271 lines
9.4 KiB
Plaintext
271 lines
9.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f60dab2a-a377-4761-8be3-69a3b8124ca6",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"import pdfplumber\n",
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"import re\n",
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"import json\n",
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"\n",
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"def parse_transaction_line(line):\n",
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" # More specific pattern that captures each component'\n",
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" pattern = r'^(\\d{2}/\\d{2})\\s+(.+?)\\s+(-?[\\d,]+\\.\\d{2})\\s+(-?[\\d,]+\\.\\d{2})$'\n",
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" match = re.match(pattern, line.strip())\n",
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" \n",
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" if match:\n",
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" date, description, amount, balance = match.groups()\n",
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" return {\n",
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" 'date': date,\n",
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" 'description': description.strip(),\n",
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" 'amount': amount,\n",
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" 'balance': balance\n",
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" }\n",
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" return None\n",
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"\n",
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"def parse_Credit_Card_transaction_line(line):\n",
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" # More specific pattern that captures each component'\n",
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" pattern = r'^(\\d{2}/\\d{2})\\s+(.+?)\\s+(-?[\\d,]+\\.\\d{2})$'\n",
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" match = re.match(pattern, line.strip())\n",
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" \n",
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" if match:\n",
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" date, description, amount = match.groups()\n",
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" return {\n",
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" 'date': date,\n",
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" 'description': description.strip(),\n",
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" 'amount': amount\n",
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" }\n",
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" return None\n",
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"\n",
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"# \n",
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"def extract_transactions_CA_from_pdf(pdf_path):\n",
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" transactions = []\n",
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" \n",
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" with pdfplumber.open(pdf_path) as pdf:\n",
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" for page in pdf.pages:\n",
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" text = page.extract_text()\n",
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" for line in text.split(\"\\n\"):\n",
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" parsed = parse_transaction_line(line)\n",
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" if parsed:\n",
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" transactions.append(parsed)\n",
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" return transactions\n",
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"\n",
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"def extract_transactions_CreditCard_from_pdf(pdf_path):\n",
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" transactions = []\n",
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" \n",
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" with pdfplumber.open(pdf_path) as pdf:\n",
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" for page in pdf.pages:\n",
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" text = page.extract_text()\n",
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" for line in text.split(\"\\n\"):\n",
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" parsed = parse_Credit_Card_transaction_line(line)\n",
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" if parsed:\n",
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" transactions.append(parsed)\n",
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" return transactions\n",
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"# print(transactions, len(transactions)) # check first 10 extracted lines\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "82c34eac-fc30-41d6-8325-77efc48d0dd8",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Load environment variables in a file called .env\n",
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"from dotenv import load_dotenv\n",
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"from openai import OpenAI\n",
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"import os\n",
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"\n",
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"load_dotenv(override=True)\n",
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"api_key = os.getenv('OPENAI_API_KEY')\n",
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"\n",
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"# Check the key\n",
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"\n",
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"if not api_key:\n",
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" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
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"elif not api_key.startswith(\"sk-proj-\"):\n",
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" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
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"elif api_key.strip() != api_key:\n",
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" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
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"else:\n",
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" print(\"API key found and looks good so far!\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 49,
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"id": "769ee512-75f5-480a-9407-f9c4cd46b679",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"# ---------- STEP 3: Build prompts ----------\n",
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"\n",
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"def build_prompts(transactions):\n",
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" system_prompt = \"\"\"\n",
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"You are a personal financial assistant.\n",
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"Your job is to analyze bank transactions, categorize each expense into categories such as:\n",
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"Food, Clothing, Rent, Utilities, Entertainment, Travel, Health, Miscellaneous, and Others.\n",
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"\n",
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"Your responsibilities:\n",
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"\n",
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"Categorize all transactions and compute total spending per category.\n",
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"\n",
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"Identify the top 5 categories by total spending.\n",
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"\n",
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"Detect high-frequency purchases, even if individual amounts are small (e.g., $4 coffee bought 40 times).\n",
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"\n",
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"For these, group transactions by merchant/description and count frequency.\n",
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"\n",
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"Highlight the top 5 frequent purchases, with both frequency and total spend.\n",
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"\n",
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"Provide a practical summary of spending habits, covering both biggest expenses and frequent small purchases.\n",
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"\n",
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"Suggest 2–3 actionable recommendations to reduce spending, targeting both:\n",
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"\n",
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"Big categories (e.g., Rent, Travel, Entertainment).\n",
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"\n",
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"Small but frequent “habit expenses” (e.g., coffee, fast food, subscriptions).\n",
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"\n",
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"The output should be a valid JSON object with this structure:\n",
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"{\n",
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" \"summary\": {\n",
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" \"Food\": <amount>,\n",
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" \"Clothing\": <amount>,\n",
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" \"Rent\": <amount>,\n",
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" \"Utilities\": <amount>,\n",
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" \"Entertainment\": <amount>,\n",
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" \"Travel\": <amount>,\n",
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" \"Health\": <amount>,\n",
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" \"Miscellaneous\": <amount>,\n",
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" \"Others\": <amount>\n",
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" },\n",
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" \"total_expenses\": <total>,\n",
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" \"top_5_categories\": [ {\"category\": <name>, \"amount\": <amount>} ],\n",
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" \"top_5_frequent_purchases\": [ {\"item\": <merchant/description>, \"count\": <frequency>, \"total\": <amount>} ],\n",
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" \"insights\": \"<short paragraph summary of spending, including both big categories and frequent small habits>\",\n",
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" \"recommendations\": [ \"<tip1>\", \"<tip2>\", \"<tip3>\" ]\n",
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"}\n",
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"\n",
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"\"\"\"\n",
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"\n",
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" user_prompt = \"Here are my bank account transactions for the past few months:\\n\\n\"\n",
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" for txn in transactions:\n",
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" user_prompt += f\"- Date: {txn['date']}, Description: {txn['description']}, Amount: {txn['amount']}\\n\"\n",
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"\n",
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" user_prompt += \"\"\"\n",
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"Please analyze these transactions according to the instructions in the system prompt.\n",
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"\"\"\"\n",
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"\n",
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" return system_prompt, user_prompt\n",
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"\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "307ca02b-2df6-4996-85e7-d073f74592f5",
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"# ---------- STEP 4: Call OpenAI ----------\n",
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"def analyze_transactions(pdf_path):\n",
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" transactions = extract_transactions_CreditCard_from_pdf(pdf_path)\n",
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" system_prompt, user_prompt = build_prompts(transactions)\n",
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"\n",
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" client = OpenAI() # assumes OPENAI_API_KEY is set in env\n",
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"\n",
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" response = client.chat.completions.create(\n",
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" model = \"gpt-4o-mini\",\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt}\n",
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" ],\n",
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" response_format={\"type\": \"json_object\"} # ensures valid JSON\n",
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" )\n",
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"\n",
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" result = response.choices[0].message.content\n",
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" return json.loads(result)\n",
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"\n",
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"# ---------- MAIN ----------\n",
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"if __name__ == \"__main__\":\n",
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" cc_pdf_file = \"cc_statement.pdf\"\n",
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" # To Debug in case of failures\n",
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" # transactions = extract_transactions_from_pdf(pdf_file)\n",
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" # print(cc_transactions,len(cc_transactions))\n",
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" # system_prompt, user_prompt = build_prompts(cc_transactions)\n",
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" # print(system_prompt, user_prompt)\n",
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"\n",
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" # Analyse the function to create a smart alert\n",
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" cc_transactions = extract_transactions_CreditCard_from_pdf(cc_pdf_file)\n",
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" analysis = analyze_transactions(cc_pdf_file)\n",
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" print(\"=========================================\")\n",
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" print(\"=== Top 5 Spending Habits & Insights ====\")\n",
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" print(\"=========================================\")\n",
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" print(json.dumps(analysis, indent=2))\n",
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" print(\"=========================================\")\n",
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" print(\"=========================================\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "831922f4-5efd-4cba-9975-54767b65f6d6",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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