From 80ef59af5e28b69f7f1b7d8ae4e2e098b6a457f0 Mon Sep 17 00:00:00 2001
From: John Gatward <John.Gatward@sainsburys.co.uk>
Date: Fri, 20 Mar 2026 12:36:59 +0000
Subject: [PATCH] use env for data.csv

---
 src/app.py | 31 +++++++++++++++++++++----------
 1 file changed, 21 insertions(+), 10 deletions(-)

diff --git a/src/app.py b/src/app.py
index 26ae5fc..7af6772 100644
--- a/src/app.py
+++ b/src/app.py
@@ -1,3 +1,4 @@
+import os
 from flask import Flask, render_template
 import pandas as pd
 import plotly.express as px
@@ -17,6 +18,7 @@ app = Flask(__name__)
 # Data loading
 # ---------------------------------------------------------------------------
 
+
 def get_data_frame(filename):
     df = pd.read_csv(filename)
     df["Date"] = pd.to_datetime(df["Date"], dayfirst=True)
@@ -27,7 +29,8 @@ def get_data_frame(filename):
 def build_hovertext(df, attendance_columns):
     present = [c for c in attendance_columns if c in df.columns]
     return df[present].apply(
-        lambda row: ", ".join(p for p in present if row[p] == 1) or "No attendance",
+        lambda row: ", ".join(
+            p for p in present if row[p] == 1) or "No attendance",
         axis=1,
     )
 
@@ -45,7 +48,8 @@ def generate_position_trend(df):
     df = df.copy()
     df["Date_ordinal"] = df["Date"].map(pd.Timestamp.toordinal)
     df["Relative Percentile"] = df["Relative Position"] * 100
-    df["Rolling Avg (5)"] = df["Relative Percentile"].rolling(5, min_periods=1).mean()
+    df["Rolling Avg (5)"] = df["Relative Percentile"].rolling(
+        5, min_periods=1).mean()
     df["Attendees"] = build_hovertext(df, constants.PLAYER_NAME_COLUMNS)
 
     X = sm.add_constant(df["Date_ordinal"])
@@ -61,7 +65,8 @@ def generate_position_trend(df):
     if slope < 0:
         predicted_ordinal = (target_percentile - intercept) / slope
 
-    end_ord = max(max_ord, predicted_ordinal) if predicted_ordinal and predicted_ordinal > max_ord else max_ord
+    end_ord = max(
+        max_ord, predicted_ordinal) if predicted_ordinal and predicted_ordinal > max_ord else max_ord
 
     extended_ords = np.linspace(min_ord, end_ord, 200)
     extended_percentile = intercept + slope * extended_ords
@@ -120,9 +125,11 @@ def generate_position_trend(df):
     fig.update_layout(
         title="Relative Position Over Time",
         xaxis_title="Date",
-        yaxis=dict(title="Relative Position Percentile (lower is better)", range=[0, 100], ticksuffix="th"),
+        yaxis=dict(title="Relative Position Percentile (lower is better)", range=[
+                   0, 100], ticksuffix="th"),
         template="plotly_white",
-        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
+        legend=dict(orientation="h", yanchor="bottom",
+                    y=1.02, xanchor="right", x=1),
         hovermode="x unified",
         margin=dict(t=60, b=40),
     )
@@ -146,12 +153,14 @@ def generate_player_impact(df):
         n = len(attended)
         if n >= MIN_APPEARANCES:
             percentile = attended["Relative Position"].mean() * 100
-            rows.append({"Player": name, "Relative Percentile": round(percentile, 1), "Games": n})
+            rows.append(
+                {"Player": name, "Relative Percentile": round(percentile, 1), "Games": n})
 
     if not rows:
         return go.Figure()
 
-    impact_df = pd.DataFrame(rows).sort_values("Relative Percentile", ascending=True)
+    impact_df = pd.DataFrame(rows).sort_values(
+        "Relative Percentile", ascending=True)
     colors = [
         "#16a34a" if p <= overall_percentile else "#dc2626"
         for p in impact_df["Relative Percentile"]
@@ -176,7 +185,8 @@ def generate_player_impact(df):
         x=overall_percentile,
         line_dash="dot",
         line_color="#6b7280",
-        annotation_text=f"Overall avg ({constants.ordinal(round(overall_percentile))})",
+        annotation_text=f"Overall avg ({constants.ordinal(
+            round(overall_percentile))})",
         annotation_position="top right",
     )
 
@@ -330,7 +340,8 @@ def generate_weekly_attendance_calendar(df):
             y_labels.append(f"{year} · W{start}-{end}")
 
     calendar["RowLabel"] = calendar.apply(
-        lambda r: f"{int(r['Year'])} · W{(int(r['Block']) - 1) * 13 + 1}-{min(int(r['Block']) * 13, 53)}",
+        lambda r: f"{int(r['Year'])} · W{(int(r['Block']) - 1)
+                                         * 13 + 1}-{min(int(r['Block']) * 13, 53)}",
         axis=1,
     )
 
@@ -389,7 +400,7 @@ def generate_visualisations(df):
 
 @app.route("/")
 def index():
-    df = get_data_frame("data.csv")
+    df = get_data_frame(os.environ.get("DATA_CSV_PATH", "data.csv"))
     stats, highlights = generate_stats(df)
     player_table = generate_player_table(df)
     plots = generate_visualisations(df)