state_handler.py

import os
from langchain.chains.router import MultiPromptChain
from langchain.chains.router.llm_router import LLMRouterChain,RouterOutputParser
# from langchain.prompts import PromptTemplate
from langchain.prompts import ChatPromptTemplate
from langchain import OpenAI, LLMChain, PromptTemplate
from langchain.memory import ConversationBufferMemory
import os
import openai
from langchain.chat_models import ChatOpenAI
from api_call import send_request
from langchain.llms import LlamaCpp
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])

# questions = {
#     'buy house or loan': 'Do you want to buy a house or loan for it?',
#     'zip code':'Could you please provide me with the zip code of the area where you are looking to buy a home?',
#     'home feature' : 'Can you describe the desired features of your dream home and your goals',
#     'budget' : 'What is your budget for buying a home?',
#     'first time buyer' : 'Are you a first-time buyer?',
#     'introduce' : 'Hi. I\'m here to assist you with buying a home or getting a loan. Could you please provide me with some information to help you better?',
#     'ignored' : 'I don\'t understand. Could you please rephrase your question?',
#     'ask_question' : 'Could you please provide me with the zip code of the area where you are looking to buy a home?'    
#       }


# questions = {
#     'buy house or loan': 'Which one are you more interested in? Buy a house or loan for it?',
#     'zip code':'Could you please provide me with the zip code of the area where you are looking to buy a home?',
#     'home feature' : 'Can you describe the desired features of your dream home and your goals',
#     'budget' : 'What is your budget for buying a home?',
#     'first time buyer' : 'Are you a first-time buyer?',
#     'introduce' : 'Hi I’m Samar, your real state assistant. In 60 seconds I can help you find a house or how to save $500 on your loans.',
#     'ignored' : 'I don\'t understand. Could you please rephrase your question?',
#     'ask_question' : 'Could you please provide me with the zip code of the area where you are looking to buy a home?'    
#       }


questions = {
    'buy house or loan': 'Are you currently in the market to purchase or rent a home?',
    'zip code':'Could you please provide me with the zip code of the area where you are looking to buy a home?',
    'home feature' : 'Can you describe the desired features of your dream home and your goals',
    'budget' : 'What is your budget for buying a home?',
    'first time buyer' : 'Are you a first-time buyer?',
    'introduce' : 'Hi, this is Samar from Royal Real State Agency. I hope you\'re doing well! I wanted to reach out because \
        I noticed your interest in real estate and thought I could assist you in finding the perfect home.',
    'ignored' : 'I don\'t understand. Could you please rephrase your question?',
    'ask_question' : 'Could you please provide me with the zip code of the area where you are looking to buy a home?'    
      }

def init_chain():

    budget_template = """ You are a compressor that get a question and answer aboute money/budget and extremly compress the answer into a number and return an integer number.
    example: if input=600k then output=600000
    Here is the question :
    {question}
    Here is the answer :
    {answer}"""
    
    zipcode_templet = """ You are a compressor that get a question and answer aboute zip code and extremly compress the answer into a number and return only a number.
    Here is the question :
    {question}
    Here is the answer :
    {answer}"""
    
    feature_template = """ You are a compressor that get a question and answer about desiered home feature and extract home feature from the answer into a short term.
    Here is the question :
    {question}
    Here is the answer :
    {answer}"""
   
    buy_loan_template = """ You are a compressor that get a question and answer a bout buy house or loan, and extremly compress the answer into short term.
    Here is the question :
    {question}
    Here is the answer :
    {answer}"""
    
    first_buyer_template = """ You are a compressor that get a question and answer a bout buy house or loan, and extremly compress the answer into short term.
    Here is the question :
    {question}
    Here is the answer :
    {answer}"""
    
    home_feature_template = """ You are a prompt generator to generate a sentence to describe a home property \ 
                            for a buyer based on the input_data. for example describe prices, floorSizeValue,numRoom \
                            numFloor, numBedroom, neighborhoods, floorSizeValue, feature item of input_data.
    Here is the input_data :
    {input_data}
    """

    cat_task_template = """ You are a classifier to assign input_message into one of the below categoryis. \
        categoryis Item: \
        - `buy house or loan `: (example: Are you currently in the market to purchase or rent a home? yes. buy a house) \
        - `budget`:  (example: What is your budget for buying a home? 600k or 5000$ or 8000 or I have 36000$ money) \
        - `first time buyer`:  (example: Are you a first-time buyer? yes) \
        - `zip code` : (example: Could you please provide me with the zip code of the area you are interested in? 19701 , 85412 , ...)
        - `home feature' : (example : Can you describe the desired features of your dream home and your goals? 2 rooms)
        - `ignored` : a message that don't related to any question and is a unusaul message. 

        Here is the input_message and question :
        {input_message}
        output:
        return the detected category.
    """
    prompt_infos = [
    {
        "name": "budget",
        "prompt_template": budget_template
    },
    {
        "name": "zip code",
        "prompt_template": zipcode_templet
    },
    {
        "name": "home feature",
        "prompt_template": feature_template
    },
    {
        "name": "buy house or loan", 
        "prompt_template": buy_loan_template
    },
    {
        "name": "first time buyer", 
        "prompt_template": first_buyer_template
    }, 
    {
        "name": "home_feature", 
        "prompt_template": home_feature_template
    },
    {
        "name": "category", 
        "prompt_template": cat_task_template
    },
    ]
    destination_chains = {}
    for p_info in prompt_infos:
        name = p_info["name"]
        prompt_template = p_info["prompt_template"]
        prompt = ChatPromptTemplate.from_template(template=prompt_template,)
        chain = LLMChain(llm=llm, prompt=prompt)
        destination_chains[name] = chain 
    return destination_chains


#Age + Pricing
llm = ChatOpenAI(temperature=0.0)
# llm = LlamaCpp(
#     model_path="/home/yaghoubian/yaghoubian/fast_avatar/hrviton/ControlNet-v1-1-nightly/lang_chain/aa/llama-2-7b-chat.ggmlv3.q6_K.bin",
#     input={"temperature": 0.1, "max_length": 2000, "top_p": 1},
#     callback_manager=callback_manager,)

# prompt_instruction = """  
# Instructions: you are a classifier for classify input message into one of the below categoryis. \
# categoryis Item: \
#  -`math_question` \
#  -`Historical` \
#  Here is the question: what is 1+1? \
# """
# print("before a")
# a = llm(prompt_instruction)
# print(a)

chains = init_chain()

def state_handler(message,user_state=None, user_info=None,gathered_info=None):
    if user_info == None:
        print("new_user")
        user_state = ['introduce','buy house or loan','zip code','home feature','budget','first time buyer']
        user_info = questions[user_state[0]] + "\n" +  questions[user_state[1]]
        gathered_info = {}
        user_state.remove('introduce')
        return user_state,user_info,gathered_info
    else:
        assigned_classes = category(input_message = user_info.split('\n')[-1] + " " + message)
        
        for assigned_class in assigned_classes : 
            Short_response, user_state= compress_response(input_message=message,input_question=questions[assigned_class],\
                                                        user_state=user_state,assigned_class=assigned_class,user_info=user_info)
            print (f"{assigned_class}  :  {Short_response}")
            gathered_info[assigned_class] = Short_response
            
        if len(user_state)>0:
            if 'rephrase your answer for this question' in Short_response:
                user_info = Short_response
            else :
                user_info = questions[user_state[0]]
        else:
            res,response = send_request(gathered_info['budget'],gathered_info['zip code'])
            # print("Start Zillow scraping. ")
            # res,response = send_zillow_request(gathered_info['budget'],gathered_info['zip code'])
            if response == None:
                user_info = "Sorry, there is an error in searching result. please try again."
            else:
                if res>0:
                    user_info = f"This is your information: \n{gathered_info}. \n we find {res} results." #\
                                # \n  Here is the result specification: \n {response}."
                    for idx,i in enumerate(response): 
                        in_feature =f" numBathroom: {i['numBathroom']} , numRoom: {i['numRoom']}, numFloor: {i['numFloor']},  yearBuilt:{i['yearBuilt']}, floorSizeValue: {i['floorSizeValue']} {i['floorSizeUnit']} "
                        home_feature_prompt = chains['home_feature'].run(input_data=in_feature)
                        print("home_feature_prompt: ",home_feature_prompt)
                        try: 
                            user_info = user_info + f"\n{idx+1}- " +home_feature_prompt + f"\n {str(i['mostRecentPriceSourceURL'])} \n"
                        except: 
                            user_info = user_info + f"\n{idx+1}- " +home_feature_prompt

                else : 
                    user_info = f"This is your information: \n{gathered_info}. \n Sorry. we can't find \
                                    any case by the entered budget and zip code"

        return user_state,user_info,gathered_info


def category(input_message):
    
    # os.environ["OPENAI_API_KEY"] = "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"
    # llm = ChatOpenAI(temperature=0.1)

    # # You are a chatbot. you must read the input message and \
    # # tag or categorize it to one of the below item 

    # new_task_template = """ You are a classifier to assign input_message into one (or more than one) of the below categoryis.\  
    # categoryis Item: \
    # - `buy house or loan `: (example: do you want to buy a house ot loan for it? buy a house) \
    # - `budget`:  (example: What is your budget for buying a home? 5000$ or 8000 or I have 36000$ money) \
    # - `first time buyer`:  (example: Are you a first-time buyer? yes) \
    # - `zip code` : (example: Could you please provide me with the zip code of the area you are interested in? 8542)
    # - `home feature' : (example : Can you describe the desired features of your dream home and your goals? 2 rooms)
    # - `introduce` : (example: Hi. I'm here to assist you with buying a home or getting a loan.)
    # - `ignored` : a message that don't related to any question and is a unusaul message. 
    # - `ask_question` : (example: Could you please provide me with some information to help you better? Sure.).

    # Here is the input message :
    # {input_message}
    # output:
    # return the detected category.
    # """
    # prompt = ChatPromptTemplate.from_template(template=new_task_template,)
    # cat_chain = LLMChain(llm=llm, prompt=prompt)

    classe_list = ["buy house or loan","budget","first time buyer","zip code","home feature"]
    
    message_classes = chains['category'].run(input_message=input_message)
    
    detected_classes = []
    for i in classe_list:
        if i in message_classes.lower():
            detected_classes.append(i)
    print("detected_classes: ",detected_classes)
    return detected_classes


def compress_response(input_message,input_question,user_state,assigned_class=None,user_info=None): 

    # new_task_template = """ You are a compressor that get a question and answer and extremly compress the answer into short term. \  
    # Here is the question :
    # {question}
    # Here is the answer :
    # {answer}
    # """
    # prompt = ChatPromptTemplate.from_template(template=new_task_template)
    # chain = LLMChain(llm=llm, prompt=prompt)
    
    if  "buy house or loan"in assigned_class.lower():
        user_state.remove('buy house or loan')
        chain = chains[assigned_class]
        response = chain.run(question=input_question , answer=input_message)
    elif  'zip code'in assigned_class.lower():
        user_state.remove('zip code')
        chain = chains[assigned_class]
        response = chain.run(question=input_question , answer=input_message)
    elif  'home feature'in assigned_class.lower():
        user_state.remove('home feature')
        chain = chains[assigned_class]
        response = chain.run(question=input_question , answer=input_message)
    elif  'budget' in assigned_class.lower():
        user_state.remove('budget')
        chain = chains[assigned_class]
        response = chain.run(question=input_question , answer=input_message)
    elif 'first time buyer' in assigned_class.lower():
        user_state.remove('first time buyer')
        chain = chains[assigned_class]
        response = chain.run(question=input_question , answer=input_message)
    elif 'ignored' in assigned_class.lower():
        # user_state.remove('ignored')
        response = f"I can't understand your answer. please rephrase your answer for this question. \n {user_info}"

    return response ,user_state 






# Great. You can see this website to see the house feature.
 
# https://www.zillow.com/homes/22201-wayside-Mission-viej-CA92692_ib/25614382_zpid