last 5 results

app.py

@@ -291,18 +291,20 @@ def display_leaderboard():
     if st.button("Show Leaderboard"):
         try:
             # # Load the 'leaders' configuration
-            # dataset = load_dataset("Jay-Rajput/DIS_IPL_Leads", split='train')
+            dataset = load_dataset("Jay-Rajput/DIS_IPL_Leads", split='train')
 
             users_data = []
-            # if dataset:
-            #     for user, points_dict in dataset[0].items():
-            #         points = points_dict.get("points", 0)
-            #         users_data.append({'User': user, 'Points': points})
-            # else:
-            data = load_users(USERS_JSON)
-            for user, points_dict in data.items():
-                points = points_dict.get("points", 0)
-                users_data.append({'User': user, 'Points': points})
+            if dataset:
+                for user, points_dict in dataset[0].items():
+                    points = points_dict.get("points", 0)
+                    last_5_results = " ".join(points_dict.get("last_5_results", ["⚪"] * 5))  # Default: 5 white circles
+                    users_data.append({'User': user, 'Points': points, "Last 5 Bids": last_5_results})
+            else:
+                data = load_users(USERS_JSON)
+                for user, points_dict in data.items():
+                    points = points_dict.get("points", 0)
+                    last_5_results = " ".join(points_dict.get("last_5_results", ["⚪"] * 5))  # Default: 5 white circles
+                    users_data.append({'User': user, 'Points': points, "Last 5 Bids": last_5_results})
 
             leaderboard = pd.DataFrame(users_data)
             
@@ -313,7 +315,7 @@ def display_leaderboard():
             leaderboard['Rank'] = range(1, len(leaderboard) + 1)
             
             # Select and order the columns for display
-            leaderboard = leaderboard[['Rank', 'User', 'Points']]
+            leaderboard = leaderboard[['Rank', 'User', 'Points', 'Last 5 Bids']]
             
             st.dataframe(leaderboard, hide_index=True)
         except Exception as e:
@@ -438,19 +440,28 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, ou
                 # 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 1000 points for not submitting a prediction
                 user_points -= 1000
+                result_indicator = "⚪"  # No Prediction
                 
-            if user_points < 0:
-                user_points = 0
+            # 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
+            
+            # 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
 
         users.to_json(USERS_JSON)
         updated_dataset = Dataset.from_pandas(users_df)

leaders/users.json

@@ -1 +1 @@
-{"Arpit": {"points": 10000, "wildcard": [0, 0, 0]}, "Ganesh": {"points": 10000, "wildcard": [0, 0, 0]}, "Haaris": {"points": 10000, "wildcard": [0, 0, 0]}, "Jay": {"points": 10000, "wildcard": [0, 0, 0]}, "Kishore": {"points": 10000, "wildcard": [0, 0, 0]}, "Megha": {"points": 10000, "wildcard": [0, 0, 0]}, "Naveein": {"points": 10000, "wildcard": [0, 0, 0]}, "Neha": {"points": 10000, "wildcard": [0, 0, 0]}, "Praveen": {"points": 10000, "wildcard": [0, 0, 0]}, "Rakesh": {"points": 10000, "wildcard": [0, 0, 0]}, "Sai": {"points": 10000, "wildcard": [0, 0, 0]}, "Sahil": {"points": 10000, "wildcard": [0, 0, 0]}, "Sunil": {"points": 10000, "wildcard": [0, 0, 0]}, "Vaibhav": {"points": 10000, "wildcard": [0, 0, 0]}, "Vinay": {"points": 10000, "wildcard": [0, 0, 0]}}
+{"Arpit": {"points": 10000, "wildcard": [0, 0, 0],"last_5_results": ["⚪", "⚪", "⚪", "⚪", "⚪"]}, "Ganesh": {"points": 10000, "wildcard": [0, 0, 0],"last_5_results": ["⚪", "⚪", "⚪", "⚪", "⚪"]}, "Haaris": {"points": 10000, "wildcard": [0, 0, 0],"last_5_results": ["⚪", "⚪", "⚪", "⚪", "⚪"]}, "Jay": {"points": 10000, "wildcard": [0, 0, 0],"last_5_results": ["⚪", "⚪", "⚪", "⚪", "⚪"]}, "Kishore": {"points": 10000, "wildcard": [0, 0, 0],"last_5_results": ["⚪", "⚪", "⚪", "⚪", "⚪"]}, "Megha": {"points": 10000, "wildcard": [0, 0, 0],"last_5_results": ["⚪", "⚪", "⚪", "⚪", "⚪"]}, "Naveein": {"points": 10000, "wildcard": [0, 0, 0],"last_5_results": ["⚪", "⚪", "⚪", "⚪", "⚪"]}, "Neha": {"points": 10000, "wildcard": [0, 0, 0],"last_5_results": ["⚪", "⚪", "⚪", "⚪", "⚪"]}, "Praveen": {"points": 10000, "wildcard": [0, 0, 0],"last_5_results": ["⚪", "⚪", "⚪", "⚪", "⚪"]}, "Rakesh": {"points": 10000, "wildcard": [0, 0, 0],"last_5_results": ["⚪", "⚪", "⚪", "⚪", "⚪"]}, "Sai": {"points": 10000, "wildcard": [0, 0, 0],"last_5_results": ["⚪", "⚪", "⚪", "⚪", "⚪"]}, "Sahil": {"points": 10000, "wildcard": [0, 0, 0],"last_5_results": ["⚪", "⚪", "⚪", "⚪", "⚪"]}, "Sunil": {"points": 10000, "wildcard": [0, 0, 0],"last_5_results": ["⚪", "⚪", "⚪", "⚪", "⚪"]}, "Vaibhav": {"points": 10000, "wildcard": [0, 0, 0],"last_5_results": ["⚪", "⚪", "⚪", "⚪", "⚪"]}, "Vinay": {"points": 10000, "wildcard": [0, 0, 0],"last_5_results": ["⚪", "⚪", "⚪", "⚪", "⚪"]}}