Enhanced admin panel

README.md

@@ -1,8 +1,8 @@
 ---
-title: Testing
-emoji: 📈
+title: DIS IPL Predictions
+emoji: 🦀
 colorFrom: red
-colorTo: red
+colorTo: pink
 sdk: streamlit
 sdk_version: 1.32.2
 app_file: app.py
@@ -10,4 +10,48 @@ pinned: false
 license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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

app.py

@@ -11,6 +11,7 @@ import pytz
 import streamlit as st
 from datasets import load_dataset
 from huggingface_hub import CommitScheduler, HfApi
+from datasets import Dataset
 
 # File paths as constants
 PREDICTIONS_CSV = 'dis_predictions.csv'
@@ -348,8 +349,12 @@ with st.expander("Leaderboard 🏆"):
 ADMIN_PASSPHRASE = "admin123"
 
 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)
+    dataset = load_dataset("Jay-Rajput/DIS_IPL_Dataset", name="predictions")
+    # Convert to pandas DataFrame
+    df = dataset.to_pandas()
+    # Remove duplicate rows
+    df_unique = df.drop_duplicates(subset=['user_name'])
+    predictions = df_unique['train'].filter(lambda example: example['match_id'] == match_id)
     return predictions
 
 
@@ -364,8 +369,8 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match):
     
     outcomes = load_data(OUTCOMES_JSON)  # Load existing match outcomes
     # 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']}
+    dataset = load_dataset("Jay-Rajput/DIS_IPL_Dataset", name="leaders")
+    users = dataset.to_pandas()
 
     # Directly update or add the match outcome
     outcome_exists = False
@@ -378,23 +383,26 @@ 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:
+    for index, prediction in predictions.iterrows():
         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}
+        predicted_winner = prediction['predicted_winner']
+        predicted_motm = prediction['predicted_motm']
+        bid_points = prediction['bid_points']
 
         # Update points based on prediction accuracy
-        if prediction['predicted_winner'] == winning_team:
+        if 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] += bid_points 
+            if 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
+            users[user_name] -= 200 + bid_points  # Penalty for wrong team prediction
 
     save_match_outcomes(outcomes)
     users.save_to_disk(USERS_JSON)
+    # Convert the updated DataFrame back to a Hugging Face Dataset and push updates
+    updated_dataset = Dataset.from_pandas(users)
+    updated_dataset.push_to_hub("Jay-Rajput/DIS_IPL_Dataset")
 
 
 with st.sidebar: