from flask import Flask,request,make_response import os import logging from dotenv import load_dotenv from heyoo import WhatsApp import assemblyai as aai import openai from utility import parse_multiple_transactions, create_inventory, create_sale from google.cloud import firestore # load env data load_dotenv() # messenger object messenger = WhatsApp( os.environ["whatsapp_token"], phone_number_id=os.environ["phone_number_id"] ) # aai.settings.api_key = os.environ["aai_key"] # transcriber = aai.Transcriber() # Authenticate to Firesotre with the JSON account key db = firestore.Client.from_service_account_json("firestore-key.json") app = Flask(__name__) VERIFY_TOKEN = "30cca545-3838-48b2-80a7-9e43b1ae8ce4" client = openai.OpenAI( api_key=os.environ.get("sambanova_api_key"), base_url="https://api.sambanova.ai/v1", ) def generateResponse(prompt): #----- Call API to classify and extract relevant transaction information # These templates help provide a unified response format for use as context clues when # parsing the AI generated response into a structured data format relevant_info_template = """ Intent: The CRUD operation Transaction Type: The type of transaction Details: as a sublist of the key details like name of item, amount, description, among other details you are able to extract. """ sample_response_template = """ The information provided indicates that you want to **create/record** a new transaction. **Extracted Information**: **Intent**: Create Transaction 1: **Transaction Type**: Purchase **Details**: - Item: Car - Purpose: Business - Quantity: 1 - Cost: 10000 - Tax: 200 - Note: A new car for business Transaction 2: **Transaction Type**: Expense **Details**: - Item: Office Chair - Quantity: 2 - Amount: 300 USD - Category: Furniture """ response = client.chat.completions.create( model='Meta-Llama-3.1-70B-Instruct', messages=[ {"role": "system", "content": f"You are a helpful assistant that classifies transactions written in natural language into CRUD operations (Create, Read, Update, and Delete) and extracts relevant information. Format the relevant information extracted from the transaction text in this format: {relevant_info_template}. You can use markdown syntax to present a nicely formated and readable response to the user, but make sure the user does not see the markdown keyword. Keywords and field names must be in bold face. A sample response could look like this: {sample_response_template}. Delineate multiple transactions with the label 'Transaction 1' before the start of the relevant information for each transaction. There should be only one intent even in the case of multiple transactions."}, {"role": "user", "content": prompt} ] ) #----- Process response try: response = response.choices[0].message.content except Exception as e: print(f'An error occurred: {str(e)}') response = None return response def respond(query_str:str): response = "hello, I don't have a brain yet" return response @app.route("/", methods=["GET", "POST"]) def hook(): if request.method == "GET": if request.args.get("hub.verify_token") == VERIFY_TOKEN: logging.info("Verified webhook") response = make_response(request.args.get("hub.challenge"), 200) response.mimetype = "text/plain" return response logging.error("Webhook Verification failed") return "Invalid verification token" # get message update.. data = request.get_json() changed_field = messenger.changed_field(data) if changed_field == "messages": new_message = messenger.get_mobile(data) if new_message: mobile = messenger.get_mobile(data) message_type = messenger.get_message_type(data) if message_type == "text": message = messenger.get_message(data) # Handle greetings if message.lower() in ("hi", "hello", "help", "how are you"): response = "Hi there! My name is SmartLedger. How can I help you today?" messenger.send_message(message=f"{response}",recipient_id=mobile) else: response = str(generateResponse(message)) # Parse AI generated response into a structured format parsed_trans_data = parse_multiple_transactions(response) # messenger.send_message(message=f"{response} \n\n {parsed_trans_data}", recipient_id=mobile) print("Response:", response) logging.info(f"\nAnswer: {response}\n") # Handle cases where response is not a valid image path # messenger.send_message(message=f"{response}", recipient_id=mobile) intent = parsed_trans_data[0]['intent'].lower() trans_type = parsed_trans_data[0]['transaction_type'].lower() if intent == 'create': if trans_type == 'purchase': if create_inventory(mobile, parsed_trans_data): firestore_msg = "Transaction recorded successfully!" else: firestore_msg = "Sorry, could not record transaction!" elif trans_type == 'sale': if create_sale(mobile, parsed_trans_data): firestore_msg = "Transaction recorded successfully!" else: firestore_msg = "Sorry, could not record transaction!" elif intent == 'update': # handle update pass elif intent == 'delete': # handle delete pass else: firestore_msg = f'The detected intent, {intent}, is not currently supported!' messenger.send_message(message=f"{response},\n\n {firestore_msg}", recipient_id=mobile) # elif message_type == "audio": # audio = messenger.get_audio(data) # audio_id, mime_type = audio["id"], audio["mime_type"] # audio_url = messenger.query_media_url(audio_id) # audio_filename = messenger.download_media(audio_url, mime_type) # transcript =transcriber.transcribe(audio_filename) # print(audio_filename) # print(transcript.text) # res = transcript.text # logging.info(f"\nAudio: {audio}\n") # response = str(generateResponse(res)) # if isinstance(response, str): # messenger.send_message(message=f"{response}", recipient_id=mobile) # elif isinstance(response, str) and os.path.isfile(response): # messenger.send_image(image_path=response, recipient_id=mobile) else: messenger.send_message(message="Please send me text or audio messages",recipient_id=mobile) return "ok" if __name__ == '__main__': app.run(debug=True,host="0.0.0.0", port=7860)