fixing the mech requests from top three accurate tools

app.py

@@ -165,13 +165,13 @@ def load_all_data():
     )
     df8 = pd.read_parquet(errors_by_mech)
 
-    # Read pearl_agents.parquet
-    pearl_agents_df = hf_hub_download(
+    # Read daily_mech_requests_by_pearl_agents.parquet
+    daily_mech_requests_by_pearl_agents = hf_hub_download(
         repo_id="valory/Olas-predict-dataset",
-        filename="pearl_agents.parquet",
+        filename="daily_mech_requests_by_pearl_agents.parquet",
         repo_type="dataset",
     )
-    df9 = pd.read_parquet(pearl_agents_df)
+    df9 = pd.read_parquet(daily_mech_requests_by_pearl_agents)
     return df1, df2, df3, df4, df5, df6, df7, df8, df9
 
 
@@ -188,7 +188,7 @@ def prepare_data():
         winning_df,
         daily_mech_requests,
         errors_by_mech,
-        pearl_agents_df,
+        daily_mech_requests_by_pearl_agents,
     ) = load_all_data()
     print(trades_df.info())
 
@@ -222,7 +222,7 @@ def prepare_data():
         winning_df,
         daily_mech_requests,
         errors_by_mech,
-        pearl_agents_df,
+        daily_mech_requests_by_pearl_agents,
     )
 
 
@@ -235,7 +235,7 @@ def prepare_data():
     winning_df,
     daily_mech_requests,
     errors_by_mech,
-    pearl_agents_df,
+    daily_mech_request_by_pearl_agents,
 ) = prepare_data()
 trades_df = trades_df.sort_values(by="creation_timestamp", ascending=True)
 unknown_trades = unknown_trades.sort_values(by="creation_timestamp", ascending=True)
@@ -554,9 +554,8 @@ with demo:
                 )
             with gr.Row():
                 _ = plot_mech_requests_topthree_tools(
-                    daily_mech_requests=daily_mech_requests,
+                    daily_mech_requests=daily_mech_requests_by_pearl_agents,
                     tools_accuracy_info=tools_accuracy_info,
-                    pearl_agents=pearl_agents_df,
                     top=3,
                 )
 

tabs/tool_accuracy.py

@@ -135,9 +135,8 @@ def plot_tools_weighted_accuracy_rotated_graph(
 
 
 def plot_mech_requests_topthree_tools(
-    daily_mech_requests: pd.DataFrame,
+    daily_mech_requests_by_pearl_agents: 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 only for pearl agents"""
@@ -146,18 +145,12 @@ def plot_mech_requests_topthree_tools(
         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
 
     # Get the daily total of mech requests no matter the tool
     total_daily_mech_requests = (
-        daily_mech_requests_local_copy.groupby(["request_date"])
+        daily_mech_requests_by_pearl_agents.groupby(["request_date"])
         .agg({"total_mech_requests": "sum"})
         .reset_index()
     )
@@ -168,7 +161,7 @@ def plot_mech_requests_topthree_tools(
     )
     # Merge the total daily mech requests with the daily mech requests
     daily_mech_requests_local_copy = pd.merge(
-        daily_mech_requests_local_copy,
+        daily_mech_requests_by_pearl_agents,
         total_daily_mech_requests,
         on="request_date",
         how="left",