enhanced admin panel

README.md

@@ -1,8 +1,8 @@
 ---
-title: DIS IPL Predictions
-emoji: 🦀
+title: Testing
+emoji: 📈
 colorFrom: red
-colorTo: pink
+colorTo: red
 sdk: streamlit
 sdk_version: 1.32.2
 app_file: app.py
@@ -10,48 +10,4 @@ pinned: false
 license: apache-2.0
 ---
 
-# DIS IPL Match Predictions App
-
-> "Predict, Compete, and Win 🏏 - Where Every Guess Counts! 🏆"
-
-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
-
-### Prerequisites
-
-- Python 3.x installed on your system
-- Git installed on your system
-- Pip package manager
-
-### Installation
-
-1. Clone this repository to your local machine using Git:
-
-   ```bash
-   git clone <repository_url>
-   ```
-
-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
-   ```
-
-### Running The APP
-
-1. After installing the dependencies, you can run the Streamlit app using the following command:
-
-   ```bash
-   streamlit run app.py
-   ```
-
-2. The app will start running locally and open in your default web browser.
-
-Thanks
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,20 +1,52 @@
 import base64
+import io
 import json
-from datetime import datetime, timedelta
+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 load_dataset
+from huggingface_hub import CommitScheduler, HfApi
 
 # File paths as constants
 PREDICTIONS_CSV = 'dis_predictions.csv'
-USERS_JSON = 'users.json'
+USERS_JSON = 'leaders/users.json'
 MATCHES_JSON = 'matches.json'
 OUTCOMES_JSON = '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)
+
+# Initialize CommitScheduler
+scheduler = CommitScheduler(
+    repo_id="DIS_IPL_Dataset",
+    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=120,  # Push every 240 minutes (4 hours)
+)
+
+# Initialize CommitScheduler
+scheduler = CommitScheduler(
+    repo_id="DIS_IPL_Dataset",
+    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=120,  # Push every 240 minutes (4 hours)
+)
+
+
 # Initialize CSV and JSON files if they don't exist
 def initialize_files():
     # Initialize predictions CSV
@@ -122,34 +154,28 @@ def submit_prediction(
         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 = load_predictions(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):
-            raise ValueError("CSV file missing one or more columns; Reinitializing.")
-    except (FileNotFoundError, ValueError) as e:
-        predictions = pd.DataFrame(columns=expected_columns)
-
-    # Check for duplicate prediction for the same match by the same user
-    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.")
-            return
+    prediction_id = uuid.uuid4().hex
+    prediction_date = datetime.now().strftime('%Y-%m-%d')
 
-    # Append new prediction
-    new_prediction = {
+    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
+        'bid_points': bid_points,
+        'prediction_date': prediction_date  # Include the prediction date
     }
+    
+    # Construct the filename to include match_id for easier retrieval
+    prediction_file_name = f"prediction_{match_id}_{prediction_id}.json"
+    prediction_file = PREDICTIONS_FOLDER / prediction_file_name
+
+    with scheduler.lock:
+        with prediction_file.open("a") as file:
+            file.write(json.dumps(prediction_data))
+            file.write("\n")
 
-    predictions = pd.concat([predictions, pd.DataFrame([new_prediction])], ignore_index=True)
-    predictions.to_csv(PREDICTIONS_CSV, index=False)
     st.success("Prediction submitted successfully!")
 
 
@@ -203,16 +229,44 @@ def user_selection_and_prediction():
     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.")
+        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"):
@@ -232,21 +286,6 @@ def display_leaderboard():
             st.write("Leaderboard data not available.")
 
 
-# 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"""
@@ -309,9 +348,10 @@ with st.expander("Leaderboard 🏆"):
 ############################# Admin Panel ##################################
 ADMIN_PASSPHRASE = "admin123"
 
-def save_users(users):
-    with open(USERS_JSON, 'w') as file:
-        json.dump(users, file, indent=4)
+def fetch_latest_predictions(match_id):
+    dataset = load_dataset("Jay-Rajput/DIS_IPL_Dataset", config_name="predictions")
+    predictions = dataset['train'].filter(lambda example: example['match_id'] == match_id)
+    return predictions
 
 
 def save_match_outcomes(outcomes):
@@ -320,9 +360,13 @@ def save_match_outcomes(outcomes):
 
 
 def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match):
+    # Fetch latest predictions from the dataset repo
+    predictions = fetch_latest_predictions(match_id)
+    
     outcomes = load_data(OUTCOMES_JSON)  # Load existing match outcomes
-    predictions = load_predictions(PREDICTIONS_CSV)  # Load existing predictions
-    users = load_users(USERS_JSON)  # Load existing user points
+    # Load existing match outcomes and user data from the test split
+    dataset = load_dataset("Jay-Rajput/DIS_IPL_Dataset", config_name="leaders")
+    users = {item['user_name']: item for item in dataset['train']}
 
     # Directly update or add the match outcome
     outcome_exists = False
@@ -335,22 +379,23 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match):
         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
-    for _, prediction in predictions.iterrows():
-        if prediction['match_id'] == match_id:
-            user_name = prediction['user_name']
-            users[user_name] = users.get(user_name, 0)  # Initialize user points if not present
-
-            # Update points based on prediction accuracy
-            if prediction['predicted_winner'] == winning_team:
-                users[user_name] += 1000
-                users[user_name] += prediction['bid_points'] 
-                if prediction['predicted_motm'] == man_of_the_match:
-                    users[user_name] += 400  # Bonus for both correct predictions
-            else:
-                users[user_name] -= 200 + prediction['bid_points']  # Penalty for wrong team prediction
+    for prediction in predictions:
+        user_name = prediction['user_name']
+        # Initialize user points if not present
+        if user_name not in users:
+            users[user_name] = {'user_name': user_name, 'points': 0}
+
+        # Update points based on prediction accuracy
+        if prediction['predicted_winner'] == winning_team:
+            users[user_name] += 1000
+            users[user_name] += prediction['bid_points'] 
+            if prediction['predicted_motm'] == man_of_the_match:
+                users[user_name] += 400  # Bonus for both correct predictions
+        else:
+            users[user_name] -= 200 + prediction['bid_points']  # Penalty for wrong team prediction
 
     save_match_outcomes(outcomes)
-    save_users(users)
+    users.save_to_disk(USERS_JSON)
 
 
 with st.sidebar:
@@ -359,11 +404,17 @@ with st.sidebar:
 
     if admin_pass == ADMIN_PASSPHRASE:
         expander.success("Authenticated")
-        matches = get_today_matches()  # This function fetches today's matches
-
+        
+        all_matches = load_data(MATCHES_JSON)
+        match_outcomes = load_data(OUTCOMES_JSON)
+        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:
-            match_selection = expander.selectbox("Select Match", matches, format_func=lambda match: f"{match['teams'][0]} vs {match['teams'][1]}", key="match_selection")
+        if matches_without_outcomes:
+            match_selection = expander.selectbox("Select Match", matches_without_outcomes, format_func=lambda match: f"{match['teams'][0]} vs {match['teams'][1]}", key="match_selection")
             selected_match_id = match_selection['match_id']
             teams = match_selection['teams']
 

dis_predictions.csv

@@ -3,3 +3,9 @@ Sunil,20240325_6,RCB,Glenn Maxwell,900
 Sahil,20240325_6,RCB,Virat Kohli,350
 Vinay,20240325_6,RCB,Faf du Plessis,200
 Megha,20240325_6,RCB,Virat Kohli,500
+Jay,20240325_6,RCB,Virat Kohli,200
+Sai,20240325_6,RCB,Glenn Maxwell,620
+Haaris,20240325_6,RCB,Virat Kohli,1200
+Naveein,20240325_6,RCB,Faf du Plessis,500
+Praveen,20240325_6,RCB,Faf du Plessis,1800
+Kishore,20240325_6,RCB,Glenn Maxwell,500

leaders/users.json

@@ -0,0 +1,17 @@
+{
+    "Arpit": 11300,
+    "Ganesh": 5750,
+    "Haaris": 6000,
+    "Jay": 6500,
+    "Kishore": 5700,
+    "Megha": 5900,
+    "Naveein": 6300,
+    "Neha": 5600,
+    "Praveen": 9000,
+    "Rakesh": 6500,
+    "Sai": 3100,
+    "Sahil": 6600,
+    "Sunil": 4501,
+    "Vaibhav": 5600,
+    "Vinay": 5300
+}

match_outcomes.json

@@ -23,5 +23,10 @@
         "match_id": "20240324_5",
         "winning_team": "GT",
         "man_of_the_match": "B Sai Sudharsan"
+    },
+    {
+        "match_id": "20240325_6",
+        "winning_team": "RCB",
+        "man_of_the_match": "Virat Kohli"
     }
 ]

requirements.txt

@@ -1,3 +1,6 @@
+datasets
+huggingface_hub
 pandas
 pytz
+requests
 streamlit