diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000..9165e0c
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,11 @@
+.git
+.venv
+env
+venv
+__pycache__
+*.pyc
+.DS_Store
+images
+*.db
+*.csv
+*.db.bak
diff --git a/.gitignore b/.gitignore
index 7aaac2c..3364e7a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,9 @@
# who the fuck forgets to add this smh
-
env/
-venv/
\ No newline at end of file
+venv/
+.venv/
+__pycache__/
+*.pyc
+.DS_Store
+*.db.bak
\ No newline at end of file
diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..6324d40
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.14
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..81f6fa0
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,14 @@
+FROM python:3.14-slim
+
+COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
+
+WORKDIR /app
+
+COPY pyproject.toml uv.lock ./
+RUN uv sync --frozen --no-dev
+
+COPY app.py import_csv.py ./
+
+EXPOSE 8000
+
+CMD ["uv", "run", "gunicorn", "app:app", "--bind", "0.0.0.0:8000", "--workers", "2", "--timeout", "120"]
diff --git a/app.py b/app.py
new file mode 100644
index 0000000..40397d0
--- /dev/null
+++ b/app.py
@@ -0,0 +1,591 @@
+import os
+import sqlite3
+import json
+from datetime import datetime
+from collections import defaultdict
+import csv
+import io
+from flask import Flask, render_template_string, request, redirect, url_for, flash
+
+DB_PATH = os.environ.get("WEAKNESS_DB_PATH", "doordash.db")
+
+# CPI-U annual average multipliers to 2026 dollars
+CPI_TO_2026 = {
+ "2019": 1.28, "2020": 1.25, "2021": 1.18, "2022": 1.10,
+ "2023": 1.06, "2024": 1.03, "2025": 1.01, "2026": 1.00,
+}
+
+app = Flask(__name__)
+app.secret_key = os.environ.get("SECRET_KEY", os.urandom(32))
+app.config["MAX_CONTENT_LENGTH"] = 16 * 1024 * 1024 # 16MB max upload
+
+EXPECTED_HEADER = [
+ "ITEM", "CATEGORY", "STORE_NAME", "UNIT_PRICE", "QUANTITY",
+ "SUBTOTAL", "CREATED_AT", "DELIVERY_TIME", "DELIVERY_ADDRESS",
+]
+
+
+def compute_wrapped(subset_rows, inflation_adjust=False):
+ if not subset_rows:
+ return None
+ ss = defaultdict(float)
+ hc = defaultdict(int)
+ wc = defaultdict(int)
+ ms = defaultdict(float)
+ ic = defaultdict(int)
+ orders = set()
+ t_spent = 0.0
+ for r in subset_rows:
+ sub = float(r["SUBTOTAL"])
+ if inflation_adjust:
+ try:
+ yr = str(datetime.fromisoformat(r["CREATED_AT"]).year)
+ except (ValueError, TypeError):
+ yr = "2026"
+ sub *= CPI_TO_2026.get(yr, 1.0)
+ t_spent += sub
+ ss[r["STORE_NAME"]] += sub
+ ic[r["ITEM"]] += 1
+ try:
+ dt = datetime.fromisoformat(r["CREATED_AT"])
+ except (ValueError, TypeError):
+ continue
+ orders.add(r["CREATED_AT"])
+ hc[dt.hour] += 1
+ wc[dt.strftime("%A")] += 1
+ ms[dt.strftime("%Y-%m")] += sub
+
+ fs = max(ss.items(), key=lambda x: x[1])
+ ph = max(hc.items(), key=lambda x: x[1])
+ pd = max(wc.items(), key=lambda x: x[1])
+ bm = max(ms.items(), key=lambda x: x[1])
+ fi = max(ic.items(), key=lambda x: x[1])
+ avg = round(t_spent / len(orders), 2) if orders else 0
+ return {
+ "fav_store_name": fs[0],
+ "fav_store_total": f"${fs[1]:,.2f}",
+ "fav_store_pct": round(fs[1] / t_spent * 100, 1) if t_spent else 0,
+ "peak_hour_label": f"{ph[0]}:00",
+ "peak_hour_count": ph[1],
+ "peak_day_name": pd[0],
+ "biggest_month_label": datetime.strptime(bm[0], "%Y-%m").strftime("%B %Y"),
+ "biggest_month_total": f"${bm[1]:,.2f}",
+ "avg_per_order": f"${avg:,.2f}",
+ "fav_item_name": fi[0],
+ "fav_item_count": fi[1],
+ "total_spent": f"${t_spent:,.2f}",
+ "total_items": len(subset_rows),
+ "total_orders": len(orders),
+ }
+
+
+def render_dashboard(rows):
+ import numpy as np
+
+ total_spent = sum(float(r["SUBTOTAL"]) for r in rows)
+ total_orders_set = set()
+ store_spend = defaultdict(float)
+ month_spend = defaultdict(float)
+ hour_counts = defaultdict(int)
+ weekday_counts = defaultdict(int)
+ week_grid = [[0] * 7 for _ in range(53)]
+
+ for r in rows:
+ store_spend[r["STORE_NAME"]] += float(r["SUBTOTAL"])
+ try:
+ dt = datetime.fromisoformat(r["CREATED_AT"])
+ except (ValueError, TypeError):
+ continue
+ total_orders_set.add(r["CREATED_AT"])
+ month_key = dt.strftime("%Y-%m")
+ month_spend[month_key] += float(r["SUBTOTAL"])
+ hour_counts[dt.hour] += 1
+ weekday_counts[dt.strftime("%A")] += 1
+ week = min(dt.isocalendar().week - 1, 52)
+ day = dt.isocalendar().weekday - 1
+ week_grid[week][day] += float(r["SUBTOTAL"])
+
+ year_data = defaultdict(list)
+ for r in rows:
+ try:
+ dt = datetime.fromisoformat(r["CREATED_AT"])
+ year_data[str(dt.year)].append(r)
+ except (ValueError, TypeError):
+ pass
+
+ wrapped_by_year = {"All Time": compute_wrapped(rows)}
+ wrapped_by_year_adj = {"All Time": compute_wrapped(rows, inflation_adjust=True)}
+ for year in sorted(year_data.keys()):
+ wrapped_by_year[year] = compute_wrapped(year_data[year])
+ wrapped_by_year_adj[year] = compute_wrapped(year_data[year], inflation_adjust=True)
+
+ top_stores = sorted(store_spend.items(), key=lambda x: x[1], reverse=True)[:15]
+ store_names = [s[0] for s in top_stores]
+ store_totals = [round(s[1], 2) for s in top_stores]
+
+ sorted_months = sorted(month_spend.keys())
+ month_totals = [round(month_spend[m], 2) for m in sorted_months]
+
+ hour_labels = list(range(24))
+ hour_values = [hour_counts.get(h, 0) for h in hour_labels]
+ hour_labels_str = [f"{h}:00" for h in hour_labels]
+
+ day_order = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
+ weekday_values = [weekday_counts.get(d, 0) for d in day_order]
+
+ grid = np.array(week_grid)
+ heatmap_data = np.rot90(grid).tolist()
+ heatmap_days = ["Sun", "Sat", "Fri", "Thu", "Wed", "Tue", "Mon"]
+
+ return render_template_string(
+ TEMPLATE,
+ total_spent=f"${total_spent:,.2f}",
+ total_items=len(rows),
+ total_orders=len(total_orders_set),
+ store_names=json.dumps(store_names),
+ store_totals=json.dumps(store_totals),
+ month_labels=json.dumps(sorted_months),
+ month_totals=json.dumps(month_totals),
+ hour_labels=json.dumps(hour_labels_str),
+ hour_values=json.dumps(hour_values),
+ day_order=json.dumps(day_order),
+ weekday_values=json.dumps(weekday_values),
+ heatmap_data=json.dumps(heatmap_data),
+ heatmap_days=json.dumps(heatmap_days),
+ wrapped_data=json.dumps(wrapped_by_year),
+ wrapped_data_adj=json.dumps(wrapped_by_year_adj),
+ )
+
+
+def parse_csv_to_rows(text):
+ reader = csv.reader(io.StringIO(text))
+ header = [h.strip().upper() for h in next(reader)]
+ if header != EXPECTED_HEADER:
+ return None, "Invalid CSV header"
+ rows = []
+ for row in reader:
+ if len(row) != 9:
+ continue
+ rows.append({
+ "ITEM": row[0], "CATEGORY": row[1], "STORE_NAME": row[2],
+ "UNIT_PRICE": row[3], "QUANTITY": row[4], "SUBTOTAL": row[5],
+ "CREATED_AT": row[6], "DELIVERY_TIME": row[7], "DELIVERY_ADDRESS": row[8],
+ })
+ return rows, None
+
+
+@app.route("/")
+def home():
+ return render_template_string(HOME_TEMPLATE)
+
+
+@app.route("/dashboard")
+def dashboard():
+ conn = sqlite3.connect(DB_PATH)
+ conn.row_factory = sqlite3.Row
+ rows = conn.execute(
+ "SELECT ITEM, STORE_NAME, UNIT_PRICE, QUANTITY, SUBTOTAL, CREATED_AT FROM doordash"
+ ).fetchall()
+ conn.close()
+ return render_dashboard(rows)
+
+
+@app.route("/upload", methods=["GET", "POST"])
+def upload():
+ if request.method == "POST":
+ file = request.files.get("csv_file")
+ if not file or not file.filename.endswith(".csv"):
+ flash("Please upload a .csv file", "error")
+ return redirect(url_for("home"))
+
+ text = file.stream.read().decode("utf-8")
+ rows, err = parse_csv_to_rows(text)
+ if err:
+ flash(err, "error")
+ return redirect(url_for("home"))
+
+ return render_dashboard(rows)
+
+ return redirect(url_for("home"))
+
+
+HOME_TEMPLATE = """
+
+
+ Moments of Weakness
+
+
+
+
+
+
Moments of Weakness
+
See exactly how much you've been spending on delivery
+
+ {% with messages = get_flashed_messages(with_categories=true) %}
+ {% for cat, msg in messages %}
+
{{ msg }}
+ {% endfor %}
+ {% endwith %}
+
+
+
+
Upload Your Data
+
Drop your DoorDash CSV export to see your dashboard. Nothing is stored.
+
+
+
+
or
+
+
+
View Saved Data
+
Browse the dashboard with previously imported order history.
+
Open Dashboard
+
+
+
+
+
+
+
+"""
+
+TEMPLATE = """
+
+
+ Weakness Dashboard
+
+
+
+
+
+
+
Moments of Weakness
+
Home
+
+ DoorDash spending dashboard
+ {% with messages = get_flashed_messages(with_categories=true) %}
+ {% for cat, msg in messages %}
+ {{ msg }}
+ {% endfor %}
+ {% endwith %}
+
+
+
+
{{ total_spent }}
+
Total Spent
+
+
+
{{ total_items }}
+
Items Ordered
+
+
+
{{ total_orders }}
+
Unique Orders
+
+
+
+
+
+
+
+
+
+
+"""
+
+if __name__ == "__main__":
+ import argparse
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("-p", "--port", type=int, default=5000)
+ args = parser.parse_args()
+ app.run(debug=True, port=args.port)
diff --git a/import_csv.py b/import_csv.py
new file mode 100644
index 0000000..bcc74e0
--- /dev/null
+++ b/import_csv.py
@@ -0,0 +1,84 @@
+#!/usr/bin/env python3
+"""Import a DoorDash CSV export into doordash.db."""
+
+import csv
+import sqlite3
+import sys
+from pathlib import Path
+
+DB_PATH = Path(__file__).parent / "doordash.db"
+
+CREATE_TABLE = """CREATE TABLE IF NOT EXISTS "doordash"(
+ "ITEM" TEXT, "CATEGORY" TEXT, "STORE_NAME" TEXT, "UNIT_PRICE" TEXT,
+ "QUANTITY" TEXT, "SUBTOTAL" TEXT, "CREATED_AT" TEXT, "DELIVERY_TIME" TEXT,
+ "DELIVERY_ADDRESS" TEXT
+);"""
+
+EXPECTED_HEADER = [
+ "ITEM", "CATEGORY", "STORE_NAME", "UNIT_PRICE", "QUANTITY",
+ "SUBTOTAL", "CREATED_AT", "DELIVERY_TIME", "DELIVERY_ADDRESS",
+]
+
+
+def import_csv(csv_path: str):
+ path = Path(csv_path)
+ if not path.exists():
+ print(f"Error: {csv_path} not found")
+ sys.exit(1)
+
+ conn = sqlite3.connect(DB_PATH)
+ c = conn.cursor()
+ c.execute(CREATE_TABLE)
+
+ # Get existing row count for reporting
+ before_count = c.execute("SELECT COUNT(*) FROM doordash").fetchone()[0]
+
+ # Check for duplicates using CREATED_AT + ITEM + STORE_NAME
+ existing = set()
+ for row in c.execute("SELECT ITEM, STORE_NAME, CREATED_AT FROM doordash"):
+ existing.add((row[0], row[1], row[2]))
+
+ imported = 0
+ skipped = 0
+
+ with open(path, newline="") as f:
+ reader = csv.reader(f)
+ header = next(reader)
+
+ # Validate header
+ header_upper = [h.strip().upper() for h in header]
+ if header_upper != EXPECTED_HEADER:
+ print(f"Error: unexpected CSV header.\nExpected: {EXPECTED_HEADER}\nGot: {header_upper}")
+ sys.exit(1)
+
+ for row in reader:
+ if len(row) != 9:
+ print(f"Warning: skipping row with {len(row)} columns: {row[:3]}...")
+ skipped += 1
+ continue
+
+ key = (row[0], row[2], row[6]) # ITEM, STORE_NAME, CREATED_AT
+ if key in existing:
+ skipped += 1
+ continue
+
+ c.execute("INSERT INTO doordash VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)", row)
+ existing.add(key)
+ imported += 1
+
+ conn.commit()
+ after_count = c.execute("SELECT COUNT(*) FROM doordash").fetchone()[0]
+ conn.close()
+
+ print(f"Done! Imported {imported} rows, skipped {skipped} duplicates.")
+ print(f"Total rows: {before_count} -> {after_count}")
+
+
+if __name__ == "__main__":
+ if len(sys.argv) < 2:
+ print(f"Usage: {sys.argv[0]} [csv_file2 ...]")
+ sys.exit(1)
+
+ for csv_file in sys.argv[1:]:
+ print(f"\nImporting {csv_file}...")
+ import_csv(csv_file)
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..e68d556
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,15 @@
+[project]
+name = "weakness"
+version = "0.1.0"
+description = "DoorDash spending dashboard"
+requires-python = ">=3.14"
+dependencies = [
+ "flask>=3.1.3",
+ "plotly>=6.6.0",
+ "numpy>=2.0",
+ "gunicorn>=25.1.0",
+]
+
+[project.scripts]
+weakness = "app:app.run"
+import-csv = "import_csv:main"
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new file mode 100644
index 0000000..174f30a
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,204 @@
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+revision = 2
+requires-python = ">=3.14"
+
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