import base64 import json import os import uuid from datetime import datetime from pathlib import Path import pandas as pd import pytz import streamlit as st from datasets import Dataset, load_dataset from huggingface_hub import CommitScheduler # File paths as constants USERS_JSON = 'leaders/users.json' MATCHES_JSON = 'matches.json' OUTCOMES_JSON = 'match_outcomes.json' OUTCOMES = 'outcomes/match_outcomes.json' PLAYERS_JSON = 'players.json' image_path = 'ipl_image.png' PREDICTIONS_FOLDER = Path("predictions") PREDICTIONS_FOLDER.mkdir(parents=True, exist_ok=True) users_file = Path("leaders") / f"users.json" USERS_FOLDER = users_file.parent USERS_FOLDER.mkdir(parents=True, exist_ok=True) outcomes_file = Path("outcomes") / f"match_outcomes.json" OUTCOMES_FOLDER = outcomes_file.parent OUTCOMES_FOLDER.mkdir(parents=True, exist_ok=True) # Initialize CommitScheduler scheduler = CommitScheduler( repo_id="DIS_IPL_Preds", repo_type="dataset", folder_path=PREDICTIONS_FOLDER, # Local folder where predictions are saved temporarily path_in_repo="predictions", # Path in dataset repo where predictions will be saved every=720, # Push every 240 minutes (4 hours) ) # Initialize CommitScheduler scheduler = CommitScheduler( repo_id="DIS_IPL_Leads", repo_type="dataset", folder_path=USERS_FOLDER, # Local folder where users are saved temporarily path_in_repo="leaders", # Path in dataset repo where predictions will be saved every=720, # Push every 240 minutes (4 hours) ) # Initialize CommitScheduler scheduler = CommitScheduler( repo_id="DIS_IPL_Outcomes", repo_type="dataset", folder_path=OUTCOMES_FOLDER, # Local folder where users are saved temporarily path_in_repo="outcomes", # Path in dataset repo where predictions will be saved every=720, # Push every 240 minutes (4 hours) ) 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: 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: if file_path.endswith('.json'): return {} elif file_path.endswith('.csv'): return pd.DataFrame() def get_base64_of_image(path): with open(path, "rb") as image_file: return base64.b64encode(image_file.read()).decode() # Get today's date in IST to load today's match def get_current_date_ist(): tz_IST = pytz.timezone('Asia/Kolkata') datetime_ist = datetime.now(tz_IST) return datetime_ist.strftime('%Y-%m-%d') # Function to get matches for today def get_today_matches(): today = get_current_date_ist() 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_data(MATCHES_JSON) # 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: 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, 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}") st.error(f"Oops! 🙈 Looks like you're going overboard with your bid points! Your bid points cannot exceed your total points. Maximum allowed bid points: {max_bid_points} 😱") return prediction_id = uuid.uuid4().hex prediction_time = datetime.now().strftime('%Y-%m-%d') prediction_data = { 'prediction_id': prediction_id, 'user_name': user_name, 'match_id': match_id, 'predicted_winner': predicted_winner, 'predicted_motm': predicted_motm, 'bid_points': bid_points, 'prediction_date': prediction_time # Include the prediction time } # Construct the filename to include match_id for easier retrieval prediction_file_name = f"prediction_{match_id}_{user_name}.json" prediction_file = PREDICTIONS_FOLDER / prediction_file_name # Load existing predictions for the user and match, if any existing_predictions = [] if prediction_file.exists(): with prediction_file.open("r") as file: for line in file: existing_predictions.append(json.loads(line.strip())) # Update existing prediction if it exists for the same user and match prediction_updated = False for existing_prediction in existing_predictions: if existing_prediction['user_name'] == user_name and existing_prediction['match_id'] == match_id: existing_prediction.update(prediction_data) prediction_updated = True break # Save the updated predictions back to the file with scheduler.lock: if not prediction_updated: # Append the new prediction if it doesn't already exist with prediction_file.open("a") as file: file.write(json.dumps(prediction_data)) file.write("\n") else: with prediction_file.open("w") as file: for prediction in existing_predictions: file.write(json.dumps(prediction)) file.write("\n") st.success("Prediction submitted successfully!") def get_user_total_points(user_name): # users_dataset = load_dataset("Jay-Rajput/DIS_IPL_Leads", split="train") # users = users_dataset.to_dict() users = load_users(USERS_JSON) return users.get(user_name, {}).get('points') 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 total_points def load_users(USERS_JSON): try: with open(USERS_JSON, 'r') as file: return json.load(file) except FileNotFoundError: return {} 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=0, 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"): all_predictions = [] # Check if the directory exists if not os.path.exists(PREDICTIONS_FOLDER): st.write("No predictions directory found.") return # List all JSON files in the directory for filename in os.listdir(PREDICTIONS_FOLDER): if filename.endswith('.json'): file_path = os.path.join(PREDICTIONS_FOLDER, filename) # Read each JSON file and append its contents to the list with open(file_path, 'r') as file: prediction = json.load(file) all_predictions.append(prediction) # Convert the list of dictionaries to a DataFrame predictions_df = pd.DataFrame(all_predictions) if not predictions_df.empty: predictions_df['prediction_date'] = predictions_df.apply(lambda x: datetime.strptime(x['prediction_date'], '%Y-%m-%d'), axis=1) # Filter for today's predictions today_str = datetime.now().strftime('%Y-%m-%d') todays_predictions = predictions_df[predictions_df['prediction_date'] == today_str] # Remove the 'prediction_id' column if it exists if 'prediction_id' in todays_predictions.columns: todays_predictions = todays_predictions.drop(columns=['prediction_id', 'prediction_date']) st.dataframe(todays_predictions, hide_index=True) else: st.write("No predictions for today's matches yet.") def display_leaderboard(): if st.button("Show Leaderboard"): try: # # Load the 'leaders' configuration # 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}) leaderboard = pd.DataFrame(users_data) # Sort DataFrame by points in descending order leaderboard = leaderboard.sort_values(by='Points', ascending=False) # Add a 'Rank' column starting from 1 leaderboard['Rank'] = range(1, len(leaderboard) + 1) # Select and order the columns for display leaderboard = leaderboard[['Rank', 'User', 'Points']] st.dataframe(leaderboard, hide_index=True) except Exception as e: st.write("Failed to load leaderboard data: ", str(e)) # Streamlit UI encoded_image = get_base64_of_image(image_path) custom_css = f""" """ # Apply custom CSS st.markdown(custom_css, unsafe_allow_html=True) # Use the custom class in a div with your title st.markdown('
DIS IPL Match Predictions
', unsafe_allow_html=True) st.write("🏆 Predict, Compete, and Win 🏏 - Where Every Guess Counts! 🏆") user_guide_content = """ ### 📘 User Guide #### Submitting Predictions - **Match Selection**: Choose the match you want to predict from today's available matches. - **Team and Player Prediction**: Select the team you predict will win and the "Man of the Match". - **Bid Points**: Enter the number of points you wish to bid on your prediction. Remember, the maximum you can bid is capped at 20% of your total points. #### Scoring System - **Winning Team Prediction**: Correct predictions earn you 1000 points, while incorrect predictions deduct 200 points. - **Man of the Match Prediction**: Correctly predicting the "Man of the Match" awards you 200 points. No penalty for incorrect guesses. - **Bonus Points**: An additional 200 points bonus is awarded for getting both the team and "Man of the Match" predictions right. #### Bid Point Constraints - You cannot bid more than 20% of your current total points. - Bid points will be doubled if your prediction is correct, and deducted if incorrect. #### Rules for Submission - Predictions must be submitted before the match starts. - Only one prediction per match is allowed. - Review your prediction carefully before submission, as it cannot be changed once submitted. """ # User Guide as an expander with st.expander("User Guide 📘"): st.markdown(user_guide_content) with st.expander("Submit Prediction 📝"): user_selection_and_prediction() with st.expander("Predictions 🔍"): display_predictions() with st.expander("Leaderboard 🏆"): display_leaderboard() ############################# Admin Panel ################################## ADMIN_PASSPHRASE = "admin123" def fetch_latest_predictions(match_id): # Load the dataset. Adjust "split" to "train" or appropriate if "predictions" is a configuration. dataset = load_dataset("Jay-Rajput/DIS_IPL_Preds", split="train") # Convert the dataset to a pandas DataFrame df = pd.DataFrame(dataset) # Ensure the DataFrame is not empty and contains the required columns if not df.empty and {'user_name', 'match_id'}.issubset(df.columns): # Filter rows by 'match_id' filtered_df = df[df['match_id'] == match_id] # Drop duplicate rows based on 'user_name' unique_df = filtered_df.drop_duplicates(subset=['user_name']) return unique_df else: return pd.DataFrame() 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]['0'] 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 if predicted_motm == man_of_the_match: user_points += 500 else: user_points -= 200 + bid_points else: # Deduct 1000 points for not submitting a prediction user_points -= 1000 if user_points < 0: user_points = 0 # Update user's points in the DataFrame users_df[user_name][0]['0'] = user_points 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] with st.sidebar: expander = st.expander("Admin Panel", expanded=False) admin_pass = expander.text_input("Enter admin passphrase:", type="password", key="admin_pass") if admin_pass == ADMIN_PASSPHRASE: expander.success("Authenticated") all_matches = load_data(MATCHES_JSON) match_outcomes = load_dataset("Jay-Rajput/DIS_IPL_Outcomes", split="train") submitted_match_ids = [outcome["match_id"] for outcome in match_outcomes] # Filter matches to those that do not have outcomes submitted yet matches_without_outcomes = [match for match in all_matches if match["match_id"] not in submitted_match_ids] # If matches are available, let the admin select one if matches_without_outcomes: # Optional: Allow the admin to filter matches by date selected_date = expander.date_input("Select Match Date", key="match_date") if selected_date: filtered_matches = fetch_matches_by_date(matches_without_outcomes, selected_date) else: filtered_matches = matches_without_outcomes if filtered_matches: match_selection = expander.selectbox("Select Match", filtered_matches, format_func=lambda match: f"{match['teams'][0]} vs {match['teams'][1]} (Match ID: {match['match_id']})", key="match_selection") selected_match_id = match_selection['match_id'] teams = match_selection['teams'] # Let admin select the winning team winning_team = expander.selectbox("Winning Team", teams, key="winning_team") # Fetch and display players for the selected winning team 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") else: players = [] man_of_the_match = expander.text_input("Man of the Match (Type if not listed)", key="man_of_the_match_fallback") # Add checkbox for outcome only submission outcome_only = expander.checkbox("Submit Outcome Only", key="outcome_only_checkbox") if expander.button("Submit Match Outcome", key="submit_outcome"): if outcome_only: # Submit match outcome without updating user scores update_leaderboard_and_outcomes(selected_match_id, winning_team, man_of_the_match, outcome_only=True) expander.success("Match outcome submitted!") else: # Submit match outcome and update user scores update_leaderboard_and_outcomes(selected_match_id, winning_team, man_of_the_match) expander.success("Match outcome submitted and leaderboard updated!") else: expander.write("No matches available for the selected date.") else: expander.write("No matches are available for today.") else: if admin_pass: # Show error only if something was typed expander.error("Not authenticated")