Update app.py

app.py

@@ -131,16 +131,12 @@ def update_leaderboard_dataset_parallel(hivex_env, path):
         row = {}
         row["User"] = user_id
         row["Model"] = model_id
-        # accuracy = parse_metrics_accuracy(meta)
-        # mean_reward, std_reward = parse_rewards(accuracy)
-        # mean_reward = mean_reward if not pd.isna(mean_reward) else 0
-        # std_reward = std_reward if not pd.isna(std_reward) else 0
-        # row["Results"] = mean_reward - std_reward
-        # row["Mean Reward"] = mean_reward
-        # row["Std Reward"] = std_reward
-        results = meta["model-index"][0]["results"][0]["metrics"]
-
-        for result in results:
+        results = meta["model-index"][0]["results"][0]
+        row["Task"] = results["task"]["task-id"]
+
+        results_metrics = results["metrics"]
+
+        for result in results_metrics:
             row[result["name"]] = float(result["value"].split("+/-")[0].strip())
 
         return row
@@ -172,21 +168,25 @@ def run_update_dataset():
     )
 
 
-def get_data(rl_env, path) -> pd.DataFrame:
+def get_data(rl_env, task, path) -> pd.DataFrame:
     """
-    Get data from rl_env
-    :return: data as a pandas DataFrame
+    Get data from rl_env and filter by the given task.
+    :return: filtered data as a pandas DataFrame
     """
     csv_path = path + "/" + rl_env + ".csv"
     data = pd.read_csv(csv_path)
 
-    for index, row in data.iterrows():
+    # Filter the data to only include rows where the "Task" column matches the given task
+    filtered_data = data[data["Task"] == task]
+
+    # Convert User and Model columns to clickable links
+    for index, row in filtered_data.iterrows():
         user_id = row["User"]
-        data.loc[index, "User"] = make_clickable_user(user_id)
+        filtered_data.loc[index, "User"] = make_clickable_user(user_id)
         model_id = row["Model"]
-        data.loc[index, "Model"] = make_clickable_model(model_id)
+        filtered_data.loc[index, "Model"] = make_clickable_model(model_id)
 
-    return data
+    return filtered_data
 
 
 def get_data_no_html(rl_env, path) -> pd.DataFrame:
@@ -221,7 +221,7 @@ with block:
                     with gr.TabItem(f"Task {j}"):
                         with gr.Row():
                             # gr_dataframe = gr.components.Dataframe(value=get_data(hivex_env["hivex_env"], path_), headers=["User", "Model", "Cumulative Reward"], datatype=["markdown", "markdown", "number"],  row_count=(100, 'fixed'))
-                            gr_dataframe = gr.components.Dataframe(value=get_data(hivex_env["hivex_env"], path_), headers=["Ranking", "User", "Model"], datatype=["number", "markdown", "markdown"], row_count=(100, 'fixed'))
+                            gr_dataframe = gr.components.Dataframe(value=get_data(hivex_env["hivex_env"], j,path_), headers=["Ranking", "User", "Model"], datatype=["number", "markdown", "markdown"], row_count=(100, 'fixed'))
                  
 
 scheduler = BackgroundScheduler()