-
-        remaining_users = [u for u in user_outcomes if u not in top5_usernames]
-        if remaining_users and lost_points_by_top5 > 0:
-            sorted_remaining = sorted(remaining_users, key=lambda u: user_outcomes[u]['updated_points'])
-            weights = {u: 1 / (i + 1) for i, u in enumerate(sorted_remaining)}
-            total_weight = sum(weights.values())
-
-            cumulative_bonus = 0
-            for i, user in enumerate(sorted_remaining):
-                if i == len(sorted_remaining) - 1:
-                    bonus = lost_points_by_top5 - cumulative_bonus
-                else:
-                    share_fraction = weights[user] / total_weight
-                    bonus = int(lost_points_by_top5 * share_fraction)
-                    cumulative_bonus += bonus
-                bonus_distribution[user] = bonus
-
-        # Step 4: Apply the appropriate update
-        for user in users_df.columns:
-            bonus = bonus_distribution.get(user, 0)
-            final_points = user_outcomes[user]["updated_points"] + bonus
-
-            users_df[user][0]['points'] = final_points
-            users_df[user][0]['redistributed_bonus'] = bonus
-
-            result = user_outcomes[user]["result_indicator"]
-            if "last_5_results" not in users_df[user][0]:
-                users_df[user][0]["last_5_results"] = []
-            users_df[user][0]["last_5_results"].insert(0, result)
-            users_df[user][0]["last_5_results"] = users_df[user][0]["last_5_results"][:5]
-
-        # Save updated leaderboard
-        users.to_json(USERS_JSON)
-        updated_dataset = Dataset.from_pandas(users_df)
-        updated_dataset.push_to_hub("Jay-Rajput/DIS_IPL_Leads", split="train")
-
-    # Save match outcome
-    outcomes.to_json(OUTCOMES)
-    outcomes.push_to_hub("Jay-Rajput/DIS_IPL_Outcomes", split="train")
-
-
 # def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, outcome_only=False):
-
-#     # Load existing match outcomes
-#     outcomes = load_dataset("Jay-Rajput/DIS_IPL_Outcomes", split="train")  
+#     outcomes = load_dataset("Jay-Rajput/DIS_IPL_Outcomes", split="train")
 #     outcomes_df = pd.DataFrame(outcomes)
 
-#     # Directly update or add the match outcome
+#     # Update or add match outcome
 #     outcome_exists = False
 #     for idx, outcome in outcomes_df.iterrows():
 #         if outcome['match_id'] == match_id:
@@ -623,65 +508,179 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, ou
 #         outcomes_df = pd.concat([outcomes_df, pd.DataFrame([new_outcome])], ignore_index=True)
 #         outcomes = Dataset.from_pandas(outcomes_df)
 
-#     if not outcome_only:  # Update user scores only if outcome_only is False
-#         # Load predictions only if necessary
+#     if not outcome_only:
 #         predictions = fetch_latest_predictions(match_id)
-
-#         # Load users' data only if necessary
 #         users = load_dataset("Jay-Rajput/DIS_IPL_Leads", split="train")
 #         users_df = pd.DataFrame(users)
 
-#         # Update user points based on prediction accuracy
-#         users_with_predictions = set(predictions['user_name'])
+#         # Capture previous leaderboard (top 5 users and their points)
+#         prev_scores = [(user, users_df[user][0]['points']) for user in users_df.columns]
+#         prev_scores.sort(key=lambda x: x[1], reverse=True)
+#         prev_top_5 = prev_scores[:5]
+#         top5_usernames = [user for user, _ in prev_top_5]
+
+#         lost_points_by_top5 = 0
+#         user_outcomes = {}
+
+#         # Step 1: Apply current match outcomes
 #         for user_name in users_df.columns:
-#             user_points = users_df[user_name][0]['points']
-#             if user_name in users_with_predictions:
+#             user_data = users_df[user_name][0]
+#             user_points = user_data['points']
+#             user_initial_points = user_points
+
+#             if user_name in set(predictions['user_name']):
 #                 prediction = predictions[predictions['user_name'] == user_name].iloc[0]
 #                 predicted_winner = prediction['predicted_winner']
 #                 predicted_motm = prediction['predicted_motm']
 #                 bid_points = prediction['bid_points']
 
-#                 # Update points based on prediction accuracy
 #                 if predicted_winner == winning_team:
 #                     user_points += 2000 + bid_points
-#                     result_indicator = "🟢"  # Correct Prediction
+#                     result_indicator = "🟢"
 #                     if predicted_motm == man_of_the_match:
 #                         user_points += 500
 #                 else:
 #                     user_points -= 200 + bid_points
-#                     result_indicator = "🔴"  # Wrong Prediction
+#                     result_indicator = "🔴"
+#                     if user_name in top5_usernames:
+#                         lost_points_by_top5 += (200 + bid_points)
 #             else:
-#                 # Deduct 200 points for not submitting a prediction
 #                 user_points -= 200
-#                 result_indicator = "⚪"  # No Prediction
-                
-#             # Ensure user_points is never negative
-#             user_points = max(user_points, 0)
-
-#             # Update user's points in the DataFrame
-#             users_df[user_name][0]['points'] = user_points
-#             users[user_name][0]['points'] = user_points
-            
-#             # Maintain last 5 prediction results
-#             if "last_5_results" not in users_df[user_name][0]:
-#                 users_df[user_name][0]["last_5_results"] = []
-#             users_df[user_name][0]["last_5_results"].insert(0, result_indicator)  # Insert at beginning
-#             users_df[user_name][0]["last_5_results"] = users_df[user_name][0]["last_5_results"][:5]  # Keep only last 5
-            
-#             if "last_5_results" not in users[user_name][0]:
-#                 users[user_name][0]["last_5_results"] = []
-#             users[user_name][0]["last_5_results"].insert(0, result_indicator)  # Insert at beginning
-#             users[user_name][0]["last_5_results"] = users[user_name][0]["last_5_results"][:5]  # Keep only last 5
+#                 result_indicator = "⚪"
+#                 if user_name in top5_usernames:
+#                     lost_points_by_top5 += 200
 
+#             user_points = max(user_points, 0)
+#             user_outcomes[user_name] = {
+#                 "updated_points": user_points,
+#                 "result_indicator": result_indicator,
+#                 "initial_points": user_initial_points
+#             }
+
+#         # 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)
+        
+#         # Step 3: Redistribute lost points with reverse leaderboard weighting
+#         bonus_distribution = {}
+
+#         remaining_users = [u for u in user_outcomes if u not in top5_usernames]
+#         if remaining_users and lost_points_by_top5 > 0:
+#             sorted_remaining = sorted(remaining_users, key=lambda u: user_outcomes[u]['updated_points'])
+#             weights = {u: 1 / (i + 1) for i, u in enumerate(sorted_remaining)}
+#             total_weight = sum(weights.values())
+
+#             cumulative_bonus = 0
+#             for i, user in enumerate(sorted_remaining):
+#                 if i == len(sorted_remaining) - 1:
+#                     bonus = lost_points_by_top5 - cumulative_bonus
+#                 else:
+#                     share_fraction = weights[user] / total_weight
+#                     bonus = int(lost_points_by_top5 * share_fraction)
+#                     cumulative_bonus += bonus
+#                 bonus_distribution[user] = bonus
+
+#         # Step 4: Apply the appropriate update
+#         for user in users_df.columns:
+#             bonus = bonus_distribution.get(user, 0)
+#             final_points = user_outcomes[user]["updated_points"] + bonus
+
+#             users_df[user][0]['points'] = final_points
+#             users_df[user][0]['redistributed_bonus'] = bonus
+
+#             result = user_outcomes[user]["result_indicator"]
+#             if "last_5_results" not in users_df[user][0]:
+#                 users_df[user][0]["last_5_results"] = []
+#             users_df[user][0]["last_5_results"].insert(0, result)
+#             users_df[user][0]["last_5_results"] = users_df[user][0]["last_5_results"][:5]
+
+#         # Save updated leaderboard
 #         users.to_json(USERS_JSON)
 #         updated_dataset = Dataset.from_pandas(users_df)
 #         updated_dataset.push_to_hub("Jay-Rajput/DIS_IPL_Leads", split="train")
-#         # redistribute_lost_points(match_id)
 
+#     # Save match outcome
 #     outcomes.to_json(OUTCOMES)
 #     outcomes.push_to_hub("Jay-Rajput/DIS_IPL_Outcomes", split="train")
 
 
+def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, outcome_only=False):
+
+    # Load existing match outcomes
+    outcomes = load_dataset("Jay-Rajput/DIS_IPL_Outcomes", split="train")  
+    outcomes_df = pd.DataFrame(outcomes)
+
+    # Directly update or add the match outcome
+    outcome_exists = False
+    for idx, outcome in outcomes_df.iterrows():
+        if outcome['match_id'] == match_id:
+            outcomes_df.at[idx, 'winning_team'] = winning_team
+            outcomes_df.at[idx, 'man_of_the_match'] = man_of_the_match
+            outcome_exists = True
+            break
+    if not outcome_exists:
+        new_outcome = {"match_id": match_id, "winning_team": winning_team, "man_of_the_match": man_of_the_match}
+        outcomes_df = pd.concat([outcomes_df, pd.DataFrame([new_outcome])], ignore_index=True)
+        outcomes = Dataset.from_pandas(outcomes_df)
+
+    if not outcome_only:  # Update user scores only if outcome_only is False
+        # Load predictions only if necessary
+        predictions = fetch_latest_predictions(match_id)
+
+        # Load users' data only if necessary
+        users = load_dataset("Jay-Rajput/DIS_IPL_Leads", split="train")
+        users_df = pd.DataFrame(users)
+
+        # Update user points based on prediction accuracy
+        users_with_predictions = set(predictions['user_name'])
+        for user_name in users_df.columns:
+            user_points = users_df[user_name][0]['points']
+            if user_name in users_with_predictions:
+                prediction = predictions[predictions['user_name'] == user_name].iloc[0]
+                predicted_winner = prediction['predicted_winner']
+                predicted_motm = prediction['predicted_motm']
+                bid_points = prediction['bid_points']
+
+                # Update points based on prediction accuracy
+                if predicted_winner == winning_team:
+                    user_points += 2000 + bid_points
+                    result_indicator = "🟢"  # Correct Prediction
+                    if predicted_motm == man_of_the_match:
+                        user_points += 500
+                else:
+                    user_points -= 200 + bid_points
+                    result_indicator = "🔴"  # Wrong Prediction
+            else:
+                # Deduct 200 points for not submitting a prediction
+                user_points -= 200
+                result_indicator = "⚪"  # No Prediction
+                
+            # Ensure user_points is never negative
+            user_points = max(user_points, 0)
+
+            # Update user's points in the DataFrame
+            users_df[user_name][0]['points'] = user_points
+            users[user_name][0]['points'] = user_points
+            
+            # Maintain last 5 prediction results
+            if "last_5_results" not in users_df[user_name][0]:
+                users_df[user_name][0]["last_5_results"] = []
+            users_df[user_name][0]["last_5_results"].insert(0, result_indicator)  # Insert at beginning
+            users_df[user_name][0]["last_5_results"] = users_df[user_name][0]["last_5_results"][:5]  # Keep only last 5
+            
+            if "last_5_results" not in users[user_name][0]:
+                users[user_name][0]["last_5_results"] = []
+            users[user_name][0]["last_5_results"].insert(0, result_indicator)  # Insert at beginning
+            users[user_name][0]["last_5_results"] = users[user_name][0]["last_5_results"][:5]  # Keep only last 5
+
+        users.to_json(USERS_JSON)
+        updated_dataset = Dataset.from_pandas(users_df)
+        updated_dataset.push_to_hub("Jay-Rajput/DIS_IPL_Leads", split="train")
+
+    outcomes.to_json(OUTCOMES)
+    outcomes.push_to_hub("Jay-Rajput/DIS_IPL_Outcomes", split="train")
+
+
 # Function to fetch matches for a given date
 def fetch_matches_by_date(matches, selected_date):
     return [match for match in matches if datetime.strptime(match['date'], '%Y-%m-%d').date() == selected_date]