update populate

src/populate.py

@@ -1,33 +1,60 @@
-import json
-import os
+# src/populate.py
 
+import os
 import pandas as pd
+import json
 
-from src.display.formatting import has_no_nan_values, make_clickable_model
-from src.display.utils import AutoEvalColumn, EvalQueueColumn
-from src.leaderboard.read_evals import get_raw_eval_results
-
+from src.display.utils import COLUMNS, EVAL_COLS
 
 def get_leaderboard_df(eval_results_path, eval_requests_path, cols, benchmark_cols):
-    # Load evaluation results into a DataFrame
-    df = load_evaluation_results(eval_results_path)
-
-    # Check if the DataFrame is empty
-    if df.empty:
-        print("No evaluation results found. The leaderboard is currently empty.")
-        # Create an empty DataFrame with the required columns
-        df = pd.DataFrame(columns=cols)
-        return df
-
-    # Proceed to sort the DataFrame by 'average' if it's not empty
-    df = df.sort_values(by=['average'], ascending=False)
+    # Initialize an empty DataFrame
+    df = pd.DataFrame(columns=cols)
+
+    # Load evaluation results from JSON files
+    if os.path.exists(eval_results_path):
+        result_files = [os.path.join(eval_results_path, f) for f in os.listdir(eval_results_path) if f.endswith('.json')]
+        data_list = []
+        for file in result_files:
+            with open(file, 'r') as f:
+                data = json.load(f)
+                # Flatten the JSON structure if needed
+                flattened_data = {}
+                flattened_data.update(data.get('config', {}))
+                flattened_data.update(data.get('results', {}))
+                data_list.append(flattened_data)
+        if data_list:
+            df = pd.DataFrame(data_list)
+
+    # Ensure DataFrame has all columns
+    for col in cols:
+        if col not in df.columns:
+            df[col] = None
+
+    # Sort by 'average' column if it exists
+    if 'average' in df.columns:
+        df = df.sort_values(by=['average'], ascending=False)
 
     return df
 
-
-def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
-    """Creates the different dataframes for the evaluation queues requests"""
-    # Since evaluations are performed immediately, the queues will be empty
-    # We'll return empty DataFrames for compatibility
-    df_empty = pd.DataFrame(columns=cols)
-    return df_empty, df_empty, df_empty
+def get_evaluation_queue_df(eval_requests_path, eval_cols):
+    # Initialize empty DataFrames
+    finished_df = pd.DataFrame(columns=eval_cols)
+    running_df = pd.DataFrame(columns=eval_cols)
+    pending_df = pd.DataFrame(columns=eval_cols)
+
+    # Load evaluation requests from JSON files
+    if os.path.exists(eval_requests_path):
+        request_files = [os.path.join(eval_requests_path, f) for f in os.listdir(eval_requests_path) if f.endswith('.json')]
+        data_list = []
+        for file in request_files:
+            with open(file, 'r') as f:
+                data = json.load(f)
+                data_list.append(data)
+        if data_list:
+            df = pd.DataFrame(data_list)
+            # Split DataFrame based on status
+            finished_df = df[df['status'] == 'finished']
+            running_df = df[df['status'] == 'running']
+            pending_df = df[df['status'] == 'pending']
+
+    return finished_df, running_df, pending_df