Adding 20% cap & points calculations

README.md

@@ -1,5 +1,5 @@
 ---
-title: DIS IPL
+title: DIS IPL Predictions
 emoji: 🦀
 colorFrom: red
 colorTo: pink
@@ -12,7 +12,7 @@ license: apache-2.0
 
 # DIS IPL Match Predictions App
 
-Welcome to the IPL Match Predictions App! This app allows you to predict the outcomes of IPL matches and compete with your colleagues to win exciting prizes.
+Welcome to the DIS IPL Match Predictions App! This app allows you to predict the outcomes of IPL matches and compete with your colleagues to win exciting prizes.
 
 ## Getting Started
 
@@ -32,13 +32,13 @@ Welcome to the IPL Match Predictions App! This app allows you to predict the out
 
 2. Navigate to the cloned repository directory:
 
-   ```
+   ```bash
    cd ipl-match-predictions-app
    ```
 
 3. Install the required Python dependencies using Pip:
 
-   ```
+   ```bash
    pip install -r requirements.txt
    ```
 
@@ -46,7 +46,7 @@ Welcome to the IPL Match Predictions App! This app allows you to predict the out
 
 1. After installing the dependencies, you can run the Streamlit app using the following command:
 
-   ```
+   ```bash
    streamlit run app.py
    ```
 

app.py

@@ -1,5 +1,5 @@
 import json
-from datetime import datetime
+from datetime import datetime, timedelta
 
 import pandas as pd
 import pytz
@@ -64,8 +64,53 @@ def get_today_matches():
     return today_matches
 
 
+# Function to check if prediction submission is allowed
+def is_submission_allowed(match_id):
+    matches = load_matches()  # This loads matches correctly with IST times
+
+    for match in matches:
+        if match["match_id"] == match_id:
+            # Parse the match start time in IST
+            tz_IST = pytz.timezone('Asia/Kolkata')
+            match_datetime_str = f'{match["date"]} {match["time"]}'
+            # The match time string is like "2024-03-21 7:30 PM"
+            match_datetime = datetime.strptime(match_datetime_str, "%Y-%m-%d %I:%M %p")
+            match_datetime = tz_IST.localize(match_datetime)  # Set the timezone to IST
+
+            # Get the current time in IST
+            current_datetime = datetime.now(tz_IST)
+
+            if current_datetime > match_datetime + timedelta(minutes=30):
+                return False
+            else:
+                return True
+    return False  # If match_id not found, default to False
+
+
 # Submit prediction function
-def submit_prediction(user_name, match_id, predicted_winner, predicted_motm, bid_points):
+def submit_prediction(
+        user_name,
+        match_id,
+        predicted_winner,
+        predicted_motm,
+        bid_points,
+        max_bid_points
+        ):
+
+    # Validation for user selection
+    if user_name == "Select a user...":
+        st.warning("Please select a valid user.")
+        return
+
+    # Check if prediction submission is allowed for the match
+    if not is_submission_allowed(match_id):
+        st.error("Prediction submission time has passed. Predictions can't be submitted after match start.")
+        return
+
+    if bid_points > max_bid_points:
+        st.error(f"Your bid points exceed the 20% limit of your total points. Maximum allowed bid points: {max_bid_points}")
+        return
+
     # Ensure predictions DataFrame is loaded or initialized correctly
     try:
         predictions = pd.read_csv(predictions_csv)
@@ -77,10 +122,7 @@ def submit_prediction(user_name, match_id, predicted_winner, predicted_motm, bid
         predictions = pd.DataFrame(columns=expected_columns)
 
     # Check for duplicate prediction for the same match by the same user
-    if user_name == "Select a user...":
-        st.warning("Please select a valid user.")
-        return
-    else:
+    if user_name != "Select a user...":
         existing_predictions = predictions[(predictions['user_name'] == user_name) & (predictions['match_id'] == match_id)]
         if not existing_predictions.empty:
             st.error("You've already submitted a prediction for this match.")
@@ -100,6 +142,19 @@ def submit_prediction(user_name, match_id, predicted_winner, predicted_motm, bid
     st.success("Prediction submitted successfully!")
 
 
+def get_user_total_points(user_name):
+    with open(users_json, 'r') as file:
+        users = json.load(file)
+    return users.get(user_name, 0)
+
+
+# Define the new function
+def calculate_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
+
+
 # Streamlit UI
 st.title("DIS IPL Match Predictions")
 
@@ -109,6 +164,14 @@ with st.expander("Submit Prediction"):
     # User selection
     user_name = st.selectbox("Select User", ["Select a user..."] + get_users())
 
+    # Initialize max_bid_points to None
+    max_bid_points = None
+
+    if user_name != "Select a user...":
+        max_bid_points = calculate_max_bid_points(user_name)
+        # Display the max bid points
+        st.write(f"Maximum bid points you can submit: {max_bid_points}")
+
     # Match selection
     matches = get_today_matches()
     if matches:
@@ -122,11 +185,12 @@ with st.expander("Submit Prediction"):
     # Predictions
     predicted_winner = st.selectbox("Predicted Winner", teams)
     predicted_motm = st.text_input("Predicted Man of the Match")
-    bid_points = st.number_input("Bid Points", min_value=1, value=100)
+    bid_points = st.number_input("Bid Points", min_value=1, value=100, format="%d")
 
     # Submit button
     if st.button("Submit Prediction"):
-        submit_prediction(user_name, match_id, predicted_winner, predicted_motm, bid_points)
+        if user_name != "Select a user...":
+            submit_prediction(user_name, match_id, predicted_winner, predicted_motm, bid_points, max_bid_points)
 
 
 # Show predictions
@@ -136,7 +200,7 @@ with st.expander("Predictions"):
     if st.button("Show Predictions"):
         try:
             df = pd.read_csv(predictions_csv)
-            st.dataframe(df)
+            st.dataframe(df, hide_index=True)
         except FileNotFoundError:
             st.write("No predictions have been submitted yet.")
 
@@ -158,7 +222,10 @@ with st.expander("Leaderboard"):
             # Reorder DataFrame columns so 'Rank' is first
             df_leaderboard = df_leaderboard[['Rank', 'User', 'Points']]
 
-            st.dataframe(df_leaderboard)
+            # Reset index to remove the default index column
+            df_leaderboard.reset_index(drop=True, inplace=True)
+
+            st.dataframe(df_leaderboard, hide_index=True)
         except FileNotFoundError:
             st.write("Leaderboard data not available.")
 
@@ -192,40 +259,48 @@ def save_match_outcomes(outcomes):
 
 
 def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match):
-        outcomes = load_match_outcomes()  # Load existing match outcomes
-        predictions = load_predictions()  # Load existing predictions
-        users = load_users()  # Load existing user points
-
-        # Update match outcomes
-        match_outcome = next((outcome for outcome in outcomes if outcome['match_id'] == match_id), None)
-        if match_outcome:
-            match_outcome['winning_team'] = winning_team
-            match_outcome['man_of_the_match'] = man_of_the_match
+    outcomes = load_match_outcomes()  # Load existing match outcomes
+    predictions = load_predictions()  # Load existing predictions
+    users = load_users()  # Load existing user points
+
+    # Update match outcomes
+    match_outcome = next((outcome for outcome in outcomes if outcome['match_id'] == match_id), None)
+    if match_outcome:
+        match_outcome['winning_team'] = winning_team
+        match_outcome['man_of_the_match'] = man_of_the_match
+    else:
+        outcomes.append({
+            "match_id": match_id,
+            "winning_team": winning_team,
+            "man_of_the_match": man_of_the_match
+        })
+
+    # Update user points based on prediction accuracy
+    match_predictions = predictions[predictions['match_id'] == match_id]
+    for _, prediction in match_predictions.iterrows():
+        user_name = prediction['user_name']
+        # Initialize user points if not present
+        users[user_name] = users.get(user_name, 0)
+
+        # Points for correct or incorrect team prediction
+        if prediction['predicted_winner'] == winning_team:
+            users[user_name] += 1000  # Correct team prediction
+            # Check for double or nothing bid points
+            users[user_name] += prediction['bid_points'] * 2
         else:
-            outcomes.append({
-                "match_id": match_id,
-                "winning_team": winning_team,
-                "man_of_the_match": man_of_the_match
-            })
-
-        # Update user points based on prediction accuracy
-        match_predictions = predictions[predictions['match_id'] == match_id]
-        for _, prediction in match_predictions.iterrows():
-            user_name = prediction['user_name']
-            # Initialize user points if not present
-            users[user_name] = users.get(user_name, 1000)
+            users[user_name] -= 200  # Deduct points for incorrect team prediction
+            users[user_name] -= prediction['bid_points']  # Lose bid points for incorrect prediction
 
+        # Points for correct man of the match prediction
+        if prediction['predicted_motm'] == man_of_the_match:
+            users[user_name] += 200  # Correct man of the match prediction
+            # Bonus for getting both right
             if prediction['predicted_winner'] == winning_team:
-                users[user_name] += prediction['bid_points'] * 2  # Correct team prediction
-            else:
-                users[user_name] -= prediction['bid_points']  # Deduct points for incorrect prediction
-
-            if prediction['predicted_motm'] == man_of_the_match:
-                users[user_name] += 100  # Correct man of the match prediction
+                users[user_name] += 200
 
-        # Save updated outcomes and user points
-        save_match_outcomes(outcomes)
-        save_users(users)
+    # Save updated outcomes and user points
+    save_match_outcomes(outcomes)
+    save_users(users)
 
 
 with st.sidebar.expander("Admin Panel", expanded=False):

matches.json

@@ -1,13 +1,6 @@
 [
     {
-        "match_id": "20240321_1",
-        "date": "2024-03-21",
-        "time": "7:30 PM",
-        "teams": ["CSK", "RCB"],
-        "venue": "Chennai"
-    },
-    {
-        "match_id": "20240322_1",
+        "match_id": "20240322_2",
         "date": "2024-03-22",
         "time": "7:30 PM",
         "teams": ["CSK", "RCB"],

predictions.csv

@@ -1,2 +1 @@
-user_name,match_id,predicted_winner,predicted_motm,bid_points
-Jay,20240321_1,CSK,Dhoni,105
+user_name,match_id,predicted_winner,predicted_motm,bid_points

users.json

@@ -1,17 +1,18 @@
 {
-    "Archana": 1000,
-    "Arpit": 1000,
-    "Ganesh": 1000,
-    "Haaris": 1000,
-    "Harshit": 1000,
-    "Jay": 1000,
-    "Kichu": 1000,
-    "Megha": 1000,
-    "Naveein": 1000,
-    "Neha": 1000,
-    "Praveen": 1000,
-    "Rakesh": 1000,
-    "Sunil": 1000,
-    "Vaibhav": 1000,
-    "Vinay": 1000
+    "Archana": 10000,
+    "Arpit": 10000,
+    "Ganesh": 10000,
+    "Haaris": 10000,
+    "Jay": 10000,
+    "Kichu": 10000,
+    "Megha": 10000,
+    "Naveein": 10000,
+    "Neha": 10000,
+    "Praveen": 10000,
+    "Rakesh": 10000,
+    "Sai": 10000,
+    "Sahil": 10000,
+    "Sunil": 10000,
+    "Vaibhav": 10000,
+    "Vinay": 10000
 }