Jay-Rajput commited on
Commit
6debba1
·
1 Parent(s): 1356e88

updated leaderboard

Browse files
Files changed (2) hide show
  1. app.py +5 -4
  2. match_outcomes.json +5 -0
app.py CHANGED
@@ -388,7 +388,8 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match):
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  # Fetch latest predictions from the dataset repo
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  predictions = fetch_latest_predictions(match_id)
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- outcomes = load_data(OUTCOMES_JSON) # Load existing match outcomes
 
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  # Load existing match outcomes and user data from the test split
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  dataset = load_dataset("Jay-Rajput/DIS_IPL_Leads", split="train")
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  users = pd.DataFrame(dataset)
@@ -404,7 +405,7 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match):
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  outcomes.append({"match_id": match_id, "winning_team": winning_team, "man_of_the_match": man_of_the_match})
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  # Update user points based on prediction accuracy
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- for index, prediction in predictions.iterrows():
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  user_name = prediction['user_name']
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  predicted_winner = prediction['predicted_winner']
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  predicted_motm = prediction['predicted_motm']
@@ -419,11 +420,11 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match):
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  else:
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  users[user_name] -= 200 + bid_points # Penalty for wrong team prediction
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- save_match_outcomes(outcomes)
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  users.to_json(USERS_JSON)
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  # Convert the updated DataFrame back to a Hugging Face Dataset and push updates
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  updated_dataset = Dataset.from_pandas(users)
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  updated_dataset.push_to_hub("Jay-Rajput/DIS_IPL_Leads", split="train")
 
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  with st.sidebar:
@@ -434,7 +435,7 @@ with st.sidebar:
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  expander.success("Authenticated")
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  all_matches = load_data(MATCHES_JSON)
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- match_outcomes = load_data(OUTCOMES_JSON)
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  submitted_match_ids = [outcome["match_id"] for outcome in match_outcomes]
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  # Filter matches to those that do not have outcomes submitted yet
 
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  # Fetch latest predictions from the dataset repo
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  predictions = fetch_latest_predictions(match_id)
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+ outcomes = load_dataset("Jay-Rajput/DIS_IPL_Outcomes", split="train") # Load existing match outcomes
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+
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  # Load existing match outcomes and user data from the test split
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  dataset = load_dataset("Jay-Rajput/DIS_IPL_Leads", split="train")
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  users = pd.DataFrame(dataset)
 
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  outcomes.append({"match_id": match_id, "winning_team": winning_team, "man_of_the_match": man_of_the_match})
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  # Update user points based on prediction accuracy
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+ for idx, prediction in predictions.iterrows():
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  user_name = prediction['user_name']
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  predicted_winner = prediction['predicted_winner']
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  predicted_motm = prediction['predicted_motm']
 
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  else:
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  users[user_name] -= 200 + bid_points # Penalty for wrong team prediction
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  users.to_json(USERS_JSON)
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  # Convert the updated DataFrame back to a Hugging Face Dataset and push updates
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  updated_dataset = Dataset.from_pandas(users)
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  updated_dataset.push_to_hub("Jay-Rajput/DIS_IPL_Leads", split="train")
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+ outcomes.push_to_hub("Jay-Rajput/DIS_IPL_Outcomes", split="train")
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  with st.sidebar:
 
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  expander.success("Authenticated")
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  all_matches = load_data(MATCHES_JSON)
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+ match_outcomes = load_dataset("Jay-Rajput/DIS_IPL_Outcomes", split="train")
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  submitted_match_ids = [outcome["match_id"] for outcome in match_outcomes]
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  # Filter matches to those that do not have outcomes submitted yet
match_outcomes.json CHANGED
@@ -88,5 +88,10 @@
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  "match_id": "20240405_18",
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  "winning_team": "SRH",
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  "man_of_the_match": "Abhishek Sharma"
 
 
 
 
 
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  }
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  ]
 
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  "match_id": "20240405_18",
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  "winning_team": "SRH",
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  "man_of_the_match": "Abhishek Sharma"
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+ },
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+ {
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+ "match_id": "20240406_19",
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+ "winning_team": "RR",
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+ "man_of_the_match": "Jos Buttler"
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  }
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  ]