new changes

app.py

@@ -4,8 +4,7 @@ import os
 import uuid
 from datetime import datetime
 from pathlib import Path
-import fcntl
-import tempfile
+
 import pandas as pd
 import pytz
 import streamlit as st
@@ -60,95 +59,27 @@ scheduler = CommitScheduler(
 )
 
 
-def safe_write_json(data, file_path):
-    """Safely write JSON data with atomic write pattern"""
-    try:
-        file_path = Path(file_path)
-        file_path.parent.mkdir(parents=True, exist_ok=True)
-        
-        # Write to temporary file first
-        with tempfile.NamedTemporaryFile(
-            mode='w',
-            dir=file_path.parent,
-            prefix=file_path.stem,
-            suffix='.tmp',
-            delete=False
-        ) as tmp_file:
-            json.dump(data, tmp_file, ensure_ascii=False, indent=4)
-            tmp_file.flush()
-            os.fsync(tmp_file.fileno())
-        
-        # Atomic rename
-        os.replace(tmp_file.name, file_path)
-        return True
-    except Exception as e:
-        st.error(f"Error writing JSON file: {e}")
-        try:
-            os.unlink(tmp_file.name)
-        except:
-            pass
-        return False
-
-def safe_load_json(file_path):
-    """Safely load JSON data with file locking"""
-    try:
-        file_path = Path(file_path)
-        if not file_path.exists():
-            return {}
-            
-        with open(file_path, 'r', encoding='utf-8') as f:
-            fcntl.flock(f, fcntl.LOCK_SH)
-            try:
-                data = json.load(f)
-                # Basic validation
-                if not isinstance(data, dict):
-                    st.error("Invalid JSON structure - expected dictionary")
-                    return {}
-                return data
-            except json.JSONDecodeError:
-                st.error("Invalid JSON file - contains syntax errors")
-                return {}
-            finally:
-                fcntl.flock(f, fcntl.LOCK_UN)
-    except Exception as e:
-        st.error(f"Error loading JSON file: {e}")
-        return {}
-
-
 def load_data(file_path):
     """
-    Load data from a JSON or CSV file with better error handling.
+    Load data from a JSON or CSV file.
     
     Args:
     file_path (str): The path to the file to load.
     
     Returns:
-    dict or pd.DataFrame: The loaded data. Returns empty dict/DataFrame on error.
+    pd.DataFrame or dict: The loaded data.
     """
     try:
         if file_path.endswith('.json'):
-            with open(file_path, 'r', encoding='utf-8') as file:
-                try:
-                    return json.load(file)
-                except json.JSONDecodeError:
-                    st.error(f"Invalid JSON in {file_path}. Loading empty data.")
-                    return {}
+            with open(file_path, 'r') as file:
+                return json.load(file)
         elif file_path.endswith('.csv'):
-            try:
-                return pd.read_csv(file_path)
-            except Exception as e:
-                st.error(f"Error reading CSV {file_path}: {e}")
-                return pd.DataFrame()
+            return pd.read_csv(file_path)
     except FileNotFoundError:
         if file_path.endswith('.json'):
             return {}
         elif file_path.endswith('.csv'):
             return pd.DataFrame()
-    except Exception as e:
-        st.error(f"Unexpected error loading {file_path}: {e}")
-        if file_path.endswith('.json'):
-            return {}
-        return pd.DataFrame()
 
 
 def get_base64_of_image(path):
@@ -280,77 +211,43 @@ def calculate_max_bid_points(user_name):
     return max_bid_points
 
 
-def load_users(users_json_path):
-    """Load users data with automatic fallback to Hugging Face"""
-    # First try local file
-    local_data = safe_load_json(users_json_path)
-    if local_data:
-        return local_data
-    
-    # If local file fails, try Hugging Face
+def load_users(USERS_JSON):
     try:
-        users = load_dataset("Jay-Rajput/DIS_IPL_Leads", split="train")
-        users_dict = {}
-        for user_name, user_data in users.to_dict().items():
-            users_dict[user_name] = user_data[0]  # Extract from list
-            
-        # Save to local file for next time
-        if users_dict and safe_write_json(users_dict, users_json_path):
-            return users_dict
-        return {}
-    except Exception as e:
-        st.error(f"Error loading from Hugging Face: {e}")
+        with open(USERS_JSON, 'r') as file:
+            return json.load(file)
+    except FileNotFoundError:
         return {}
 
 
 def user_selection_and_prediction():
-    try:
-        users_data = load_users(USERS_JSON)  # Use our improved load_users function
-        if not users_data:
-            st.error("Failed to load users data. Please try again later.")
-            return
-            
-        users = list(users_data.keys())
-        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.")
-    except Exception as e:
-        st.error(f"Error in prediction submission: {e}")
+    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():
@@ -393,22 +290,21 @@ def display_predictions():
 def display_leaderboard():
     if st.button("Show Leaderboard"):
         try:
-            # Load users data using our centralized function
+            # # Load the 'leaders' configuration
+            dataset = load_dataset("Jay-Rajput/DIS_IPL_Leads", split='train')
+
             users_data = []
-            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
+            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})
 
             leaderboard = pd.DataFrame(users_data)
             
@@ -422,9 +318,8 @@ def display_leaderboard():
             leaderboard = leaderboard[['Rank', 'User', 'Points', 'Last 5 Bids']]
             
             st.dataframe(leaderboard, hide_index=True)
-            
         except Exception as e:
-            st.error(f"Failed to load leaderboard data: {str(e)}")
+            st.write("Failed to load leaderboard data: ", str(e))
 
 
 # Streamlit UI
@@ -519,108 +414,81 @@ def fetch_latest_predictions(match_id):
         return pd.DataFrame()
 
 
-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 {}
+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)
 
-def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, outcome_only=False):
-    try:
-        # Load current data
-        users_dict = load_users(USERS_JSON)
-        if not users_dict:
-            st.error("Failed to load users data")
-            return False
-        
-        # 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 = "🔴"
+    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
                 else:
-                    user_data['points'] -= 1000
-                    result_indicator = "⚪"
-                
-                user_data['points'] = max(user_data['points'], 0)
+                    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
                 
-                # 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
-            if not safe_write_json(users_dict, USERS_JSON):
-                st.error("Failed to save updated users data")
-                return False
+            # 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
             
-            # Push to Hugging Face
-            try:
-                users_dataset = Dataset.from_dict({k: [v] for k, v in users_dict.items()})
-                users_dataset.push_to_hub("Jay-Rajput/DIS_IPL_Leads")
-            except Exception as e:
-                st.error(f"Failed to update Hugging Face: {e}")
-
-        # Save outcomes
-        outcomes.to_json(OUTCOMES)
-        outcomes.push_to_hub("Jay-Rajput/DIS_IPL_Outcomes", split="train")
-        st.success("Match outcome submitted and leaderboard updated!")
-        return True
-    except Exception as e:
-        st.error(f"Error updating leaderboard: {e}")
-        return False
+            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")
+
+    outcomes.to_json(OUTCOMES)
+    outcomes.push_to_hub("Jay-Rajput/DIS_IPL_Outcomes", split="train")
 
 
 # Function to fetch matches for a given date
@@ -672,19 +540,14 @@ with st.sidebar:
                 outcome_only = expander.checkbox("Submit Outcome Only", key="outcome_only_checkbox")
 
                 if expander.button("Submit Match Outcome", key="submit_outcome"):
-                    with st.spinner("Processing match outcome..."):
-                        if update_leaderboard_and_outcomes(
-                            selected_match_id, 
-                            winning_team, 
-                            man_of_the_match, 
-                            outcome_only
-                        ):
-                            # Clear any previous errors
-                            st.success("Successfully updated match results and leaderboard!")
-                            # Force refresh the UI
-                            st.experimental_rerun()
-                        else:
-                            st.error("Failed to update match results")
+                    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:

leaders/users.json

@@ -0,0 +1,332 @@
+{
+  "Arpit": {
+      "last_5_results": [
+          "🔴",
+          "🔴",
+          "⚪",
+          "🔴",
+          "🔴"
+      ],
+      "points": 994,
+      "wildcard": [
+          0,
+          0,
+          0
+      ]
+  },
+  "Ganesh": {
+      "last_5_results": [
+          "⚪",
+          "⚪",
+          "⚪",
+          "⚪",
+          "⚪"
+      ],
+      "points": 0,
+      "wildcard": [
+          0,
+          0,
+          0
+      ]
+  },
+  "Haaris": {
+      "last_5_results": [
+          "⚪",
+          "⚪",
+          "⚪",
+          "⚪",
+          "⚪"
+      ],
+      "points": 0,
+      "wildcard": [
+          0,
+          0,
+          0
+      ]
+  },
+  "Jay": {
+      "last_5_results": [
+          "⚪",
+          "🔴",
+          "⚪",
+          "🔴",
+          "🔴"
+      ],
+      "points": 4312,
+      "wildcard": [
+          0,
+          0,
+          0
+      ]
+  },
+  "Kishore": {
+      "last_5_results": [
+          "🔴",
+          "⚪",
+          "⚪",
+          "🔴",
+          "🔴"
+      ],
+      "points": 12900,
+      "wildcard": [
+          0,
+          0,
+          0
+      ]
+  },
+  "Megha": {
+      "last_5_results": [
+          "🔴",
+          "🟢",
+          "🔴",
+          "⚪",
+          "🔴"
+      ],
+      "points": 10000,
+      "wildcard": [
+          0,
+          0,
+          0
+      ]
+  },
+  "Naveein": {
+      "last_5_results": [
+          "⚪",
+          "🟢",
+          "⚪",
+          "🔴",
+          "🔴"
+      ],
+      "points": 9600,
+      "wildcard": [
+          0,
+          0,
+          0
+      ]
+  },
+  "Neha": {
+      "last_5_results": [
+          "⚪",
+          "⚪",
+          "⚪",
+          "⚪",
+          "🔴"
+      ],
+      "points": 0,
+      "wildcard": [
+          0,
+          0,
+          0
+      ]
+  },
+  "Praveen": {
+      "last_5_results": [
+          "⚪",
+          "⚪",
+          "🔴",
+          "⚪",
+          "🔴"
+      ],
+      "points": 6300,
+      "wildcard": [
+          0,
+          0,
+          0
+      ]
+  },
+  "Rakesh": {
+      "last_5_results": [
+          "🔴",
+          "🔴",
+          "🔴",
+          "⚪",
+          "⚪"
+      ],
+      "points": 440,
+      "wildcard": [
+          0,
+          0,
+          0
+      ]
+  },
+  "Sai": {
+      "last_5_results": [
+          "🟢",
+          "🔴",
+          "🔴",
+          "🟢",
+          "🔴"
+      ],
+      "points": 10608,
+      "wildcard": [
+          0,
+          0,
+          0
+      ]
+  },
+  "Sunil": {
+      "last_5_results": [
+          "🟢",
+          "🔴",
+          "⚪",
+          "⚪",
+          "🟢"
+      ],
+      "points": 18800,
+      "wildcard": [
+          0,
+          0,
+          0
+      ]
+  },
+  "Vaibhav": {
+      "last_5_results": [
+          "🔴",
+          "⚪",
+          "🔴",
+          "🟢",
+          "🔴"
+      ],
+      "points": 11320,
+      "wildcard": [
+          0,
+          0,
+          0
+      ]
+  },
+  "Vinay": {
+      "last_5_results": [
+          "⚪",
+          "🔴",
+          "⚪",
+          "🔴",
+          "🔴"
+      ],
+      "points": 5800,
+      "wildcard": [
+          0,
+          0,
+          0
+      ]
+  },
+  "Anandh": {
+      "last_5_results": [
+          "⚪",
+          "⚪",
+          "🔴",
+          "⚪",
+          "⚪"
+      ],
+      "points": 3100,
+      "wildcard": [
+          0,
+          0,
+          0
+      ]
+  },
+  "Archana": {
+      "last_5_results": [
+          "⚪",
+          "⚪",
+          "⚪",
+          "⚪",
+          "⚪"
+      ],
+      "points": 0,
+      "wildcard": [
+          0,
+          0,
+          0
+      ]
+  },
+  "Biswabarenya": {
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