Delete populate.py

populate.py

@@ -1,117 +0,0 @@
-import json
-import os
-
-import pandas as pd
-
-from src.display.formatting import has_no_nan_values, make_clickable_model
-from src.display.utils import AutoEvalColumn, EvalQueueColumn, ModelType, Precision, WeightType
-from src.leaderboard.read_evals import get_raw_eval_results
-from src.about import Tasks
-
-
-def load_csv_results():
-    """Load results from main-results.csv file"""
-    csv_path = "main-results.csv"
-    if not os.path.exists(csv_path):
-        return []
-    
-    df = pd.read_csv(csv_path)
-    results = []
-    
-    for _, row in df.iterrows():
-        # Parse parameters - handle different formats
-        param_str = str(row['Param'])
-        if 'activated' in param_str:
-            # Extract the activated parameter count (e.g., "2.8B activated (16B total)")
-            param_value = float(param_str.split('B')[0])
-        elif 'B' in param_str:
-            # Simple format (e.g., "9B")
-            param_value = float(param_str.replace('B', ''))
-        else:
-            param_value = 0
-            
-        # Convert CSV data to the format expected by the leaderboard
-        data_dict = {
-            AutoEvalColumn.model.name: make_clickable_model(row['Model']),
-            AutoEvalColumn.average.name: row['ACC'],  # Using ACC as the average score
-            AutoEvalColumn.params.name: param_value,
-            AutoEvalColumn.license.name: "Open Source" if row['Open Source?'] == 'Yes' else "Proprietary",
-            AutoEvalColumn.model_type.name: ModelType.FT.value.name,  # Default to fine-tuned
-            AutoEvalColumn.precision.name: Precision.float16.value.name,  # Default precision
-            AutoEvalColumn.weight_type.name: WeightType.Original.value.name,
-            AutoEvalColumn.architecture.name: "Unknown",
-            AutoEvalColumn.still_on_hub.name: True,
-            AutoEvalColumn.revision.name: "",
-            AutoEvalColumn.likes.name: 0,
-            AutoEvalColumn.model_type_symbol.name: ModelType.FT.value.symbol,
-        }
-        
-        # Add task-specific scores (required by the leaderboard)
-        for task in Tasks:
-            data_dict[task.name] = row['ACC']  # Use the same ACC score for all tasks
-            
-        results.append(data_dict)
-    
-    return results
-
-
-def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
-    """Creates a dataframe from all the individual experiment results"""
-    raw_data = get_raw_eval_results(results_path, requests_path)
-    all_data_json = [v.to_dict() for v in raw_data]
-    
-    # If no JSON data found, try loading from CSV
-    if not all_data_json:
-        all_data_json = load_csv_results()
-    
-    if not all_data_json:
-        # Return empty dataframe if no data found
-        return pd.DataFrame(columns=cols)
-
-    df = pd.DataFrame.from_records(all_data_json)
-    
-    # Only include columns that exist in the dataframe
-    existing_cols = [col for col in cols if col in df.columns]
-    
-    df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
-    df = df[existing_cols].round(decimals=2)
-
-    # filter out if any of the benchmarks have not been produced
-    df = df[has_no_nan_values(df, benchmark_cols)]
-    return df
-
-
-def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
-    """Creates the different dataframes for the evaluation queues requestes"""
-    entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")]
-    all_evals = []
-
-    for entry in entries:
-        if ".json" in entry:
-            file_path = os.path.join(save_path, entry)
-            with open(file_path) as fp:
-                data = json.load(fp)
-
-            data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
-            data[EvalQueueColumn.revision.name] = data.get("revision", "main")
-
-            all_evals.append(data)
-        elif ".md" not in entry:
-            # this is a folder
-            sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if os.path.isfile(e) and not e.startswith(".")]
-            for sub_entry in sub_entries:
-                file_path = os.path.join(save_path, entry, sub_entry)
-                with open(file_path) as fp:
-                    data = json.load(fp)
-
-                data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
-                data[EvalQueueColumn.revision.name] = data.get("revision", "main")
-                all_evals.append(data)
-
-    pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]]
-    running_list = [e for e in all_evals if e["status"] == "RUNNING"]
-    finished_list = [e for e in all_evals if e["status"].startswith("FINISHED") or e["status"] == "PENDING_NEW_EVAL"]
-    df_pending = pd.DataFrame.from_records(pending_list, columns=cols)
-    df_running = pd.DataFrame.from_records(running_list, columns=cols)
-    df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
-    return df_finished[cols], df_running[cols], df_pending[cols]