improving errors by mech layout

tabs/error.py

@@ -130,31 +130,26 @@ def plot_week_error_data_by_market(error_df: pd.DataFrame, week: str) -> gr.Plot
 
 def plot_weekly_errors_by_mech(errors_by_mech: pd.DataFrame) -> gr.Plot:
     """Function to plot the weekly errors by mech address"""
-    unique_mech_addresses = errors_by_mech["mech_address"].unique()
-
-    for mech_address in unique_mech_addresses:
-        # Filter the data for the current mech_address
-        filtered_data = errors_by_mech[errors_by_mech["mech_address"] == mech_address]
-
-        # Create the bar chart for the filtered data
-        fig = px.bar(
-            filtered_data,
-            x="request_month_year_week",
-            y="requests",
-            color="error_cat",
-            barmode="group",
-            color_discrete_sequence=["green", "orange"],
-            title=f"Error distribution for Mech Address: {mech_address}",
-            labels={
-                "requestmonthyearweek": "Month-Year-Week",
-                "requests": "Number of Requests",
-                "error_cat": "Error Category",
-                "errors_percentage": "Percentage",
-            },
-            text="errors_percentage",
-            height=HEIGHT,
-            width=WIDTH,
-        )
+    fig = px.bar(
+        errors_by_mech,
+        x="request_month_year_week",
+        y="requests",
+        color="error_cat",
+        barmode="group",
+        color_discrete_sequence=["green", "orange"],
+        labels={
+            "requestmonthyearweek": "Month-Year-Week",
+            "requests": "Number of Requests",
+            "error_cat": "Error Category",
+            "errors_percentage": "Percentage",
+        },
+        text="errors_percentage",
+        height=600,
+        width=800,
+        facet_col="mech_address",
+        facet_col_spacing=0.5,
+        facet_col_wrap=1,
+    )
     fig.update_traces(
         texttemplate="%{y} (%{text:.2f}%)",
         textposition="outside",