Delete screens

screens/chat_bot.py

@@ -1,187 +0,0 @@
-import streamlit as st
-#Import library
-import yaml
-#load config.yml and parse into variables 
-with open("config.yml", "r") as ymlfile:
-    cfg = yaml.safe_load(ymlfile)
-_BARD_API_KEY = cfg["API_KEY"]["Bard"]
-main_path = cfg["LOCAL_PATH"]["main_path"]
-chat_context_length = cfg["CHAT"]["chat_context_length"]
-model_name = cfg["EMBEDDINGS"]["HuggingFaceEmbeddings"]["model_name"]
-model_kwargs = cfg["EMBEDDINGS"]["HuggingFaceEmbeddings"]["model_kwargs"]
-chunk_size = cfg["CHUNK"]["chunk_size"]
-chunk_overlap = cfg["CHUNK"]["chunk_overlap"]
-
-from langchain.vectorstores import Chroma
-import streamlit as st
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.chains import ConversationalRetrievalChain
-from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate
-# Bard
-from bardapi import Bard
-from typing import Any, List, Mapping, Optional
-from langchain.llms.base import LLM
-from langchain.callbacks.manager import CallbackManagerForLLMRun
-
-from streamlit_feedback import streamlit_feedback
-
-
-#define Bard
-class BardLLM(LLM):
-
-    @property
-    def _llm_type(self) -> str:
-        return "custom"
-
-    def _call(
-        self,
-        prompt: str,
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-    ) -> str:
-        response = Bard(token=_BARD_API_KEY).get_answer(prompt)['content']
-        return response
-
-    @property
-    def _identifying_params(self) -> Mapping[str, Any]:
-        """Get the identifying parameters."""
-        return {}
-
-
-
-def load_embeddings(): 
-    embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
-    chroma_index = Chroma(persist_directory=main_path+"/vectorstore/chroma_db", embedding_function=embeddings)
-    print("Successfully loading embeddings and indexing")
-    return chroma_index
-
-
-
-def ask_with_memory(vector_store, question, chat_history=[], document_description=""):
-
-    llm=BardLLM()
-    retriever = vector_store.as_retriever( # now the vs can return documents
-    search_type='similarity', search_kwargs={'k': 3})
- 
-    general_system_template = f"""
-    You are a professional consultant at a real estate consulting company, providing consulting services \
-    to customers on real estate development strategies, real estate news and real estate law.\
-    Your role is to communicate with customer, then interact with them about their concerns about real estates.\
-    Once the customer has been provided their question,\
-    then you obtain some documents about real estate laws or real estate news related to their question.\
-    Then you will examine these documents .\
-    You must provide the answer based on these documents which means\
-    using only the heading and piece of context to answer the questions at the end.\
-    If you don't know the answer just say that you don't know, don't try to make up an answer. \
-    If the question is not in the field of real estate , just answer that you do not know. \
-    You respond in a short, very conversational friendly style.\
-    Answer only in Vietnamese\
-    ----
-    HEADING: ({document_description})
-    CONTEXT: {{context}}
-    ----
-    """
-    general_user_template = """Here is the next question, remember to only answer if you can from the provided context. 
-    If the question is not relevant to real estate , just answer that you do not know, do not create your own answer.
-    Only respond in Vietnamese.
-     QUESTION:```{question}```"""
-
-    messages = [
-                SystemMessagePromptTemplate.from_template(general_system_template),
-                HumanMessagePromptTemplate.from_template(general_user_template)
-    ]
-    qa_prompt = ChatPromptTemplate.from_messages( messages )
-
-
-    crc = ConversationalRetrievalChain.from_llm(llm, retriever, combine_docs_chain_kwargs={'prompt': qa_prompt})
-    result = crc({'question': question, 'chat_history': chat_history})
-    return result
-
-
-def clear_history():
-    if "history" in st.session_state:
-        st.session_state.history = []
-        st.session_state.messages = [] 
-
-# Define a function for submitting feedback
-def _submit_feedback(user_response, emoji=None):
-    st.toast(f"Feedback submitted: {user_response}", icon=emoji)
-    return user_response.update({"some metadata": 123})
-
-
-def format_chat_history(chat_history):
-    formatted_history = ""
-    for entry in chat_history:
-        question, answer = entry
-        # Added an extra '\n' for the blank line
-        formatted_history += f"Question: {question}\nAnswer: {answer}\n\n"
-    return formatted_history
-
-def run_chatbot():
-    with st.sidebar.title("Sidebar"):
-        if st.button("Clear History"):
-            clear_history()
-
-    st.title("🦾 Chatbot (news,law)")
-
-    # Initialize the chatbot and load embeddings
-    if "messages" not in st.session_state:
-        with st.spinner("Initializing, please wait a moment!!!"):
-            st.session_state.vector_store = load_embeddings()
-            st.success("Finish!!!")
-        st.session_state["messages"] = [{"role": "assistant", "content": "Tôi có thể giúp gì được cho bạn?"}]
-
-    messages = st.session_state.messages
-    feedback_kwargs = {
-        "feedback_type": "thumbs",
-        "optional_text_label": "Please provide extra information",
-        "on_submit": _submit_feedback,
-    }
-
-    for n, msg in enumerate(messages):
-        st.chat_message(msg["role"]).write(msg["content"])
-
-        if msg["role"] == "assistant" and n > 1:
-            feedback_key = f"feedback_{int(n/2)}"
-
-            if feedback_key not in st.session_state:
-                st.session_state[feedback_key] = None
-
-            streamlit_feedback(
-                **feedback_kwargs,
-                key=feedback_key,
-            )
-
-
-    chat_history_placeholder = st.empty()
-    if "history" not in st.session_state:
-        st.session_state.history = []
-
-    if prompt := st.chat_input():
-        if "vector_store" in st.session_state:
-            vector_store = st.session_state["vector_store"]
-            
-            q = prompt
-
-            st.session_state.messages.append({"role": "user", "content": prompt})
-            st.chat_message("user").write(prompt)
-            with st.spinner("Thinking..."):
-                response = ask_with_memory(vector_store, q, st.session_state.history)
-
-            if len(st.session_state.history) >= chat_context_length:
-                st.session_state.history = st.session_state.history[1:]
-
-            st.session_state.history.append((q, response['answer']))
-
-            chat_history_str = format_chat_history(st.session_state.history)
-
-            msg = {"role": "assistant", "content": response['answer']}
-            st.session_state.messages.append(msg)
-            st.chat_message("assistant").write(msg["content"])
-
-            # Display the feedback component after the chatbot responds
-            feedback_key = f"feedback_{len(st.session_state.messages) - 1}"
-            streamlit_feedback(
-                **feedback_kwargs,
-                key=feedback_key,
-            )

screens/chat_bot_2.py

@@ -1,184 +0,0 @@
-import streamlit as st
-#Import library
-import yaml
-#load config.yml and parse into variables 
-with open("config2.yml", "r") as ymlfile:
-    cfg = yaml.safe_load(ymlfile)
-_BARD_API_KEY = cfg["API_KEY"]["Bard"]
-main_path = cfg["LOCAL_PATH"]["main_path"]
-chat_context_length = cfg["CHAT"]["chat_context_length"]
-model_name = cfg["EMBEDDINGS"]["HuggingFaceEmbeddings"]["model_name"]
-model_kwargs = cfg["EMBEDDINGS"]["HuggingFaceEmbeddings"]["model_kwargs"]
-chunk_size = cfg["CHUNK"]["chunk_size"]
-chunk_overlap = cfg["CHUNK"]["chunk_overlap"]
-
-import os
-from dotenv import load_dotenv, find_dotenv
-from langchain.vectorstores import Chroma
-import streamlit.components.v1 as components
-import streamlit as st
-import sys
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.chains import ConversationalRetrievalChain
-from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate
-# Bard
-from bardapi import Bard
-from typing import Any, List, Mapping, Optional
-from getpass import getpass
-import os
-from langchain.llms.base import LLM
-from langchain.callbacks.manager import CallbackManagerForLLMRun
-
-from streamlit_feedback import streamlit_feedback
-
-
-#define Bard
-class BardLLM(LLM):
-
-    @property
-    def _llm_type(self) -> str:
-        return "custom"
-
-    def _call(
-        self,
-        prompt: str,
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-    ) -> str:
-        response = Bard(token=_BARD_API_KEY).get_answer(prompt)['content']
-        return response
-
-    @property
-    def _identifying_params(self) -> Mapping[str, Any]:
-        """Get the identifying parameters."""
-        return {}
-
-
-
-def load_embeddings(): 
-    embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
-    chroma_index = Chroma(persist_directory="./chroma_index_1", embedding_function=embeddings)
-    print("Successfully loading embeddings and indexing")
-    return chroma_index
-
-
-
-def ask_with_memory(vector_store, question, chat_history=[], document_description=""):
-
-    llm=BardLLM()
-    retriever = vector_store.as_retriever( # now the vs can return documents
-    search_type='similarity', search_kwargs={'k': 3})
- 
-    general_system_template = f"""
-    You are a helpful and informative bot that answers questions posed below using page_content information from real estate documents.
-    Do not create your own answer, just answer using page_content and metadata information from related documents in Vietnamese.
-    Be sure to respond in a complete sentence, being comprehensive, including all metadata information.
-    Imagine you're talking to a friend and use natural language and phrasing.
-    You can only use Vietnamese do not use other languages.
-    ----
-    CONTEXT: {{context}}
-    ----
-    """
-    general_user_template = """Here is the next question, remember to only answer if you can from the provided context. 
-    If the question is not relevant to real estate , just answer that you do not know, do not create your own answer.
-    Only respond in Vietnamese.
-     QUESTION:```{question}```"""
-
-    messages = [
-                SystemMessagePromptTemplate.from_template(general_system_template),
-                HumanMessagePromptTemplate.from_template(general_user_template)
-    ]
-    qa_prompt = ChatPromptTemplate.from_messages( messages )
-
-
-    crc = ConversationalRetrievalChain.from_llm(llm, retriever, combine_docs_chain_kwargs={'prompt': qa_prompt})
-    result = crc({'question': question, 'chat_history': chat_history})
-    return result
-
-
-def clear_history():
-    if "history" in st.session_state:
-        st.session_state.history = []
-        st.session_state.messages = []
-
-# Define a function for submitting feedback
-def _submit_feedback(user_response, emoji=None):
-    st.toast(f"Feedback submitted: {user_response}", icon=emoji)
-    return user_response.update({"some metadata": 123})
-
-
-def format_chat_history(chat_history):
-    formatted_history = ""
-    for entry in chat_history:
-        question, answer = entry
-        # Added an extra '\n' for the blank line
-        formatted_history += f"Question: {question}\nAnswer: {answer}\n\n"
-    return formatted_history
-
-def run_chatbot_2():
-    with st.sidebar.title("Sidebar"):
-        if st.button("Clear History"):
-            clear_history()
-
-    st.title("🤖 Chatbot (property)")
-
-    # Initialize the chatbot and load embeddings
-    if "messages" not in st.session_state:
-        with st.spinner("Initializing, please wait a moment!!!"):
-            st.session_state.vector_store = load_embeddings()
-            st.success("Finish!!!")
-        st.session_state["messages"] = [{"role": "assistant", "content": "Tôi có thể giúp gì được cho bạn?"}]
-
-    messages = st.session_state.messages
-    feedback_kwargs = {
-        "feedback_type": "thumbs",
-        "optional_text_label": "Please provide extra information",
-        "on_submit": _submit_feedback,
-    }
-
-    for n, msg in enumerate(messages):
-        st.chat_message(msg["role"]).write(msg["content"])
-
-        if msg["role"] == "assistant" and n > 1:
-            feedback_key = f"feedback_{int(n/2)}"
-
-            if feedback_key not in st.session_state:
-                st.session_state[feedback_key] = None
-
-            streamlit_feedback(
-                **feedback_kwargs,
-                key=feedback_key,
-            )
-
-    chat_history_placeholder = st.empty()
-    if "history" not in st.session_state:
-        st.session_state.history = []
-
-    if prompt := st.chat_input():
-        if "vector_store" in st.session_state:
-            vector_store = st.session_state["vector_store"]
-            
-            q = prompt
-
-            st.session_state.messages.append({"role": "user", "content": prompt})
-            st.chat_message("user").write(prompt)
-
-            response = ask_with_memory(vector_store, q, st.session_state.history)
-
-            if len(st.session_state.history) >= chat_context_length:
-                st.session_state.history = st.session_state.history[1:]
-
-            st.session_state.history.append((q, response['answer']))
-
-            chat_history_str = format_chat_history(st.session_state.history)
-
-            msg = {"role": "assistant", "content": response['answer']}
-            st.session_state.messages.append(msg)
-            st.chat_message("assistant").write(msg["content"])
-
-            # Display the feedback component after the chatbot responds
-            feedback_key = f"feedback_{len(st.session_state.messages) - 1}"
-            streamlit_feedback(
-                **feedback_kwargs,
-                key=feedback_key,
-            )

screens/index.py

@@ -1,26 +0,0 @@
-from screens.search import Search_Property
-from screens.chat_bot import run_chatbot
-from screens.chat_bot_2 import run_chatbot_2
-from utils.index import get_hash
-
-def get_routes():
-    screens = [
-        
-        {
-            "component": Search_Property,
-            "name": "Search",
-            "icon": "search"  
-        },
-        {
-            "component": run_chatbot,
-            "name": "Chatbot (news,law)",
-            "icon": "chat"  
-        },
-        {
-            "component": run_chatbot_2,
-            "name": "Chatbot (property)",
-            "icon": "chat"  
-        }
-    ]
-    
-    return get_hash(screens)

screens/search.py

@@ -1,234 +0,0 @@
-import streamlit as st
-import os
-import streamlit.components.v1 as components
-from io import BytesIO
-import requests
-import ast
-
-from langchain import PromptTemplate
-from langchain.chains import RetrievalQA
-from langchain.vectorstores import Chroma
-from langchain.embeddings import SentenceTransformerEmbeddings
-from bardapi import Bard
-from typing import Any, List, Mapping, Optional
-
-os.environ['_BARD_API_KEY'] = "aAhD1NyQqzeoXs8PclDOD_hvEI3N9uHnsn2F0isADM5FFwBfYxatJf1csSUTMo4TXLjOxA."
-
-from langchain.llms.base import LLM
-from langchain.callbacks.manager import CallbackManagerForLLMRun
-class BardLLM(LLM):
-
-
-    @property
-    def _llm_type(self) -> str:
-        return "custom"
-
-    def _call(
-        self,
-        prompt: str,
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-    ) -> str:
-        response = Bard(token=os.environ['_BARD_API_KEY']).get_answer(prompt)['content']
-        return response
-
-    @property
-    def _identifying_params(self) -> Mapping[str, Any]:
-        """Get the identifying parameters."""
-        return {}
-    
-@st.cache_data
-def get_image(url):
-    r = requests.get(url)
-    return BytesIO(r.content)
-
-
-# Define global variables
-embeddings = None
-index = None
-QUESTION_PROMPT = None
-qa = None
-result = []
-
-# Custom session state class for managing pagination
-class SessionState:
-    def __init__(self):
-        self.page_index = 0  # Initialize page index
-        self.database_loaded = False  # Initialize database loaded state
-        self.all_results_displayed = False
-
-# Create a session state object
-session_state = SessionState()
-
-# Define the search function outside of Search_Property
-def display_search_results(result, start_idx, end_idx):
-    if result:
-        st.subheader("Search Results:")
-        for idx in range(start_idx, end_idx):
-            if idx >= len(result):
-                break
-            property_info = result[idx]
-            st.markdown(f"**Result {idx + 1}**")
-            
-            # Display property information
-            image_path_urls = property_info.metadata['Image URL']
-            if image_path_urls is not None and not isinstance(image_path_urls, float):
-                # Convert the string to a Python list
-                imageUrls = ast.literal_eval(image_path_urls)
-
-                # Now, imageUrls is a list of strings
-                st.image(imageUrls[0],width=700)
-
-            st.markdown(f"🏡 {property_info.metadata['Title']}")
-            st.write(f"📍 Address: {property_info.metadata['Location']}")
-            st.markdown(f"💰 Price: {property_info.metadata['Price']} VND | 📏 Size: {property_info.metadata['Area']}")
-            st.markdown(f"📅 Published Date: {property_info.metadata['Time stamp']}") 
-            col3, col4 = st.columns([2, 1]) 
-            with col3: 
-                with st.expander("Full Property Information"):
-                    st.write(f"🏡 Property Title: {property_info.metadata['Title']}")
-                    st.write(f"📏 Size: {property_info.metadata['Area']}")
-                    st.write(f"🏢 Category: {property_info.metadata['Category']}")
-                    st.write(f"📝 Description: {property_info.metadata['Description']}")
-                    st.write(f"💰 Price: {property_info.metadata['Price']} VND")
-                    st.write(f"📅 Date: {property_info.metadata['Time stamp']}")
-                    st.write(f"📍 Address: {property_info.metadata['Location']}")
-                    st.write(f"🆔 ID: {property_info.metadata['ID']}")
-                    if 'Estate type' in property_info.metadata and property_info.metadata['Estate type'] is not None and not isinstance(property_info.metadata['Estate type'], float):
-                        st.write(f"🏠 Housing Type: {property_info.metadata['Estate type']}")
-                    if 'Email' in property_info.metadata and property_info.metadata['Email'] is not None and not isinstance(property_info.metadata['Email'], float):
-                        st.write(f"✉️ Email: {property_info.metadata['Email']}")
-                    if 'Mobile Phone' in property_info.metadata and property_info.metadata['Mobile Phone'] is not None and not isinstance(property_info.metadata['Mobile Phone'], float):
-                        st.write(f"📞 Phone: {property_info.metadata['Mobile Phone']}")
-                    if 'Certification status' in property_info.metadata and property_info.metadata['Certification status'] is not None and not isinstance(property_info.metadata['Certification status'], float):
-                        st.write(f"🏆 Certification status: {property_info.metadata['Certification status']}")
-                    if 'Direction' in property_info.metadata and property_info.metadata['Direction'] is not None and not isinstance(property_info.metadata['Direction'], float):
-                        st.write(f"🧭 Direction: {property_info.metadata['Direction']}")
-                    if 'Rooms' in property_info.metadata and property_info.metadata['Rooms'] is not None and not isinstance(property_info.metadata['Rooms'], float):
-                        st.write(f"🚪 Rooms: {property_info.metadata['Rooms']}")
-                    if 'Bedrooms' in property_info.metadata and property_info.metadata['Bedrooms'] is not None and not isinstance(property_info.metadata['Bedrooms'], float):
-                        st.write(f"🛏️ Bedrooms: {property_info.metadata['Bedrooms']}")
-                    if 'Kitchen' in property_info.metadata and property_info.metadata['Kitchen'] is not None and not isinstance(property_info.metadata['Kitchen'], float):
-                        st.write(f"🍽️ Kitchen: {property_info.metadata['Kitchen']}")
-                    if 'Living room' in property_info.metadata and property_info.metadata['Living room'] is not None and not isinstance(property_info.metadata['Living room'], float):
-                        st.write(f"🛋️ Living room: {property_info.metadata['Living room']}")
-                    if 'Bathrooms' in property_info.metadata and property_info.metadata['Bathrooms'] is not None and not isinstance(property_info.metadata['Bathrooms'], float):
-                        st.write(f"🚽 Bathrooms: {property_info.metadata['Bathrooms']}")
-                    if 'Front width' in property_info.metadata and property_info.metadata['Front width'] is not None and not isinstance(property_info.metadata['Front width'], float):
-                        st.write(f"📐 Front width: {property_info.metadata['Front width']}")
-                    if 'Floor' in property_info.metadata and property_info.metadata['Floor'] is not None and not isinstance(property_info.metadata['Floor'], float):
-                        st.write(f"🧱 Floor: {property_info.metadata['Floor']}")
-                    if 'Parking Slot' in property_info.metadata and property_info.metadata['Parking Slot'] is not None and not isinstance(property_info.metadata['Parking Slot'], float):
-                        st.write(f"🚗 Parking Slot: {property_info.metadata['Parking Slot']}")
-                    if 'Seller name' in property_info.metadata and property_info.metadata['Seller name'] is not None and not isinstance(property_info.metadata['Seller name'], float):
-                        st.write(f"👤 Seller Name: {property_info.metadata['Seller name']}")
-                    if 'Seller type' in property_info.metadata and property_info.metadata['Seller type'] is not None and not isinstance(property_info.metadata['Seller type'], float):
-                        st.write(f"👨‍💼 Seller type: {property_info.metadata['Seller type']}")
-                    if 'Seller Address' in property_info.metadata and property_info.metadata['Seller Address'] is not None and not isinstance(property_info.metadata['Seller Address'], float):
-                        st.write(f"📌 Seller Address: {property_info.metadata['Seller Address']}")
-                    if 'Balcony Direction' in property_info.metadata and property_info.metadata['Balcony Direction'] is not None and not isinstance(property_info.metadata['Balcony Direction'], float):
-                        st.write(f"🌄 Balcony Direction: {property_info.metadata['Balcony Direction']}")
-                    if 'Furniture' in property_info.metadata and property_info.metadata['Furniture'] is not None and not isinstance(property_info.metadata['Furniture'], float):
-                        st.write(f"🛋️ Furniture: {property_info.metadata['Furniture']}")
-                    if 'Toilet' in property_info.metadata and property_info.metadata['Toilet'] is not None and not isinstance(property_info.metadata['Toilet'], float):
-                        st.write(f"🚽 Toilet: {property_info.metadata['Toilet']}")                
-
-            with col4:
-                st.empty()
-
-            imageCarouselComponent = components.declare_component("image-carousel-component", path="frontend/public")
-            image_path_urls = property_info.metadata['Image URL']
-            if image_path_urls is not None and not isinstance(image_path_urls, float):
-                # Convert the string to a Python list
-                imageUrls = ast.literal_eval(image_path_urls)
-                if len(imageUrls) > 1:
-                    selectedImageUrl = imageCarouselComponent(imageUrls=imageUrls, height=200)
-                    if selectedImageUrl is not None:
-                        st.image(selectedImageUrl)
-
-            # Add a divider after displaying property info
-            st.markdown("<hr style='border: 2px solid white'>", unsafe_allow_html=True)  # Horizontal rule as a divider
-            
-
-                    
-
-def Search_Property():
-    global embeddings, index, result, QUESTION_PROMPT, qa
-
-    st.title("🏘️ Property Search ")
-    # Load data and create the search
-    if not session_state.database_loaded:
-        st.info("Loading database... This may take a moment.")
-        embeddings = SentenceTransformerEmbeddings(model_name="keepitreal/vietnamese-sbert")
-        # Create a Chroma object with persistence
-        db = Chroma(persist_directory="./chroma_index_1", embedding_function=embeddings)
-        # Get documents from the database
-        db.get()
-        llm=BardLLM()
-        qa = RetrievalQA.from_chain_type(
-        llm=llm,
-        chain_type="stuff",
-        retriever=db.as_retriever(search_type="similarity", search_kwargs={"k":4}),
-        return_source_documents=True)
-        question_template = """
-        Context: You are a helpful and informative bot that answers questions posed below using page_content information from real estate documents.
-        Do not create your own answer, just answer using page_content and metadata information from related documents in Vietnamese.
-        Be sure to respond in a complete sentence, being comprehensive, including all metadata information.
-        Imagine you're talking to a friend and use natural language and phrasing.
-        You can only use Vietnamese do not use other languages.
-
-        QUESTION: '{question}'
-
-        ANSWER:
-        """
-        QUESTION_PROMPT = PromptTemplate(
-            template=question_template, input_variables=["question"]
-        )   
-        session_state.database_loaded = True
-
-    if session_state.database_loaded:
-        col1, col2 = st.columns([2, 1])  # Create a two-column layout
-
-        with col1:
-            query = st.text_input("Enter your property search query:")
-            search_button = st.button("Search", help="Click to start the search")
-
-            if search_button:
-                with st.spinner("Searching..."):
-                    if query is not None:  # Check if model_embedding is not None
-                        qa.combine_documents_chain.llm_chain.prompt = QUESTION_PROMPT
-                        qa.combine_documents_chain.verbose = True
-                        qa.return_source_documents = True
-                        results = qa({"query":query,})
-                        result = results["source_documents"]
-                        session_state.page_index = 0  # Reset page index when a new search is performed
-                    
-        with col2:
-            if len(result) > 0:
-                st.write(f'Total Results: {len(result)} properties found.')  # Display "Total Results" in the second column
-
-        if result:   
-            N = 5 
-            prev_button, next_button = st.columns([4,1])
-            last_page = len(result) // N
-
-            
-            # Update page index based on button clicks
-            if prev_button.button("Previous", key="prev_button"):
-                if session_state.page_index - 1 < 0:
-                    session_state.page_index = last_page
-                else:
-                    session_state.page_index -= 1
-
-            if next_button.button("Next", key="next_button"):
-                if session_state.page_index > last_page:
-                    session_state.page_index = 0
-                else:
-                    session_state.page_index += 1
-
-            # Calculate the range of results to display (5 properties at a time)
-            start_idx = session_state.page_index * N 
-            end_idx = (1 + session_state.page_index) * N
-
-            # Display results for the current page
-            display_search_results(result, start_idx, end_idx)