new changes

app.py

@@ -212,10 +212,16 @@ def calculate_max_bid_points(user_name):
 
 
 def load_users(USERS_JSON):
+    """Load users data with fallback to Hugging Face"""
     try:
-        with open(USERS_JSON, 'r') as file:
-            return json.load(file)
-    except FileNotFoundError:
+        # First try local file
+        if os.path.exists(USERS_JSON):
+            with open(USERS_JSON, 'r', encoding='utf-8') as file:
+                return json.load(file)
+        # If not found, load from Hugging Face
+        return load_and_process_users()
+    except Exception as e:
+        st.error(f"Error loading users data: {e}")
         return {}
 
 
@@ -290,21 +296,22 @@ def display_predictions():
 def display_leaderboard():
     if st.button("Show Leaderboard"):
         try:
-            # # Load the 'leaders' configuration
-            dataset = load_dataset("Jay-Rajput/DIS_IPL_Leads", split='train')
-
+            # Load users data using our centralized function
             users_data = []
-            if dataset:
-                for user, points_dict in dataset[0].items():
-                    points = points_dict.get("points", 0)
-                    last_5_results = " ".join(points_dict.get("last_5_results", ["⚪"] * 5))  # Default: 5 white circles
-                    users_data.append({'User': user, 'Points': points, "Last 5 Bids": last_5_results})
-            else:
-                data = load_users(USERS_JSON)
-                for user, points_dict in data.items():
-                    points = points_dict.get("points", 0)
-                    last_5_results = " ".join(points_dict.get("last_5_results", ["⚪"] * 5))  # Default: 5 white circles
-                    users_data.append({'User': user, 'Points': points, "Last 5 Bids": last_5_results})
+            data = load_users(USERS_JSON)
+            
+            for user, user_info in data.items():
+                points = user_info.get("points", 0)
+                last_5_results = " ".join(user_info.get("last_5_results", ["⚪"] * 5))
+                users_data.append({
+                    'User': user,
+                    'Points': points,
+                    "Last 5 Bids": last_5_results
+                })
+
+            if not users_data:
+                st.warning("No user data found!")
+                return
 
             leaderboard = pd.DataFrame(users_data)
             
@@ -318,8 +325,9 @@ def display_leaderboard():
             leaderboard = leaderboard[['Rank', 'User', 'Points', 'Last 5 Bids']]
             
             st.dataframe(leaderboard, hide_index=True)
+            
         except Exception as e:
-            st.write("Failed to load leaderboard data: ", str(e))
+            st.error(f"Failed to load leaderboard data: {str(e)}")
 
 
 # Streamlit UI
@@ -414,86 +422,108 @@ def fetch_latest_predictions(match_id):
         return pd.DataFrame()
 
 
-def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, outcome_only=False):
-    features = Features({
-        'match_id': Value('string'),
-        'man_of_the_match': Value('string'),
-        'winning_team': Value('string'),
-    })
-
-    # 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)
+def load_and_process_users():
+    """Load and process users data from Hugging Face"""
+    try:
+        # Load dataset from Hugging Face
+        users = load_dataset("Jay-Rajput/DIS_IPL_Leads", split="train")
+        users_df = users.to_dict()
+        
+        # Process into the correct format
+        users_dict = {}
+        for user_name, user_data in users_df.items():
+            users_dict[user_name] = user_data[0]  # Extract the first (and only) item
+        
+        # Save to local JSON file for faster access
+        with open(USERS_JSON, 'w', encoding='utf-8') as f:
+            json.dump(users_dict, f, ensure_ascii=False, indent=4)
+        
+        return users_dict
+    except Exception as e:
+        st.error(f"Error loading users data: {e}")
+        return {}
 
-    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]['points']
-            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
-                    result_indicator = "🟢"  # Correct Prediction
-                    if predicted_motm == man_of_the_match:
-                        user_points += 500
+def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, outcome_only=False):
+    try:
+        # First load and process the latest users data
+        users_dict = load_and_process_users()
+        
+        # Rest of your existing code for updating outcomes...
+        features = Features({
+            'match_id': Value('string'),
+            'man_of_the_match': Value('string'),
+            'winning_team': Value('string'),
+        })
+
+        # Load existing match outcomes
+        outcomes = load_dataset("Jay-Rajput/DIS_IPL_Outcomes", split="train")  
+        outcomes_df = pd.DataFrame(outcomes)
+
+        # 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:
+            # Load predictions
+            predictions = fetch_latest_predictions(match_id)
+
+            # Update user points based on prediction accuracy
+            users_with_predictions = set(predictions['user_name'])
+            for user_name, user_data in users_dict.items():
+                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_data['points'] += 2000 + bid_points
+                        result_indicator = "🟢"
+                        if predicted_motm == man_of_the_match:
+                            user_data['points'] += 500
+                    else:
+                        user_data['points'] -= 200 + bid_points
+                        result_indicator = "🔴"
                 else:
-                    user_points -= 200 + bid_points
-                    result_indicator = "🔴"  # Wrong Prediction
-            else:
-                # Deduct 1000 points for not submitting a prediction
-                user_points -= 1000
-                result_indicator = "⚪"  # No Prediction
+                    user_data['points'] -= 1000
+                    result_indicator = "⚪"
                 
-            # Ensure user_points is never negative
-            user_points = max(user_points, 0)
-
-            # Update user's points in the DataFrame
-            users_df[user_name][0]['points'] = user_points
-            users[user_name][0]['points'] = user_points
-            
-            # Maintain last 5 prediction results
-            if "last_5_results" not in users_df[user_name][0]:
-                users_df[user_name][0]["last_5_results"] = []
-            users_df[user_name][0]["last_5_results"].insert(0, result_indicator)  # Insert at beginning
-            users_df[user_name][0]["last_5_results"] = users_df[user_name][0]["last_5_results"][:5]  # Keep only last 5
+                user_data['points'] = max(user_data['points'], 0)
+                
+                # Update last 5 results
+                if "last_5_results" not in user_data:
+                    user_data["last_5_results"] = []
+                user_data["last_5_results"].insert(0, result_indicator)
+                user_data["last_5_results"] = user_data["last_5_results"][:5]
+
+            # Save updated users data back to Hugging Face
+            users_dataset = Dataset.from_dict({k: [v] for k, v in users_dict.items()})
+            users_dataset.push_to_hub("Jay-Rajput/DIS_IPL_Leads")
             
-            if "last_5_results" not in users[user_name][0]:
-                users[user_name][0]["last_5_results"] = []
-            users[user_name][0]["last_5_results"].insert(0, result_indicator)  # Insert at beginning
-            users[user_name][0]["last_5_results"] = users[user_name][0]["last_5_results"][:5]  # Keep only last 5
-
-        users.to_json(USERS_JSON)
-        updated_dataset = Dataset.from_pandas(users_df)
-        updated_dataset.push_to_hub("Jay-Rajput/DIS_IPL_Leads", split="train")
+            # Also save locally
+            with open(USERS_JSON, 'w', encoding='utf-8') as f:
+                json.dump(users_dict, f, ensure_ascii=False, indent=4)
 
-    outcomes.to_json(OUTCOMES)
-    outcomes.push_to_hub("Jay-Rajput/DIS_IPL_Outcomes", split="train")
+        # Save outcomes
+        outcomes.to_json(OUTCOMES)
+        outcomes.push_to_hub("Jay-Rajput/DIS_IPL_Outcomes", split="train")
+        
+        return True
+    except Exception as e:
+        st.error(f"Error updating leaderboard: {e}")
+        return False
 
 
 # Function to fetch matches for a given date
@@ -553,6 +583,7 @@ with st.sidebar:
                         # 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!")
+                        st.experimental_rerun()
             else:
                 expander.write("No matches available for the selected date.")
         else: