Add app file

api_call.py

@@ -0,0 +1,80 @@
+import os
+import json
+import requests
+import requests
+import urllib.parse
+import json
+from easydict import EasyDict as edict
+from fastapi import FastAPI, File, UploadFile, Form
+from Zillow_Scraper.selenium_wrapper import search 
+
+API_token = 'eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJ1eG42aWVkcXlwMmtzbGVnZms2N3pmN3kyN2MzMTBsaSIsImlzcyI6ImRhdGFmaW5pdGkuY28ifQ.fwFM5-Z5ZMPwqbRuiyQhcQP9NjMqP-sm-EZ7W3BA2NnfqN6p0_Kapz6pr2tMGlZ7ranZ1DVb_gScmtPm8R_F6JpJ5dKNL7rZamUwR84TzxNPzOAns7UgjMSx-5p-7f9U2_NGg3ORveaQTfs1-QH9uiDw4v6U91lhD4rReXVgom5qhNlSbmHkj81DHVHaX969hPDtdEiQ5tspib3xgwWDyXYQYGcwPFoLIHET9srNP7zKWyghbiwh90DTnj3ASmYFO3Nrj8zdfXmkCkUXa1vwEf69qKD8T2L8-OsZlMn-t8X7uYyi_pFP4v78N_6SgfX1t8csRUqlymkFX-gVnqeDsPivSXvQrz9Su68g2HlMyOrutpdc7i6z3OCy8zt3HET_LMw4ccKHYeNxHJLf572ALRHvLVxWJTtEVPyqZ_IXHSHdhpk5jGvxSaW_T9ufmmeNfuLdDnOm_T1VhnXMI6-iAYDsWidSSzhQkrle8XcptRbyRyilMzU37BCd4klUiuVoMNJrBhIQEM9vgDEH8BfJHHOha3dCxWj0pKzlv4SHXokTlBVNDWg_vZFobylTOzsi0055PgenV-qiACzZpbFylTP0egdVKrxxfuOYe8fhzi61cwoaZ9YUqOspYRGsm_wRCwc3daS8SEvaEl1tXoRTpKsxe5kI6u7AVubqmhhI1LA'
+# eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJ1MzRmanRmdmZwNnplcmJldHEzcnpienExZ2l6b201dyIsImlzcyI6ImRhdGFmaW5pdGkuY28ifQ.CONEaG0VEABopROdGvMWy3sBv0X8dB2rAUhijXEKRdRgAbII-zbX6FIZsV7pZ72M5BbaREhjOi9eU-5NKpvhiWnU8cjlrx0AmHRNrIBrHmPvoFE9IFeIN1pYS9nOvU6CctQB6ZvmoEMpF_VYVKwkBkmUNmY1UPF6TdINkdWg9ym0pC1TIhch4WM5akuHwlJXirzbV07SRrijnQUT1DRpt7_igbwRSt8smFUWejFDJnbxAaMeqsLHo7Trb3FgkBLAEHDdA7CgG2HONsAvOEKnf7hwpp3_mfziZ_uYO369wbfxkYIn6WnMqHYjjC6viD0b5zZI-hjZgT-87CUetWj3Oqs_B37JzqiHm0rxRHxlfFP_IvpqS263tjdnMgSUmsETy62W_cXAuk_Wn45nGiPfM26HclcZxBieYuCOFNojdZE-1apMehtXAgN0JEN29y5JZGBoSPHPZQKfvfAGEHlh-Ty3yZRiiPaQ2m4BLilZN2bB_tA0c8s7RXeHWph_t_GH5xoLO0PB_fZa_RrUmg-Cc2wsjTr59rYH-uGWpfhE-7Qa1du1tZYE4PpRqTq7KysAZzfuAgkhpJzpXwDAnuEsRDSGdhbteKnRIKajiEw8jt9KW0OnXEDOxx_cU9YrPmDvJIdvQjKKYz1Tuptce7ZmxXaHYoOtzN1I1aKpGQe1UJw
+format = 'JSON'
+# query = 'country:US AND numRoom:3 AND numFloor:1 AND postalCode:75231 AND "prices":{"amountMax": 289000}'
+download = False
+request_headers = {
+    'Authorization': 'Bearer ' + API_token,
+    'Content-Type': 'application/json',
+}
+def send_request (budget,zip_code): 
+    # query = "country:US AND numRoom:3 AND numFloor:1 AND postalCode:75231 AND {prices.amountMin:>=100} AND {prices.amountMax:<={param}} AND {prices.currency:USD} "
+    query = "country:US AND numRoom:3 AND numFloor:1 AND postalCode:"+f"{zip_code}"+" AND {prices.amountMin:>=100} AND {prices.amountMax:<="+ f"{budget}"+"} AND {prices.currency:USD} "
+    num_records = 7
+    request_data = {
+        'query': query,
+        'format': format,
+        'num_records': num_records,
+        'download': download,
+    }
+    # Make the API call.
+    r = requests.post('https://api.datafiniti.co/v4/properties/search',json=request_data,headers=request_headers)
+    if r.status_code == 200:
+        out_data = json.loads(r.content.decode('utf8').replace('"', '\"'))
+        print("Founded result: ",out_data['num_found'])
+        return out_data['num_found'],out_data['records']
+    else:
+        print('Request failed')
+        return None,None
+
+
+# _,o = send_request (8000000,19701)
+# print(o)
+
+def send_zillow_request (budget,zip_code): 
+    inf = search(zipcode=zip_code, budget=budget, for_sale = "rent")   
+    return 1, inf
+
+    # r = requests.post('https://api.datafiniti.co/v4/properties/search',json=request_data,headers=request_headers)
+    # if r.status_code == 200:
+    #     out_data = json.loads(r.content.decode('utf8').replace('"', '\"'))
+    #     print("Founded result: ",out_data['num_found'])
+    #     return out_data['num_found'],out_data['records']
+    # else:
+    #     print('Request failed')
+    #     return None,None
+
+
+
+
+# ## 1. Health Check
+# url = "https://api.datafiniti.co/v4/health"
+# response = requests.request("GET", url, headers=[])
+# print(response.text)
+
+# ## 2. Authentication
+
+# url = "https://api.datafiniti.co/v4/auth"
+# header= {"key": "Content-Type",
+#         "name": "Content-Type",
+#         "type": "text",
+#         "value": "application/json"
+#         }
+# # payload = json.dumps({
+# body = {"mode": "raw","raw": "{\n\t\"email\": \"ar@playpingpong.co\",\n\t\"password\": \"p@ssWord123\"\n\t\n}"}
+# response = requests.request("POST", url, headers=header, data=body)
+# print(response.text)
+
+# query = "country:US AND numRoom:3 AND numFloor:1 AND postalCode:75231 AND {prices.amountMin:>=100} AND {prices.amountMax:<= "+ f"{param}"+ "} AND {prices.currency:USD} "
+
+

app.py

@@ -0,0 +1,107 @@
+import time
+import gradio as gr
+from random import uniform
+from state_handler import state_handler
+
+# with gr.Blocks(css=".gradio-container {text-align: right;}") as demo:
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox()
+    clear = gr.ClearButton([msg, chatbot])
+    user_state = gr.State(value=None)
+    user_info = gr.State(value=None)
+    gathered_info = gr.State(value=None)
+    
+    def user(user_message, history):
+        return gr.update(value=user_message, interactive=True), history + [[user_message, None]]
+
+    def respond(message, chat_history, user_state, user_info,gathered_info):
+        # user_state, bot_message, user_info = state_handler(user_state, message, user_info)
+        user_state,user_info,gathered_info = state_handler(message,user_state, user_info,gathered_info)
+
+        bot_message = user_info
+        
+        time_sleep = uniform(0.004, 0.005) if len(bot_message) < 80 else uniform(0.005, 0.006)
+        chat_history[-1][1] = ""
+        for character in bot_message:
+            chat_history[-1][1] += character
+            time.sleep(time_sleep)
+            yield "", chat_history, user_state, user_info, gathered_info
+
+    msg.submit(
+        user,
+        [msg, chatbot],
+        [msg, chatbot]
+    ).then(
+        respond,
+        [msg, chatbot, user_state, user_info, gathered_info],
+        [msg, chatbot, user_state, user_info, gathered_info]
+    ).then(
+        lambda: gr.update(interactive=True),
+        None,
+        [msg],
+        queue=False
+    )
+
+demo.queue()
+# demo.launch() #
+demo.launch() #
+# demo.launch(server_name='0.0.0.0', server_port=8555,share=True) #
+
+
+
+
+
+
+
+
+
+
+
+
+
+# import gradio as gr
+# import requests
+# import json
+
+# with gr.Blocks(css=".gradio-container {text-align: right;}") as demo:
+#     chatbot = gr.Chatbot()
+#     msg = gr.Textbox()
+#     clear = gr.ClearButton([msg, chatbot])
+#     user_state = gr.State(value=0)
+    
+#     def send_request(msg):
+#         url = "http://localhost:5005/webhooks/rest/webhook"
+#         # url = "http://192.168.1.170:9901/webhooks/rest/webhook"
+
+#         payload = json.dumps({
+#         "sender": "test_user",
+#         "message": msg,
+#         "platform_type": "telegram",
+#         "metadata": {}
+#         })
+#         headers = {
+#         'Content-Type': 'application/json'
+#         }
+
+#         return requests.request("POST", url, headers=headers, data=payload)
+
+#     def respond(message, chat_history):
+#         responses = send_request(message)
+#         if responses.status_code != 200:
+#             return "خطا در اتصال"
+#         responses = responses.json()
+#         bot_message = "\n\n".join([response['text'] for response in responses])
+#         # for response in responses:
+#         #     bot_message = response['text']
+            
+#         chat_history.append((message, bot_message))
+
+#         return "", chat_history
+
+#     msg.submit(respond, [msg, chatbot], [msg, chatbot])    
+
+# demo.launch(
+#     server_name="0.0.0.0",
+#     server_port=8000,
+# )

state_handler.py

@@ -0,0 +1,299 @@
+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 , send_zillow_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
+os.environ["OPENAI_API_KEY"] = "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"
+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 

test_api.py

@@ -0,0 +1,14 @@
+import os
+import requests
+url = "http://0.0.0.0:8000/run_fully_automated?input_image&prompt&room_class"
+
+# Livingroom Payloads
+l_payload1 = {"zipcode" : "19701",
+                "budget":"800K", 
+                "for_sale":"rent"}
+response = requests.request("POST", url, data=l_payload1)
+print(response.text)              
+
+
+
+