updated the mech requests top three most accurate tools only for pearl agents

app.py

@@ -164,7 +164,15 @@ def load_all_data():
         repo_type="dataset",
     )
     df8 = pd.read_parquet(errors_by_mech)
-    return df1, df2, df3, df4, df5, df6, df7, df8
+
+    # Read pearl_agents.parquet
+    pearl_agents_df = hf_hub_download(
+        repo_id="valory/Olas-predict-dataset",
+        filename="pearl_agents.parquet",
+        repo_type="dataset",
+    )
+    df9 = pd.read_parquet(pearl_agents_df)
+    return df1, df2, df3, df4, df5, df6, df7, df8, df9
 
 
 def prepare_data():
@@ -180,6 +188,7 @@ def prepare_data():
         winning_df,
         daily_mech_requests,
         errors_by_mech,
+        pearl_agents_df,
     ) = load_all_data()
     print(trades_df.info())
 
@@ -213,6 +222,7 @@ def prepare_data():
         winning_df,
         daily_mech_requests,
         errors_by_mech,
+        pearl_agents_df,
     )
 
 
@@ -225,6 +235,7 @@ def prepare_data():
     winning_df,
     daily_mech_requests,
     errors_by_mech,
+    pearl_agents_df,
 ) = prepare_data()
 trades_df = trades_df.sort_values(by="creation_timestamp", ascending=True)
 unknown_trades = unknown_trades.sort_values(by="creation_timestamp", ascending=True)
@@ -539,12 +550,13 @@ with demo:
 
             with gr.Row():
                 gr.Markdown(
-                    "# Mech requests percentage of the top three tools from the daily total"
+                    "# Mech requests percentage of the top three tools from the daily total (only for Pearl Agents)"
                 )
             with gr.Row():
                 _ = plot_mech_requests_topthree_tools(
                     daily_mech_requests=daily_mech_requests,
                     tools_accuracy_info=tools_accuracy_info,
+                    pearl_agents=pearl_agents_df,
                     top=3,
                 )
 

tabs/tool_accuracy.py

@@ -135,19 +135,26 @@ def plot_tools_weighted_accuracy_rotated_graph(
 
 
 def plot_mech_requests_topthree_tools(
-    daily_mech_requests: pd.DataFrame, tools_accuracy_info: pd.DataFrame, top: int
+    daily_mech_requests: pd.DataFrame,
+    tools_accuracy_info: pd.DataFrame,
+    pearl_agents: pd.DataFrame,
+    top: int,
 ):
-    """Function to plot the percentage of mech requests from the top three tools"""
+    """Function to plot the percentage of mech requests from the top three tools only for pearl agents"""
     # Get the top three tools
     top_tools = tools_accuracy_info.sort_values(
         by="tool_accuracy", ascending=False
     ).head(top)
     top_tools = top_tools.tool.tolist()
+    # Get the list of unique addresses from the daa_pearl_df
+    unique_addresses = pearl_agents["safe_address"].unique()
+    # Filter the weekly_roi_df to include only those addresses
+    daily_mech_requests_local_copy = daily_mech_requests[
+        daily_mech_requests["trader_address"].isin(unique_addresses)
+    ].copy()
+
     # Filter the daily mech requests for the top three tools
-    daily_mech_requests_local_copy = daily_mech_requests.copy()
-    daily_mech_requests_local_copy = daily_mech_requests_local_copy[
-        daily_mech_requests_local_copy["market_creator"] == "all"
-    ]
+
     # Get the daily total of mech requests no matter the tool
     total_daily_mech_requests = (
         daily_mech_requests_local_copy.groupby(["request_date"])