CashGPT- AI finance assistant to help you track your Google Pay expenses.

community/cashGPT/README.md

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+# CashGPT
+
+Tracking your UPI transactions is a hassle. CashGPT helps you analyze your transactions based on your Google Pay data. You can track your spending and much more.
+
+## Table of Contents
+
+- [Usage](#usage)
+- [Example Prompts](#Example)
+- [Technologies Used](#technologies-used)
+
+## Usage
+
+1. Download your Google Takeout data from [Google Takeout](https://takeout.google.com/) for Google Pay.
+2. Find the `My Activity.html` file inside `Takeout/Google Pay/My Activity` and paste it inside the CashGPT folder.
+3. Start the Python server: `python3 api.py`
+4. Open `index.html` from the `client` folder: `client/index.html`
+
+## Example Prompts
+
+- How much did I overall spend last month?
+- Where did I spend the most this month?
+- My last 5 transactions
+
+## Technologies Used
+
+- **Backend:** Python
+- **Frontend:** HTML, CSS, JavaScript
+- **AI Model API:** proxy.tune.app
+
+## Snippet of Working
+
+![Working Demo](https://github.com/phaniankur/cookbook/blob/main/community/cashGPT/demo.gif)

community/cashGPT/api.py

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+import json
+from http.server import BaseHTTPRequestHandler, HTTPServer
+from cashgpt import cashgpt_controller  # Import the controller function
+
+# Define the request handler class
+class RequestHandler(BaseHTTPRequestHandler):
+
+    def _send_response(self, response_data, status=200):
+        self.send_response(status)
+        self.send_header('Content-type', 'application/json')
+        self.send_header('Access-Control-Allow-Origin', '*') 
+        self.send_header('Access-Control-Allow-Methods', 'POST, OPTIONS')
+        self.send_header('Access-Control-Allow-Headers', 'Content-Type') 
+        self.end_headers()
+        self.wfile.write(json.dumps(response_data).encode('utf-8'))
+
+    # Handle POST requests
+    def do_POST(self):
+        # Check if the request path is /api/cashgpt
+        if self.path == '/api/cashgpt':
+            content_length = int(self.headers['Content-Length']) 
+            post_data = self.rfile.read(content_length)
+
+            try:
+                # Parse the data as JSON
+                data = json.loads(post_data)
+
+                # Check if 'hukum' key is in the data
+                if 'hukum' not in data:
+                    response_data = {"error": "Missing 'hukum' key"}
+                    self._send_response(response_data, status=400)
+                    return
+
+                # Process the 'hukum' value
+                hukum_value = data['hukum']
+                # Call the controller function
+                response_data = cashgpt_controller(hukum_value)
+                self._send_response(response_data)
+
+            except json.JSONDecodeError:
+                # Handle invalid JSON
+                response_data = {"error": "Invalid JSON format"}
+                self._send_response(response_data, status=400)
+        else:
+            self.send_response(404)
+            self.end_headers()
+            self.wfile.write(b'Not Found')
+
+    # Handle OPTIONS requests for CORS preflight
+    def do_OPTIONS(self):
+        self.send_response(204)  # No content
+        self.send_header('Access-Control-Allow-Origin', '*')  # Allow all origins
+        self.send_header('Access-Control-Allow-Methods', 'POST, OPTIONS')  # Allow specific methods
+        self.send_header('Access-Control-Allow-Headers', 'Content-Type')  # Allow specific headers
+        self.end_headers()
+
+# Define the server address and port
+server_address = ('', 8080)
+
+# Create and run the HTTP server
+httpd = HTTPServer(server_address, RequestHandler)
+print("Server running on port 8080...")
+httpd.serve_forever()

community/cashGPT/cashgpt.py

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+from bs4 import BeautifulSoup
+from datetime import datetime, timedelta
+import re
+import http.client
+import json
+import os
+from dotenv import load_dotenv
+load_dotenv()
+
+def extract_content_from_html(file_path):
+    extracted_data = []
+
+    # Open and parse the HTML file
+    with open(file_path, "r") as fp:
+        soup = BeautifulSoup(fp, 'html.parser')
+
+    # Loop through all <div> elements with the specified class
+    for tag in soup.find_all('div', {'class': 'content-cell mdl-cell mdl-cell--6-col mdl-typography--body-1'}):
+        text = tag.get_text(strip=True) 
+        br_tag = tag.find("br")
+
+        # Extract the text after the <br> tag
+        if br_tag:
+            DateToBeAppended = br_tag.find_next_sibling(string=True)
+
+            try:
+                # Convert the string to a datetime object
+                date_object = datetime.strptime(DateToBeAppended, '%b %d, %Y, %I:%M:%S %p %Z')
+
+                # Format the date to only display the date (YYYY-MM-DD)
+                formatted_date = date_object.strftime('%Y-%m-%d')
+
+                # Get the current date and calculate the date one month (30 days) ago
+                current_date = datetime.now()
+                one_month_ago = current_date - timedelta(days=60) # restricting the search till 60 days to keep the model light
+
+                # Check if the `date_object` is within the last month
+                if date_object >= one_month_ago: 
+                    amountToBeAppended = extract_amount(text)  # Extract the amount from the text
+
+                    if amountToBeAppended is not None:
+                         # Identify the business, person, or bank name
+                        business_name = extract_business_name(text)
+                        isPaid = paidOrReceived(text)
+
+                        # Initialize the base data structure
+                        data = {
+                            "date": formatted_date,      
+                            "paid": 0,
+                            "received": 0,
+                            "business_name": business_name
+                        }
+
+                        # Update the `paid` or `received` field based on the transaction type
+                        if isPaid == "paid" or isPaid == "sent":
+                            data["paid"] = amountToBeAppended
+                        elif isPaid == "received":
+                            data["received"] = amountToBeAppended
+
+                        # Append the transaction data to the result list
+                        extracted_data.append(data)
+            except Exception as e:
+                print(f"Error processing date: {e}")
+
+    return extracted_data
+
+# extract business name
+def extract_business_name(text):
+    # Look for "to [Business]" or "using [Bank Account]"
+    to_pattern = re.search(r'to ([A-Za-z\s]+)', text)
+    using_bank_pattern = re.search(r'using (Bank Account)', text)
+
+    if to_pattern:
+        return to_pattern.group(1).strip()  # Extract the business or person name
+    elif using_bank_pattern:
+        return "Bank Account"  # Return "Bank Account" if no business is found
+    else:
+        return "Unknown"  # Return Unknown if no match is found
+
+# extract amount from string
+def extract_amount(text):
+    # Sample regex to extract the amount (e.g., "₹250.00")
+    match = re.search(r'₹([\d,]+\.\d{2})', text)
+    if match:
+        return match.group(1)
+    return None
+
+# was amount paid or received
+def paidOrReceived(text):
+    # logic to determine the transaction type
+    if "Paid" in text:
+        return "paid"
+    elif "Sent" in text:
+        return "sent"
+    elif "Received" in text:
+        return "received"
+    return None
+
+
+def prompt_transaction_calculation(transaction_prompt, client_prompt):
+    conn = http.client.HTTPSConnection("proxy.tune.app")
+    payload = json.dumps({
+        "temperature": 0.9,
+        "messages": [
+            {
+                "role": "system",
+                "content": transaction_prompt
+            },
+            {
+                "role": "user",
+                "content": client_prompt 
+            }
+        ],
+        "model": "AnkurPhani/AP",
+        "stream": False,
+        "frequency_penalty": 0.2,
+        "max_tokens": 500
+    })
+    headers = {
+        'Authorization': os.getenv('AUTHORIZATION_KEY'),
+        'Content-Type': 'application/json'
+    }
+
+    conn.request("POST", "/chat/completions", payload, headers)
+    res = conn.getresponse()
+    data = res.read()
+    result = json.loads(data.decode("utf-8"))
+
+    # Extract and return the desired content
+    return result['choices'][0]['message']['content'] 
+
+
+
+def cashgpt_controller(client_prompt):
+    if client_prompt != "":
+        # Specify the path to the HTML file
+        file_path = "./My Activity.html"
+
+        # Call the function and store the result
+        transaction = extract_content_from_html(file_path)
+
+        # Convert the transaction to a string representation
+        transaction_string = str(transaction)
+        # print("Transaction str", transaction)
+
+        accountant_prompt = f"You are an experienced accountant responsible for handling your client's finances. You will be prompted by your client to analyse his financial transactions. You are provided an array of objects of your client's transactions like transaction date, paid or received amount and the name of the business the amount was paid to. \n 'date' key is the date of transaction, 'paid' is amount is paid if not zero and 'received' is amount is received if non zero. 'business_name' is the name of the business. Calculate requested calculations by your client like expenses, or received to a business or monthly or weekly expenses, Last paid amount to a business or total amount paid to a business in a month.\n\nHere is the list of all transactions\n\n{transaction_string}The output of your response needs to be formal and any amount mentioned should be in rupee (₹). Stick to the point with the answers."
+        response = prompt_transaction_calculation(accountant_prompt, client_prompt)
+        return response
+    else:
+        print("Prompt is required to continue.")
+
+        return "no prompt to continue"

community/cashGPT/client/index.css

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+@import url('https://fonts.googleapis.com/css2?family=Roboto:wght@400;700&display=swap');
+
+body {
+    background-color: #f0f8f0;
+    font-family: 'Roboto', sans-serif;
+    margin: 0;
+    display: flex;
+    justify-content: center;
+    align-items: center; 
+    height: 100vh; 
+}
+
+.chat-container {
+    /* min-width: 7%; */
+    width: 800px;
+    margin: 0; /*Remove margin to use flex centering */
+    border: 1px solid #4CAF50;
+    border-radius: 10px;
+    overflow: hidden;
+    box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
+    display: flex;
+    flex-direction: column;
+    height: 600px; /* Set a fixed height */
+}
+
+
+.chat-header {
+    background-color: #4CAF50;
+    color: white;
+    padding: 15px;
+    text-align: center;
+}
+.chat-header h2{
+    font-size: 18px;
+}
+.chat-header p{
+    font-size: 18px;
+}
+
+.chat-box {
+    flex: 1;
+    display: flex;
+    flex-direction: column;
+    justify-content: flex-start;
+    align-items: flex-start; /* Align items to the left */
+    height: 300px;
+    overflow-y: auto;
+    padding: 10px;
+    background-color: #fff;
+}
+
+.input-container {
+    display: flex;
+    padding: 10px;
+    background-color: #f9f9f9;
+    border-top: 1px solid #4CAF50;
+    min-height: 50px;
+}
+
+input[type="text"] {
+    flex: 1;
+    padding: 10px;
+    border: 1px solid #4CAF50;
+    border-radius: 5px;
+    margin-right: 10px;
+}
+
+button {
+    background-color: #4CAF50;
+    color: white;
+    border: none;
+    padding: 10px 15px;
+    border-radius: 5px;
+    cursor: pointer;
+}
+
+button:hover {
+    background-color: #45a049;
+}
+
+.message {
+    margin: 10px 0;
+    padding: 10px;
+    border-radius: 10px;
+    max-width: 70%;
+}
+
+.user {
+    background-color: #d1ffd1;
+    align-self: flex-start;
+}
+
+.assistant {
+    background-color: #4CAF50;
+    color: white;
+    align-self: flex-end;
+    line-height: 28px;
+}
+
+/* Animation */
+.message {
+    opacity: 0;
+    transform: translateY(20px);
+    transition: opacity 0.5s, transform 0.5s;
+}
+
+.message.show {
+    opacity: 1;
+    transform: translateY(0);
+}

community/cashGPT/client/index.html

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+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <link rel="stylesheet" href="./index.css">
+    <title>CashGPT</title>
+</head>
+
+<body>
+    <div class="chat-container">
+        <div class="chat-header">
+            <h1>CashGPT</h1>
+            <p>AI Finance assistant to help you track your Google Pay expenses.</p>
+
+        </div>
+        <div class="chat-box" id="chat-box">
+            <!-- Chat messages will be displayed here -->
+        </div>
+        <div class="input-container">
+            <input type="text" id="user-input" placeholder="How much did I spend this month?" />
+            <button id="send-button">Send</button>
+        </div>
+    </div>
+    <script src="script.js"></script>
+</body>
+
+</html>