enhance

app.py

@@ -211,10 +211,11 @@ def get_user_total_points(user_name):
     return users.get(user_name, {}).get('points')
 
 
-def calculate_max_bid_points(user_name):
+def calculate_min_max_bid_points(user_name):
     total_points = get_user_total_points(user_name)
-    max_bid_points = int(total_points * 0.20)  # 20% of total points
-    return max_bid_points
+    min_bid_points = int(total_points * 0.10)  # 10% of total points
+    max_bid_points = int(total_points * 0.50)  # 50% of total points
+    return min_bid_points, max_bid_points
 
 
 def load_users(USERS_JSON):
@@ -236,10 +237,10 @@ def user_selection_and_prediction():
     users = list(load_data(USERS_JSON))
     user_name = st.selectbox("Select User", ["Select a user..."] + users)
 
-    max_bid_points = None
+    min_bid_points, max_bid_points = None, None
     if user_name != "Select a user...":
-        max_bid_points = calculate_max_bid_points(user_name)
-        st.write(f"Maximum bid points you can submit: {max_bid_points}")
+        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}")
 
     matches = get_today_matches()
     if matches:
@@ -255,7 +256,7 @@ def user_selection_and_prediction():
             players = player_list[predicted_winner]
             predicted_motm = st.selectbox("Predicted Man of the Match", players)
 
-        bid_points = st.number_input("Bid Points", min_value=0, value=100, format="%d")
+        bid_points = st.number_input("Bid Points", min_value=min_bid_points, value=100, format="%d")
 
         if st.button("Submit Prediction"):
             submit_prediction(user_name, match_id, predicted_winner, predicted_motm, bid_points, max_bid_points)
@@ -376,10 +377,10 @@ user_guide_content = """
   - ✅ **Correct Prediction**: You earn **an additional 500 points**.  
   - ❌ **Incorrect Prediction**: No penalty.  
 - **No Prediction Submitted**:  
-  - ❌ **You lose 1000 points** automatically for not submitting a prediction. 
+  - ❌ **You lose 10% of your total points** automatically for not submitting a prediction. 
 
 #### Bid Point Constraints
-- You cannot bid more than 20% of your current total points.
+- You cannot bid less then 10% and more than 50% of your current total points.
 - Bid points will be doubled if your prediction is correct, and deducted if incorrect.
 
 #### Rules for Submission
@@ -428,69 +429,6 @@ def fetch_latest_predictions(match_id):
     else:
         return pd.DataFrame()
 
-
-def redistribute_lost_points(match_id):
-    predictions = fetch_latest_predictions(match_id)
-    users = load_dataset("Jay-Rajput/DIS_IPL_Leads", split="train")
-    users_df = pd.DataFrame(users)
-
-    # Build current leaderboard (after score updates)
-    leaderboard = []
-    for user_name in users_df.columns:
-        points = users_df[user_name][0]['points']
-        leaderboard.append((user_name, points))
-
-    leaderboard.sort(key=lambda x: x[1], reverse=True)
-
-    top_5 = leaderboard[:5]
-    others = leaderboard[5:]
-
-    # Fetch match outcome
-    outcomes_df = load_dataset("Jay-Rajput/DIS_IPL_Outcomes", split="train").to_pandas()
-    match_row = outcomes_df[outcomes_df['match_id'] == match_id].iloc[0]
-    winning_team = match_row['winning_team']
-
-    # Calculate lost points from top 5 users who predicted incorrectly
-    total_lost_points = 0
-    lost_points_per_user = {}
-
-    for user_name, _ in top_5:
-        if user_name in predictions['user_name'].values:
-            pred = predictions[predictions['user_name'] == user_name].iloc[0]
-            if pred['predicted_winner'] != winning_team:
-                lost_points = 200 + pred['bid_points']
-                total_lost_points += lost_points
-                lost_points_per_user[user_name] = lost_points
-
-    if total_lost_points == 0 or not others:
-        return  # Nothing to redistribute
-
-    # Total points of eligible users (position 6 to last)
-    total_eligible_points = sum([points for (_, points) in others])
-    if total_eligible_points == 0:
-        return
-
-    # Distribute lost points proportionally
-    for user_name, user_points in others:
-        share_ratio = user_points / total_eligible_points
-        bonus = int(total_lost_points * share_ratio)
-
-        # Update bonus in leads
-        users_df[user_name][0]['points'] += bonus
-        users[user_name][0]['points'] = users_df[user_name][0]['points']
-
-        # Track redistributed bonus (initialize or accumulate)
-        prev_bonus_df = users_df[user_name][0].get("redistributed_bonus", 0)
-        prev_bonus_dict = users[user_name][0].get("redistributed_bonus", 0)
-        users_df[user_name][0]["redistributed_bonus"] = prev_bonus_df + bonus
-        users[user_name][0]["redistributed_bonus"] = prev_bonus_dict + bonus
-
-    # Push updated dataset
-    users.to_json(USERS_JSON)
-    updated_dataset = Dataset.from_pandas(users_df)
-    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)
@@ -545,10 +483,11 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, ou
                     if user_name in top3_usernames:
                         lost_points_by_top3 += (200 + bid_points)
             else:
-                user_points -= 200
+                penalty = int(0.10 * user_points)
+                user_points -= penalty
                 result_indicator = "⚪"
                 if user_name in top3_usernames:
-                    lost_points_by_top3 += 200
+                    lost_points_by_top3 += penalty
 
             user_points = max(user_points, 0)
             user_outcomes[user_name] = {

leaders/users.json

@@ -1,14 +1,14 @@
 {
   "Arpit": {
     "last_5_results": [
+      "🟢",
       "🟢",
       "🟢",
       "🔴",
-      "⚪",
-      "🔴"
+      "⚪"
     ],
-    "points": 21033,
-    "redistributed_bonus": 8,
+    "points": 23154,
+    "redistributed_bonus": 21,
     "wildcard": [
       0,
       0,
@@ -17,14 +17,14 @@
   },
   "Ganesh": {
     "last_5_results": [
+      "⚪",
       "⚪",
       "🟢",
       "🔴",
-      "🟢",
-      "⚪"
+      "🟢"
     ],
-    "points": 5015,
-    "redistributed_bonus": 15,
+    "points": 4856,
+    "redistributed_bonus": 41,
     "wildcard": [
       0,
       0,
@@ -33,14 +33,14 @@
   },
   "Haaris": {
     "last_5_results": [
+      "🔴",
       "🟢",
       "🔴",
       "🔴",
-      "⚪",
-      "🔴"
+      "⚪"
     ],
-    "points": 2616,
-    "redistributed_bonus": 16,
+    "points": 2160,
+    "redistributed_bonus": 44,
     "wildcard": [
       0,
       0,
@@ -51,12 +51,12 @@
     "last_5_results": [
       "🟢",
       "🟢",
-      "🔴",
       "🟢",
-      "🔴"
+      "🔴",
+      "🟢"
     ],
-    "points": 30248,
-    "redistributed_bonus": 5,
+    "points": 33258,
+    "redistributed_bonus": 10,
     "wildcard": [
       0,
       0,
@@ -65,14 +65,14 @@
   },
   "Kishore": {
     "last_5_results": [
+      "⚪",
       "🔴",
       "🟢",
       "⚪",
-      "🔴",
       "🔴"
     ],
-    "points": 38001,
-    "redistributed_bonus": 1,
+    "points": 37806,
+    "redistributed_bonus": 5,
     "wildcard": [
       0,
       0,
@@ -82,13 +82,13 @@
   "Megha": {
     "last_5_results": [
       "🔴",
-      "🟢",
       "🔴",
+      "🟢",
       "🔴",
-      "⚪"
+      "🔴"
     ],
-    "points": 21608,
-    "redistributed_bonus": 8,
+    "points": 20931,
+    "redistributed_bonus": 23,
     "wildcard": [
       0,
       0,
@@ -97,14 +97,14 @@
   },
   "Naveein": {
     "last_5_results": [
+      "🔴",
       "⚪",
       "🟢",
       "⚪",
-      "🟢",
-      "🔴"
+      "🟢"
     ],
-    "points": 42600,
-    "redistributed_bonus": 0,
+    "points": 41601,
+    "redistributed_bonus": 1,
     "wildcard": [
       0,
       0,
@@ -119,8 +119,8 @@
       "⚪",
       "⚪"
     ],
-    "points": 17,
-    "redistributed_bonus": 17,
+    "points": 46,
+    "redistributed_bonus": 46,
     "wildcard": [
       0,
       0,
@@ -129,14 +129,14 @@
   },
   "Praveen": {
     "last_5_results": [
+      "⚪",
       "🟢",
       "🔴",
       "⚪",
-      "⚪",
       "⚪"
     ],
-    "points": 9861,
-    "redistributed_bonus": 13,
+    "points": 9697,
+    "redistributed_bonus": 36,
     "wildcard": [
       0,
       0,
@@ -146,13 +146,13 @@
   "Rakesh": {
     "last_5_results": [
       "🔴",
-      "🟢",
       "🔴",
+      "🟢",
       "🔴",
       "🔴"
     ],
-    "points": 23188,
-    "redistributed_bonus": 8,
+    "points": 22959,
+    "redistributed_bonus": 21,
     "wildcard": [
       0,
       0,
@@ -164,10 +164,10 @@
       "🟢",
       "🟢",
       "🟢",
-      "🔴",
-      "🟢"
+      "🟢",
+      "🔴"
     ],
-    "points": 47708,
+    "points": 50208,
     "redistributed_bonus": 0,
     "wildcard": [
       0,
@@ -177,13 +177,13 @@
   },
   "Sunil": {
     "last_5_results": [
+      "🔴",
       "🟢",
       "🟢",
       "🟢",
-      "🟢",
-      "⚪"
+      "🟢"
     ],
-    "points": 53133,
+    "points": 52810,
     "redistributed_bonus": 0,
     "wildcard": [
       0,
@@ -193,14 +193,14 @@
   },
   "Vaibhav": {
     "last_5_results": [
-      "⚪",
       "🟢",
       "⚪",
+      "🟢",
       "⚪",
-      "🔴"
+      "⚪"
     ],
-    "points": 29005,
-    "redistributed_bonus": 5,
+    "points": 31617,
+    "redistributed_bonus": 12,
     "wildcard": [
       0,
       0,
@@ -215,8 +215,8 @@
       "⚪",
       "⚪"
     ],
-    "points": 3796,
-    "redistributed_bonus": 16,
+    "points": 3638,
+    "redistributed_bonus": 42,
     "wildcard": [
       0,
       0,
@@ -225,14 +225,14 @@
   },
   "Anandh": {
     "last_5_results": [
-      "🔴",
       "🟢",
       "🔴",
+      "🟢",
       "🔴",
       "🔴"
     ],
-    "points": 5524,
-    "redistributed_bonus": 15,
+    "points": 8561,
+    "redistributed_bonus": 37,
     "wildcard": [
       0,
       0,
@@ -247,8 +247,8 @@
       "⚪",
       "⚪"
     ],
-    "points": 4415,
-    "redistributed_bonus": 15,
+    "points": 4257,
+    "redistributed_bonus": 42,
     "wildcard": [
       0,
       0,
@@ -257,14 +257,14 @@
   },
   "Biswabarenya": {
     "last_5_results": [
-      "⚪",
+      "🔴",
       "⚪",
       "⚪",
       "⚪",
       "⚪"
     ],
-    "points": 5365,
-    "redistributed_bonus": 15,
+    "points": 4606,
+    "redistributed_bonus": 41,
     "wildcard": [
       0,
       0,
@@ -273,13 +273,13 @@
   },
   "Naitik": {
     "last_5_results": [
+      "⚪",
       "🟢",
       "🔴",
       "🔴",
-      "🔴",
-      "🟢"
+      "🔴"
     ],
-    "points": 42800,
+    "points": 42600,
     "redistributed_bonus": 0,
     "wildcard": [
       0,
@@ -295,8 +295,8 @@
       "⚪",
       "⚪"
     ],
-    "points": 11172,
-    "redistributed_bonus": 12,
+    "points": 11006,
+    "redistributed_bonus": 34,
     "wildcard": [
       0,
       0,
@@ -311,8 +311,8 @@
       "⚪",
       "⚪"
     ],
-    "points": 17,
-    "redistributed_bonus": 17,
+    "points": 46,
+    "redistributed_bonus": 46,
     "wildcard": [
       0,
       0,
@@ -322,13 +322,13 @@
   "Priyavrat Mohan": {
     "last_5_results": [
       "🟢",
-      "🔴",
       "🟢",
       "🔴",
+      "🟢",
       "🔴"
     ],
-    "points": 34828,
-    "redistributed_bonus": 3,
+    "points": 37333,
+    "redistributed_bonus": 5,
     "wildcard": [
       0,
       0,
@@ -337,14 +337,14 @@
   },
   "Satish": {
     "last_5_results": [
+      "⚪",
       "🟢",
       "🔴",
       "🟢",
-      "🔴",
-      "⚪"
+      "🔴"
     ],
-    "points": 37666,
-    "redistributed_bonus": 2,
+    "points": 37471,
+    "redistributed_bonus": 5,
     "wildcard": [
       0,
       0,