Enhanced script

app.py

@@ -6,11 +6,12 @@ import pandas as pd
 import pytz
 import streamlit as st
 
-# File paths
-predictions_csv = 'predictions.csv'
-users_json = 'users.json'
-matches_json = 'matches.json'
-outcomes_json = 'match_outcomes.json'
+# File paths as constants
+PREDICTIONS_CSV = 'predictions.csv'
+USERS_JSON = 'users.json'
+MATCHES_JSON = 'matches.json'
+OUTCOMES_JSON = 'match_outcomes.json'
+PLAYERS_JSON = 'players.json'
 image_path = 'ipl_image.png'
 
 
@@ -18,43 +19,34 @@ image_path = 'ipl_image.png'
 def initialize_files():
     # Initialize predictions CSV
     try:
-        pd.read_csv(predictions_csv)
+        pd.read_csv(PREDICTIONS_CSV)
     except FileNotFoundError:
         df = pd.DataFrame(columns=['user_name', 'match_id', 'predicted_winner', 'predicted_motm', 'bid_points'])
-        df.to_csv(predictions_csv, index=False)
-
-
-# Load users from JSON
-def get_users():
-    try:
-        with open(users_json, 'r') as file:
-            users = json.load(file)
-        return list(users.keys())
-    except FileNotFoundError:
-        return []
-
-
-# Load matches from JSON
-def load_matches():
+        df.to_csv(PREDICTIONS_CSV, index=False)
+
+
+@st.cache_data
+def load_data(file_path):
+    """
+    Load data from a JSON or CSV file.
+    
+    Args:
+    file_path (str): The path to the file to load.
+    
+    Returns:
+    pd.DataFrame or dict: The loaded data.
+    """
     try:
-        with open(matches_json, 'r') as f:
-            return json.load(f)
+        if file_path.endswith('.json'):
+            with open(file_path, 'r') as file:
+                return json.load(file)
+        elif file_path.endswith('.csv'):
+            return pd.read_csv(file_path)
     except FileNotFoundError:
-        return []
-
-
-def load_match_outcomes():
-    try:
-        with open(outcomes_json, 'r') as file:
-            return json.load(file)
-    except FileNotFoundError:
-        return []
-
-
-# Load the player list from the JSON file
-def load_player_list():
-    with open('players.json', 'r') as file:
-        return json.load(file)
+        if file_path.endswith('.json'):
+            return {}
+        elif file_path.endswith('.csv'):
+            return pd.DataFrame()
 
 
 def get_base64_of_image(path):
@@ -72,14 +64,14 @@ def get_current_date_ist():
 # Function to get matches for today
 def get_today_matches():
     today = get_current_date_ist()
-    matches = load_matches()
+    matches = load_data(MATCHES_JSON)
     today_matches = [match for match in matches if match['date'] == today]
     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
+    matches = load_data(MATCHES_JSON)  # This loads matches correctly with IST times
 
     for match in matches:
         if match["match_id"] == match_id:
@@ -126,7 +118,7 @@ def submit_prediction(
 
     # Ensure predictions DataFrame is loaded or initialized correctly
     try:
-        predictions = pd.read_csv(predictions_csv)
+        predictions = load_data(PREDICTIONS_CSV)
         # Check if all expected columns are present, if not, reinitialize the DataFrame
         expected_columns = ['user_name', 'match_id', 'predicted_winner', 'predicted_motm', 'bid_points']
         if not all(column in predictions.columns for column in expected_columns):
@@ -151,13 +143,12 @@ def submit_prediction(
     }
 
     predictions = pd.concat([predictions, pd.DataFrame([new_prediction])], ignore_index=True)
-    predictions.to_csv(predictions_csv, index=False)
+    predictions.to_csv(PREDICTIONS_CSV, index=False)
     st.success("Prediction submitted successfully!")
 
 
 def get_user_total_points(user_name):
-    with open(users_json, 'r') as file:
-        users = json.load(file)
+    users = load_data(USERS_JSON)
     return users.get(user_name, 0)
 
 
@@ -168,6 +159,87 @@ def calculate_max_bid_points(user_name):
     return max_bid_points
 
 
+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
+    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}")
+
+    matches = get_today_matches()
+    if matches:
+        match_choice = st.selectbox("Select Today's Match", matches, format_func=lambda match: f"{match['teams'][0]} vs {match['teams'][1]}")
+        match_id = match_choice['match_id']
+        teams = match_choice['teams']
+
+        predicted_winner = st.selectbox("Predicted Winner", teams)
+
+        player_list = load_data(PLAYERS_JSON)
+        predicted_motm = ""
+        if predicted_winner in player_list:
+            players = player_list[predicted_winner]
+            predicted_motm = st.selectbox("Predicted Man of the Match", players)
+
+        bid_points = st.number_input("Bid Points", min_value=1, value=100, format="%d")
+
+        if st.button("Submit Prediction"):
+            submit_prediction(user_name, match_id, predicted_winner, predicted_motm, bid_points, max_bid_points)
+    else:
+        st.write("No matches are scheduled for today.")
+
+def display_predictions():
+    if st.button("Show Predictions"):
+        try:
+            todays_predictions = show_todays_match_predictions()
+            if not todays_predictions.empty:
+                st.dataframe(todays_predictions, hide_index=True)
+            else:
+                st.write("No predictions for today's matches yet.")
+        except FileNotFoundError:
+            st.write("No predictions have been submitted yet.")
+
+def display_leaderboard():
+    if st.button("Show Leaderboard"):
+        try:
+            users = load_data(USERS_JSON)  # Adjusted for the new load_data function
+            leaderboard = sorted(users.items(), key=lambda x: x[1], reverse=True)
+            
+            # Generate a list of dictionaries, each representing a row in the leaderboard
+            leaderboard_dicts = [{"Rank": rank+1, "User": user[0], "Points": user[1]} 
+                                 for rank, user in enumerate(leaderboard)]
+            
+            # Convert the list of dictionaries to a DataFrame
+            df_leaderboard = pd.DataFrame(leaderboard_dicts)
+            
+            st.dataframe(df_leaderboard, hide_index=True)
+        except FileNotFoundError:
+            st.write("Leaderboard data not available.")
+
+
+def load_predictions(PREDICTIONS_CSV):
+    try:
+        return pd.read_csv(PREDICTIONS_CSV)
+    except FileNotFoundError:
+        return pd.DataFrame()
+
+
+# Show Predictions functionality
+def show_todays_match_predictions():
+    # Get today's matches
+    today_matches = get_today_matches()
+    today_match_ids = [match['match_id'] for match in today_matches]
+    
+    # Load all predictions
+    predictions = load_predictions(PREDICTIONS_CSV)
+    
+    # Filter predictions for today's matches
+    today_predictions = predictions[predictions['match_id'].isin(today_match_ids)]
+    
+    return today_predictions
+
+
 # Streamlit UI
 encoded_image = get_base64_of_image(image_path)
 custom_css = f"""
@@ -217,84 +289,14 @@ user_guide_content = """
 with st.expander("User Guide 📘"):
     st.markdown(user_guide_content)
 
-# Prediction form
 with st.expander("Submit Prediction 📝"):
+    user_selection_and_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:
-        match_choice = st.selectbox("Select Today's Match", matches, format_func=lambda match: f"{match['teams'][0]} vs {match['teams'][1]}")
-        match_id = match_choice['match_id']
-        teams = match_choice['teams']
-    else:
-        st.write("No matches are scheduled for today.")
-        st.stop()
-
-    # Predictions
-    predicted_winner = st.selectbox("Predicted Winner", teams)
-
-    # Load the player list and populate the dropdown based on the selected team
-    predicted_motm = ""
-    player_list = load_player_list()
-    if predicted_winner in player_list:
-        players = player_list[predicted_winner]
-        predicted_motm = st.selectbox("Predicted Man of the Match", players)
-    # predicted_motm = st.text_input("Predicted Man of the Match")
-
-    bid_points = st.number_input("Bid Points", min_value=1, value=100, format="%d")
-
-    # Submit button
-    if st.button("Submit Prediction"):
-        if user_name != "Select a user...":
-            submit_prediction(user_name, match_id, predicted_winner, predicted_motm, bid_points, max_bid_points)
-
-
-# Show predictions
 with st.expander("Predictions 🔍"):
+    display_predictions()
 
-    # Display predictions
-    if st.button("Show Predictions"):
-        try:
-            df = pd.read_csv(predictions_csv)
-            st.dataframe(df, hide_index=True)
-        except FileNotFoundError:
-            st.write("No predictions have been submitted yet.")
-
-
-# Show leaderboard
 with st.expander("Leaderboard 🏆"):
-
-    # Display leaderboard
-    if st.button("Show Leaderboard"):
-        try:
-            with open(users_json, 'r') as f:
-                users = json.load(f)
-            leaderboard = sorted(users.items(), key=lambda x: x[1], reverse=True)
-            df_leaderboard = pd.DataFrame(leaderboard, columns=['User', 'Points'])
-
-            # Add a 'Rank' column starting from 1
-            df_leaderboard['Rank'] = range(1, len(df_leaderboard) + 1)
-
-            # Reorder DataFrame columns so 'Rank' is first
-            df_leaderboard = df_leaderboard[['Rank', 'User', 'Points']]
-
-            # 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.")
+    display_leaderboard()
 
 
 ############################# Admin Panel ##################################
@@ -304,30 +306,25 @@ ADMIN_PASSPHRASE = "admin123"
 def load_predictions():
     # loading predictions from 'predictions.csv'
     try:
-        return pd.read_csv(predictions_csv)
+        return load_data(PREDICTIONS_CSV)
     except FileNotFoundError:
         return pd.DataFrame(columns=['user_name', 'match_id', 'predicted_winner', 'predicted_motm', 'bid_points'])
 
 
-def load_users():
-    with open(users_json, 'r') as file:
-        return json.load(file)
-
-
 def save_users(users):
-    with open(users_json, 'w') as file:
+    with open(USERS_JSON, 'w') as file:
         json.dump(users, file, indent=4)
 
 
 def save_match_outcomes(outcomes):
-    with open(outcomes_json, 'w') as file:
+    with open(OUTCOMES_JSON, 'w') as file:
         json.dump(outcomes, file, indent=4)
 
 
 def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match):
-    outcomes = load_match_outcomes()  # Load existing match outcomes
+    outcomes = load_data(OUTCOMES_JSON)  # Load existing match outcomes
     predictions = load_predictions()  # Load existing predictions
-    users = load_users()  # Load existing user points
+    users = load_data(USERS_JSON)  # Load existing user points
 
     # Update match outcomes
     match_outcome = next((outcome for outcome in outcomes if outcome['match_id'] == match_id), None)
@@ -387,7 +384,7 @@ with st.sidebar:
             winning_team = expander.selectbox("Winning Team", teams, key="winning_team")
 
             # Fetch and display players for the selected winning team
-            player_list = load_player_list()
+            player_list = load_data(PLAYERS_JSON)
             if winning_team in player_list:
                 players = player_list[winning_team]
                 man_of_the_match = expander.selectbox("Man of the Match", players, key="man_of_the_match")

predictions.csv

@@ -1,5 +1,13 @@
 user_name,match_id,predicted_winner,predicted_motm,bid_points
-Sahil,20240322_2,CSK,Ruturaj Gaikwad,250
-Naveein,20240322_2,CSK,Rachin Ravindra,1000
-Ganesh,20240322_2,CSK,Ruturaj Gaikwad,300
-Jay,20240322_2,CSK,Ruturaj Gaikwad,500
+Sahil,20240322_1,CSK,Ruturaj Gaikwad,250
+Naveein,20240322_1,CSK,Rachin Ravindra,1000
+Ganesh,20240322_1,CSK,Ruturaj Gaikwad,300
+Jay,20240322_1,CSK,Ruturaj Gaikwad,500
+Megha,20240322_1,RCB,Virat Kohli,500
+Arpit,20240322_1,CSK,Ravindra Jadeja,1000
+Sunil,20240322_1,RCB,Virat Kohli,299
+Praveen,20240322_1,CSK,Ravindra Jadeja,1000
+Vinay,20240322_1,CSK,Rachin Ravindra,216
+Kichu,20240322_1,CSK,MS Dhoni,500
+Haaris,20240322_1,CSK,MS Dhoni,500
+Rakesh,20240322_1,CSK,MS Dhoni,500