Question:
I have a deployed Repl, which means I shouldn’t need to run the app in the console. However, when I ‘stop’ it from running and then use my application (the app is a background worker that sends a webhook to Twilio when a text message is received at a particular number) by sending a text, I notice that it starts booting up again in the console. Ultimately it wont work unless its running in the console, but I thought that wasnt necessary if its deployed
Repl link/Link to where the bug appears:
https://replit.com/@JasonTange/TheGame-message-2
Screenshots, links, or other helpful context:
# Import libraries
from flask import Flask, request
from twilio.twiml.messaging_response import MessagingResponse
import os
import sqlite3
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory
from langchain.chains import RetrievalQA
from transformers import AutoTokenizer, AutoModel
import requests
HUBSPOT_TOKEN = ""
def get_contact_by_phone(phone_number):
url = "https://api.hubapi.com/crm/v3/objects/contacts/search"
payload = {
"filterGroups": [{
"filters": [{
"propertyName": "mobilephone",
"operator": "EQ",
"value": phone_number
}]
}],
"properties": ["mobilephone", "message_history"],
"limit": 1
}
headers = {
'accept': "application/json",
'content-type': "application/json",
'authorization': f"Bearer {HUBSPOT_TOKEN}"
}
response = requests.post(url, json=payload, headers=headers)
return response.json()
def update_contact_messages(contact_id, message_count, message_history):
url = f"https://api.hubapi.com/crm/v3/objects/contacts/{contact_id}"
payload = {
"properties": {
"of_messages": str(message_count),
"message_history": message_history # Add this line
}
}
headers = {
'accept': "application/json",
'content-type': "application/json",
'authorization': f"Bearer {HUBSPOT_TOKEN}"
}
response = requests.patch(url, json=payload, headers=headers)
return response.json()
def create_contact(phone_number, message_history):
url = "https://api.hubapi.com/crm/v3/objects/contacts"
payload = {
"properties": {
"firstname": phone_number,
"mobilephone": phone_number,
"message_history": message_history # Add this line
}
}
headers = {
'accept': "application/json",
'content-type': "application/json",
'authorization': f"Bearer {HUBSPOT_TOKEN}"
}
response = requests.post(url, json=payload, headers=headers)
return response.json()
# Initialize SQLite Database
def init_db():
with sqlite3.connect('message_counts.db') as conn:
cursor = conn.cursor()
cursor.execute('''
CREATE TABLE IF NOT EXISTS message_counts (
phone_number TEXT PRIMARY KEY,
count INTEGER DEFAULT 0
);
''')
conn.commit()
init_db()
# Set OpenAI API Key
os.environ["OPENAI_API_KEY"] = ""
# Load Sentence Transformer model
model_name = "intfloat/e5-base-v2"
embeddings = HuggingFaceEmbeddings(model_name=model_name)
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
# Load FAISS index
persist_directory = 'thegame'
VectorStore = FAISS.load_local(persist_directory, embeddings)
# Initialize language model
llm = ChatOpenAI(temperature=0.7, model_name='gpt-3.5-turbo')
template = """You are just like the character 'Hank Moody' in the show 'Californication'
{context}
Also, here are previous text messages: {chat_history}
Human: {human_input}
Chatbot:"""
prompt = PromptTemplate(
input_variables=["chat_history", "human_input", "context"], template=template
)
memory = ConversationBufferMemory(memory_key="chat_history", input_key="human_input")
chain = load_qa_chain(
llm=llm, chain_type="stuff", memory=memory, prompt=prompt
)
# Create Flask app instance
app = Flask(__name__)
@app.route('/api', methods=['POST'])
def api_endpoint():
sender_phone_number = request.form.get('From')
user_query = request.form.get('Body')
with sqlite3.connect('message_counts.db') as conn:
cursor = conn.cursor()
cursor.execute("SELECT count FROM message_counts WHERE phone_number=?", (sender_phone_number,))
row = cursor.fetchone()
if row:
current_count = row[0]
else:
current_count = 0
if current_count >= 300:
resp = MessagingResponse()
resp.message("You have reached the maximum message limit. Please wait for some time.")
return str(resp)
if row:
cursor.execute("UPDATE message_counts SET count = count + 1 WHERE phone_number=?", (sender_phone_number,))
else:
cursor.execute("INSERT INTO message_counts (phone_number, count) VALUES (?, 1)", (sender_phone_number,))
conn.commit()
if not user_query:
return "User query is missing or empty!", 400
docs = VectorStore.similarity_search(user_query, k=5)
sources = [doc.metadata.get('source') for doc in docs]
ans = chain({"input_documents": docs, "human_input": user_query}, return_only_outputs=True)
contacts = get_contact_by_phone(sender_phone_number)
# Initialize current_history with empty string
current_history = ""
if contacts.get('results'):
contact = contacts['results'][0]
properties = contact.get('properties', {})
contact_id = contact.get('id')
# Extract current history, if it exists
message_history = properties.get('message_history') if properties else None
if isinstance(message_history, dict):
current_history = message_history.get('value', '')
else:
current_history = ""
current_history += f"\nHuman: {user_query}\nChatbot: {ans['output_text']}"
if contacts.get('results'):
update_contact_messages(contact_id, current_count + 1, current_history)
else:
create_contact(sender_phone_number, current_history)
resp = MessagingResponse()
resp.message(ans['output_text'])
return str(resp)
@app.route('/', methods=['GET'])
def root_endpoint():
return "Welcome to the root endpoint!"
if __name__ == '__main__':
app.run(host='0.0.0.0', port=8080)
