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Update app/main.py
Browse files- app/main.py +52 -53
app/main.py
CHANGED
@@ -2,82 +2,77 @@ from twilio.rest import Client
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import yaml
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import json
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import os
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import
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import
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from langchain.embeddings import OpenAIEmbeddings
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from langchain_community.vectorstores import Chroma
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from helper import retrieve_relevant_context, generate_response_with_context
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from pathlib import Path
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file = Path(__file__).resolve()
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parent, root = file.parent, file.parents[1]
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sys.path.append(str(root))
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print("str(root)
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print("parent
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print("CWD :",os.getcwd())
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# Load relevant API Keys
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file_path = parent / 'Config/API_KEYS.yml'
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# Define the persist directory
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persist_directory = str(parent / 'vector_db/chroma_v01/')
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print("
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print("persist_directory :",str(persist_directory))
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with open(file_path, 'r') as file:
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api_keys = yaml.safe_load(file)
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# Extract openai username and key
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openai_key = api_keys['OPEN_AI']['Key']
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os.environ["OPENAI_API_KEY"] = openai_key
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# Extract openai username and key
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account_sid = api_keys['TWILIO']['account_sid']
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auth_token = api_keys['TWILIO']['auth_token']
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account_sid
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auth_token = auth_token
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print("====account_sid:=====",account_sid)
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# Define the persist directory
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# persist_directory = './vector_db/chroma_v01'
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# Initialize the embeddings model
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embedding_model = OpenAIEmbeddings()
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### Vectorstores
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from langchain_community.vectorstores import Chroma
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# Load the Chroma vector store
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vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_model)
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#setup Twilio client
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client = Client(account_sid, auth_token)
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#
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print("flask app is running")
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app = Flask(__name__)
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@app.route("/whatsapp", methods=['GET', 'POST'])
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"""Send a dynamic reply to an incoming text message"""
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# Get the message the user sent our Twilio number
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body = request.values.get('Body', None)
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print("body
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##### Process incoming text #############
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incoming_msg = body.strip()
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if not incoming_msg:
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return str(MessagingResponse())
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retrieved_texts = retrieve_relevant_context(vectordb, incoming_msg)
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context = "\n".join(retrieved_texts)
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response = generate_response_with_context(incoming_msg, context)
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print("response
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##### Process incoming text Done #############
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# Start our TwiML response
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resp = MessagingResponse()
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print("TwiML resp
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resp.message(response)
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return str(resp)
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if __name__ == "__main__":
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import yaml
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import json
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import os
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import sys
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from pathlib import Path
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from flask import Flask, request, redirect
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from twilio.twiml.messaging_response import MessagingResponse
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from langchain.embeddings import OpenAIEmbeddings
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from langchain_community.vectorstores import Chroma
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from helper import retrieve_relevant_context, generate_response_with_context
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from pyngrok import ngrok
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# Setting up paths
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file = Path(__file__).resolve()
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parent, root = file.parent, file.parents[1]
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sys.path.append(str(root))
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print("str(root):", str(root))
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print("parent:", parent)
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print("CWD:", os.getcwd())
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# Load relevant API Keys
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file_path = parent / 'Config/API_KEYS.yml'
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persist_directory = str(parent / 'vector_db/chroma_v01/')
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print("file_path:", file_path)
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print("persist_directory:", str(persist_directory))
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with open(file_path, 'r') as file:
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api_keys = yaml.safe_load(file)
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# Extract OpenAI key
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openai_key = api_keys['OPEN_AI']['Key']
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os.environ["OPENAI_API_KEY"] = openai_key
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# Extract Twilio credentials
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account_sid = api_keys['TWILIO']['account_sid']
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auth_token = api_keys['TWILIO']['auth_token']
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twilio_whatsapp_number = api_keys['TWILIO']['whatsapp_number']
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print("====account_sid:=====", account_sid)
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# Initialize the embeddings model
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embedding_model = OpenAIEmbeddings()
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# Load the Chroma vector store
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vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_model)
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# Setup Twilio client
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client = Client(account_sid, auth_token)
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# Example to send a WhatsApp message
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def send_whatsapp_message(to_number, message):
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"""
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Send a WhatsApp message using Twilio.
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:param to_number: str, recipient's WhatsApp number in the format 'whatsapp:+1234567890'
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:param message: str, message text to send
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"""
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from_number = f'whatsapp:{twilio_whatsapp_number}'
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to_number = f'whatsapp:{to_number}'
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message = client.messages.create(
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body=message,
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from_=from_number,
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to=to_number
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)
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print(f"Message sent with SID: {message.sid}")
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# Example usage
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if __name__ == "__main__":
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recipient_number = '+91-9108843322' # Replace with the recipient's WhatsApp number
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text_message = 'Hello from Twilio WhatsApp!'
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send_whatsapp_message(recipient_number, text_message)
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# Flask app setup
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print("Flask app is running")
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app = Flask(__name__)
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@app.route("/whatsapp", methods=['GET', 'POST'])
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"""Send a dynamic reply to an incoming text message"""
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# Get the message the user sent our Twilio number
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body = request.values.get('Body', None)
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print("body:", body)
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incoming_msg = body.strip()
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if not incoming_msg:
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return str(MessagingResponse())
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retrieved_texts = retrieve_relevant_context(vectordb, incoming_msg)
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context = "\n".join(retrieved_texts)
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response = generate_response_with_context(incoming_msg, context)
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print("response:", response)
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# Start our TwiML response
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resp = MessagingResponse()
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print("TwiML resp:", resp)
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resp.message(response)
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return str(resp)
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if __name__ == "__main__":
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# Start ngrok tunnel
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ngrok_tunnel = ngrok.connect(5000)
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print(f"ngrok tunnel 'http' URL: {ngrok_tunnel.public_url}")
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# Print the ngrok URL so you can set it in Twilio webhook
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print("ngrok URL:", ngrok_tunnel.public_url + "/whatsapp")
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app.run(host='0.0.0.0', port=5000, debug=True)
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