daily mech requests per market graphs

app.py

@@ -27,6 +27,7 @@ from tabs.metrics import (
 from tabs.tool_win import (
     integrated_plot_tool_winnings_overall_per_market_by_week,
     integrated_tool_winnings_by_tool_per_market,
+    get_daily_mech_requests,
 )
 
 from tabs.tool_accuracy import (
@@ -144,7 +145,16 @@ def load_all_data():
     )
 
     df6 = pd.read_parquet(winning_df)
-    return df1, df2, df3, df4, df5, df6
+
+    # daily_mech_requests.parquet
+    daily_mech_req_df = hf_hub_download(
+        repo_id="valory/Olas-predict-dataset",
+        filename="daily_mech_requests.parquet",
+        repo_type="dataset",
+    )
+
+    df7 = pd.read_parquet(daily_mech_req_df)
+    return df1, df2, df3, df4, df5, df6, df7
 
 
 def prepare_data():
@@ -158,6 +168,7 @@ def prepare_data():
         invalid_trades,
         unknown_trades,
         winning_df,
+        daily_mech_requests,
     ) = load_all_data()
     print(trades_df.info())
 
@@ -186,6 +197,7 @@ def prepare_data():
         invalid_trades,
         unknown_trades,
         winning_df,
+        daily_mech_requests,
     )
 
 
@@ -196,6 +208,7 @@ def prepare_data():
     invalid_trades,
     unknown_trades,
     winning_df,
+    daily_mech_requests,
 ) = prepare_data()
 trades_df = trades_df.sort_values(by="creation_timestamp", ascending=True)
 unknown_trades = unknown_trades.sort_values(by="creation_timestamp", ascending=True)
@@ -440,7 +453,20 @@ with demo:
                 winning_selector
             with gr.Row():
                 winning_plot
+            with gr.Row():
+                gr.Markdown("# Daily mech requests by each tool in Pearl markets")
 
+            with gr.Row():
+                daily_pearl_requests_plot = get_daily_mech_requests(
+                    daily_mech_req_df=daily_mech_requests, market_creator="pearl"
+                )
+            with gr.Row():
+                gr.Markdown("# Daily mech requests by each tool in Quickstart markets")
+
+            with gr.Row():
+                daily_qs_requests_plot = get_daily_mech_requests(
+                    daily_mech_req_df=daily_mech_requests, market_creator="quickstart"
+                )
             with gr.Row():
                 gr.Markdown("# Winning performance by each tool")
 

tabs/tool_win.py

@@ -1,16 +1,30 @@
 import pandas as pd
 import gradio as gr
-from typing import List
+import numpy as np
 from tabs.metrics import tool_metric_choices
 import plotly.express as px
 
 
 HEIGHT = 600
 WIDTH = 1000
+tools_palette = {
+    "prediction-request-reasoning": "darkorchid",
+    "claude-prediction-offline": "rebeccapurple",
+    "prediction-request-reasoning-claude": "slateblue",
+    "prediction-request-rag-claude": "steelblue",
+    "prediction-online": "darkcyan",
+    "prediction-offline": "mediumaquamarine",
+    "claude-prediction-online": "mediumseagreen",
+    "prediction-online-sme": "yellowgreen",
+    "prediction-url-cot-claude": "gold",
+    "prediction-offline-sme": "orange",
+    "prediction-request-rag": "chocolate",
+}
 
 
 def prepare_tools(tools: pd.DataFrame) -> pd.DataFrame:
-    tools["request_time"] = pd.to_datetime(tools["request_time"])
+    tools["request_time"] = pd.to_datetime(tools["request_time"], utc=True)
+    tools["request_date"] = tools["request_time"].dt.date
     tools = tools.sort_values(by="request_time", ascending=True)
 
     tools["request_month_year_week"] = (
@@ -143,3 +157,57 @@ def integrated_tool_winnings_by_tool_per_market(
     fig.update_layout(width=WIDTH, height=HEIGHT)
     fig.update_xaxes(tickformat="%b %d\n%Y")
     return gr.Plot(value=fig)
+
+
+def get_daily_mech_requests(
+    daily_mech_req_df: pd.DataFrame, market_creator: str
+) -> gr.Plot:
+
+    if market_creator == "pearl":
+        daily_mech_req_per_tool = daily_mech_req_df.loc[
+            daily_mech_req_df["market_creator"] == "pearl"
+        ]
+    else:  # quickstart
+        daily_mech_req_per_tool = daily_mech_req_df.loc[
+            daily_mech_req_df["market_creator"] == "quickstart"
+        ]
+
+    daily_mech_req_per_tool = daily_mech_req_per_tool[
+        ["request_date", "tool", "total_mech_requests"]
+    ]
+    pivoted = daily_mech_req_per_tool.pivot(
+        index="request_date", columns="tool", values="total_mech_requests"
+    )
+
+    # Sort the columns for each row independently
+    sorted_values = np.sort(pivoted.values, axis=1)[
+        :, ::-1
+    ]  # sort and reverse (descending)
+    sorted_columns = np.argsort(pivoted.values, axis=1)[:, ::-1]  # get sorting indices
+
+    sorted_df = pd.DataFrame(
+        sorted_values,
+        index=pivoted.index,
+        columns=[
+            pivoted.columns[i] for i in sorted_columns[0]
+        ],  # use first row's order
+    )
+
+    sorted_long = sorted_df.reset_index().melt(
+        id_vars=["request_date"], var_name="tool", value_name="total_mech_requests"
+    )
+
+    fig = px.bar(
+        sorted_long,
+        x="request_date",
+        y="total_mech_requests",
+        color="tool",
+        color_discrete_map=tools_palette,
+    )
+    fig.update_layout(
+        xaxis_title="Day of the request",
+        yaxis_title="Total daily mech requests",
+        # legend=dict(yanchor="top", y=0.5),
+    )
+    fig.update_layout(width=WIDTH, height=HEIGHT)
+    return gr.Plot(value=fig)