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5a1e954
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Parent(s):
fd9c582
Upload 7 files
Browse files- screens/__pycache__/chat_bot.cpython-311.pyc +0 -0
- screens/__pycache__/chat_bot_2.cpython-311.pyc +0 -0
- screens/__pycache__/search.cpython-311.pyc +0 -0
- screens/chat_bot.py +187 -0
- screens/chat_bot_2.py +184 -0
- screens/index.py +26 -0
- screens/search.py +234 -0
screens/__pycache__/chat_bot.cpython-311.pyc
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Binary file (10.7 kB). View file
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screens/__pycache__/chat_bot_2.cpython-311.pyc
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Binary file (10.2 kB). View file
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screens/__pycache__/search.cpython-311.pyc
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Binary file (17.9 kB). View file
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screens/chat_bot.py
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@@ -0,0 +1,187 @@
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1 |
+
import streamlit as st
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2 |
+
#Import library
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3 |
+
import yaml
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4 |
+
#load config.yml and parse into variables
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5 |
+
with open("config.yml", "r") as ymlfile:
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6 |
+
cfg = yaml.safe_load(ymlfile)
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7 |
+
_BARD_API_KEY = cfg["API_KEY"]["Bard"]
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8 |
+
main_path = cfg["LOCAL_PATH"]["main_path"]
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9 |
+
chat_context_length = cfg["CHAT"]["chat_context_length"]
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10 |
+
model_name = cfg["EMBEDDINGS"]["HuggingFaceEmbeddings"]["model_name"]
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11 |
+
model_kwargs = cfg["EMBEDDINGS"]["HuggingFaceEmbeddings"]["model_kwargs"]
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+
chunk_size = cfg["CHUNK"]["chunk_size"]
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13 |
+
chunk_overlap = cfg["CHUNK"]["chunk_overlap"]
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+
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+
from langchain.vectorstores import Chroma
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16 |
+
import streamlit as st
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+
from langchain.embeddings import HuggingFaceEmbeddings
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18 |
+
from langchain.chains import ConversationalRetrievalChain
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+
from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate
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+
# Bard
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21 |
+
from bardapi import Bard
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22 |
+
from typing import Any, List, Mapping, Optional
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+
from langchain.llms.base import LLM
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24 |
+
from langchain.callbacks.manager import CallbackManagerForLLMRun
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+
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26 |
+
from streamlit_feedback import streamlit_feedback
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+
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+
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+
#define Bard
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30 |
+
class BardLLM(LLM):
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+
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+
@property
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+
def _llm_type(self) -> str:
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+
return "custom"
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+
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36 |
+
def _call(
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+
self,
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+
prompt: str,
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39 |
+
stop: Optional[List[str]] = None,
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40 |
+
run_manager: Optional[CallbackManagerForLLMRun] = None,
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41 |
+
) -> str:
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42 |
+
response = Bard(token=_BARD_API_KEY).get_answer(prompt)['content']
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43 |
+
return response
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44 |
+
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45 |
+
@property
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46 |
+
def _identifying_params(self) -> Mapping[str, Any]:
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47 |
+
"""Get the identifying parameters."""
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48 |
+
return {}
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49 |
+
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50 |
+
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51 |
+
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52 |
+
def load_embeddings():
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53 |
+
embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
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54 |
+
chroma_index = Chroma(persist_directory=main_path+"/vectorstore/chroma_db", embedding_function=embeddings)
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55 |
+
print("Successfully loading embeddings and indexing")
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56 |
+
return chroma_index
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57 |
+
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+
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59 |
+
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60 |
+
def ask_with_memory(vector_store, question, chat_history_1=[], document_description=""):
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61 |
+
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+
llm=BardLLM()
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63 |
+
retriever = vector_store.as_retriever( # now the vs can return documents
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64 |
+
search_type='similarity', search_kwargs={'k': 3})
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65 |
+
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66 |
+
general_system_template = f"""
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67 |
+
You are a professional consultant at a real estate consulting company, providing consulting services \
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68 |
+
to customers on real estate development strategies, real estate news and real estate law.\
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69 |
+
Your role is to communicate with customer, then interact with them about their concerns about real estates.\
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70 |
+
Once the customer has been provided their question,\
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+
then you obtain some documents about real estate laws or real estate news related to their question.\
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+
Then you will examine these documents .\
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+
You must provide the answer based on these documents which means\
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+
using only the heading and piece of context to answer the questions at the end.\
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75 |
+
If you don't know the answer just say that you don't know, don't try to make up an answer. \
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76 |
+
If the question is not in the field of real estate , just answer that you do not know. \
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77 |
+
You respond in a short, very conversational friendly style.\
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78 |
+
Answer only in Vietnamese\
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79 |
+
----
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80 |
+
HEADING: ({document_description})
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81 |
+
CONTEXT: {{context}}
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82 |
+
----
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83 |
+
"""
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84 |
+
general_user_template = """Here is the next question, remember to only answer if you can from the provided context.
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+
If the question is not relevant to real estate , just answer that you do not know, do not create your own answer.
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Only respond in Vietnamese.
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+
QUESTION:```{question}```"""
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+
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89 |
+
messages_1 = [
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90 |
+
SystemMessagePromptTemplate.from_template(general_system_template),
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91 |
+
HumanMessagePromptTemplate.from_template(general_user_template)
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+
]
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93 |
+
qa_prompt = ChatPromptTemplate.from_messages( messages_1 )
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94 |
+
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+
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96 |
+
crc = ConversationalRetrievalChain.from_llm(llm, retriever, combine_docs_chain_kwargs={'prompt': qa_prompt})
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+
result = crc({'question': question, 'chat_history': chat_history_1})
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+
return result
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99 |
+
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+
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101 |
+
def clear_history():
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102 |
+
if "history_1" in st.session_state:
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103 |
+
st.session_state.history_1 = []
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104 |
+
st.session_state.messages_1 = []
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105 |
+
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106 |
+
# Define a function for submitting feedback
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107 |
+
def _submit_feedback(user_response, emoji=None):
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108 |
+
st.toast(f"Feedback submitted: {user_response}", icon=emoji)
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109 |
+
return user_response.update({"some metadata": 123})
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110 |
+
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111 |
+
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112 |
+
def format_chat_history(chat_history_1):
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113 |
+
formatted_history = ""
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114 |
+
for entry in chat_history_1:
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115 |
+
question, answer = entry
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116 |
+
# Added an extra '\n' for the blank line
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117 |
+
formatted_history += f"Question: {question}\nAnswer: {answer}\n\n"
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118 |
+
return formatted_history
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119 |
+
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120 |
+
def run_chatbot():
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121 |
+
with st.sidebar.title("Sidebar"):
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122 |
+
if st.button("Clear History"):
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123 |
+
clear_history()
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124 |
+
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125 |
+
st.title("🦾 Chatbot (news,law)")
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126 |
+
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127 |
+
# Initialize the chatbot and load embeddings
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128 |
+
if "messages_1" not in st.session_state:
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129 |
+
with st.spinner("Initializing, please wait a moment!!!"):
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130 |
+
st.session_state.vector_store = load_embeddings()
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131 |
+
st.success("Finish!!!")
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132 |
+
st.session_state["messages_1"] = [{"role": "assistant", "content": "Tôi có thể giúp gì được cho bạn?"}]
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133 |
+
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134 |
+
messages_1 = st.session_state.messages_1
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135 |
+
feedback_kwargs = {
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136 |
+
"feedback_type": "thumbs",
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137 |
+
"optional_text_label": "Please provide extra information",
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138 |
+
"on_submit": _submit_feedback,
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139 |
+
}
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140 |
+
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141 |
+
for n, msg in enumerate(messages_1):
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142 |
+
st.chat_message(msg["role"]).write(msg["content"])
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143 |
+
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144 |
+
if msg["role"] == "assistant" and n > 1:
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145 |
+
feedback_key = f"feedback_{int(n/2)}"
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146 |
+
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147 |
+
if feedback_key not in st.session_state:
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148 |
+
st.session_state[feedback_key] = None
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149 |
+
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150 |
+
streamlit_feedback(
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151 |
+
**feedback_kwargs,
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152 |
+
key=feedback_key,
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153 |
+
)
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154 |
+
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155 |
+
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156 |
+
chat_history_placeholder = st.empty()
|
157 |
+
if "history_1" not in st.session_state:
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158 |
+
st.session_state.history_1 = []
|
159 |
+
|
160 |
+
if prompt := st.chat_input():
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161 |
+
if "vector_store" in st.session_state:
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162 |
+
vector_store = st.session_state["vector_store"]
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163 |
+
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164 |
+
q = prompt
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165 |
+
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166 |
+
st.session_state.messages_1.append({"role": "user", "content": prompt})
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167 |
+
st.chat_message("user").write(prompt)
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168 |
+
with st.spinner("Thinking..."):
|
169 |
+
response = ask_with_memory(vector_store, q, st.session_state.history_1)
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170 |
+
|
171 |
+
if len(st.session_state.history_1) >= chat_context_length:
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172 |
+
st.session_state.history_1 = st.session_state.history_1[1:]
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173 |
+
|
174 |
+
st.session_state.history_1.append((q, response['answer']))
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175 |
+
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176 |
+
chat_history_str = format_chat_history(st.session_state.history_1)
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177 |
+
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178 |
+
msg = {"role": "assistant", "content": response['answer']}
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179 |
+
st.session_state.messages_1.append(msg)
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180 |
+
st.chat_message("assistant").write(msg["content"])
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181 |
+
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182 |
+
# Display the feedback component after the chatbot responds
|
183 |
+
feedback_key = f"feedback_{len(st.session_state.messages_1) - 1}"
|
184 |
+
streamlit_feedback(
|
185 |
+
**feedback_kwargs,
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186 |
+
key=feedback_key,
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187 |
+
)
|
screens/chat_bot_2.py
ADDED
@@ -0,0 +1,184 @@
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|
1 |
+
import streamlit as st
|
2 |
+
#Import library
|
3 |
+
import yaml
|
4 |
+
#load config.yml and parse into variables
|
5 |
+
with open("config2.yml", "r") as ymlfile:
|
6 |
+
cfg = yaml.safe_load(ymlfile)
|
7 |
+
_BARD_API_KEY = cfg["API_KEY"]["Bard"]
|
8 |
+
main_path = cfg["LOCAL_PATH"]["main_path"]
|
9 |
+
chat_context_length = cfg["CHAT"]["chat_context_length"]
|
10 |
+
model_name = cfg["EMBEDDINGS"]["HuggingFaceEmbeddings"]["model_name"]
|
11 |
+
model_kwargs = cfg["EMBEDDINGS"]["HuggingFaceEmbeddings"]["model_kwargs"]
|
12 |
+
chunk_size = cfg["CHUNK"]["chunk_size"]
|
13 |
+
chunk_overlap = cfg["CHUNK"]["chunk_overlap"]
|
14 |
+
|
15 |
+
import os
|
16 |
+
from dotenv import load_dotenv, find_dotenv
|
17 |
+
from langchain.vectorstores import Chroma
|
18 |
+
import streamlit.components.v1 as components
|
19 |
+
import streamlit as st
|
20 |
+
import sys
|
21 |
+
from langchain.embeddings import HuggingFaceEmbeddings
|
22 |
+
from langchain.chains import ConversationalRetrievalChain
|
23 |
+
from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate
|
24 |
+
# Bard
|
25 |
+
from bardapi import Bard
|
26 |
+
from typing import Any, List, Mapping, Optional
|
27 |
+
from getpass import getpass
|
28 |
+
import os
|
29 |
+
from langchain.llms.base import LLM
|
30 |
+
from langchain.callbacks.manager import CallbackManagerForLLMRun
|
31 |
+
|
32 |
+
from streamlit_feedback import streamlit_feedback
|
33 |
+
|
34 |
+
|
35 |
+
#define Bard
|
36 |
+
class BardLLM(LLM):
|
37 |
+
|
38 |
+
@property
|
39 |
+
def _llm_type(self) -> str:
|
40 |
+
return "custom"
|
41 |
+
|
42 |
+
def _call(
|
43 |
+
self,
|
44 |
+
prompt: str,
|
45 |
+
stop: Optional[List[str]] = None,
|
46 |
+
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
47 |
+
) -> str:
|
48 |
+
response = Bard(token=_BARD_API_KEY).get_answer(prompt)['content']
|
49 |
+
return response
|
50 |
+
|
51 |
+
@property
|
52 |
+
def _identifying_params(self) -> Mapping[str, Any]:
|
53 |
+
"""Get the identifying parameters."""
|
54 |
+
return {}
|
55 |
+
|
56 |
+
|
57 |
+
|
58 |
+
def load_embeddings():
|
59 |
+
embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
|
60 |
+
chroma_index = Chroma(persist_directory="./chroma_index_1", embedding_function=embeddings)
|
61 |
+
print("Successfully loading embeddings and indexing")
|
62 |
+
return chroma_index
|
63 |
+
|
64 |
+
|
65 |
+
|
66 |
+
def ask_with_memory(vector_store, question, chat_history=[], document_description=""):
|
67 |
+
|
68 |
+
llm=BardLLM()
|
69 |
+
retriever = vector_store.as_retriever( # now the vs can return documents
|
70 |
+
search_type='similarity', search_kwargs={'k': 3})
|
71 |
+
|
72 |
+
general_system_template = f"""
|
73 |
+
You are a helpful and informative bot that answers questions posed below using provided context.
|
74 |
+
You have to be truthful. Do not recommend or propose any infomation of the properties.
|
75 |
+
Be sure to respond in a complete sentence, being comprehensive, including all information in the provided context.
|
76 |
+
Imagine you're talking to a friend and use natural language and phrasing.
|
77 |
+
You can only use Vietnamese do not use other languages.
|
78 |
+
----
|
79 |
+
CONTEXT: {{context}}
|
80 |
+
----
|
81 |
+
"""
|
82 |
+
general_user_template = """Here is the next question, remember to only answer if you can from the provided context.
|
83 |
+
If the question is not relevant to real estate , just answer that you do not know, do not create your own answer.
|
84 |
+
Only respond in Vietnamese.
|
85 |
+
QUESTION:```{question}```"""
|
86 |
+
|
87 |
+
messages = [
|
88 |
+
SystemMessagePromptTemplate.from_template(general_system_template),
|
89 |
+
HumanMessagePromptTemplate.from_template(general_user_template)
|
90 |
+
]
|
91 |
+
qa_prompt = ChatPromptTemplate.from_messages( messages )
|
92 |
+
|
93 |
+
|
94 |
+
crc = ConversationalRetrievalChain.from_llm(llm, retriever, combine_docs_chain_kwargs={'prompt': qa_prompt})
|
95 |
+
result = crc({'question': question, 'chat_history': chat_history})
|
96 |
+
return result
|
97 |
+
|
98 |
+
|
99 |
+
def clear_history():
|
100 |
+
if "history" in st.session_state:
|
101 |
+
st.session_state.history = []
|
102 |
+
st.session_state.messages = []
|
103 |
+
|
104 |
+
# Define a function for submitting feedback
|
105 |
+
def _submit_feedback(user_response, emoji=None):
|
106 |
+
st.toast(f"Feedback submitted: {user_response}", icon=emoji)
|
107 |
+
return user_response.update({"some metadata": 123})
|
108 |
+
|
109 |
+
|
110 |
+
def format_chat_history(chat_history):
|
111 |
+
formatted_history = ""
|
112 |
+
for entry in chat_history:
|
113 |
+
question, answer = entry
|
114 |
+
# Added an extra '\n' for the blank line
|
115 |
+
formatted_history += f"Question: {question}\nAnswer: {answer}\n\n"
|
116 |
+
return formatted_history
|
117 |
+
|
118 |
+
def run_chatbot_2():
|
119 |
+
with st.sidebar.title("Sidebar"):
|
120 |
+
if st.button("Clear History"):
|
121 |
+
clear_history()
|
122 |
+
|
123 |
+
st.title("🤖 Chatbot (property)")
|
124 |
+
|
125 |
+
# Initialize the chatbot and load embeddings
|
126 |
+
if "messages" not in st.session_state:
|
127 |
+
with st.spinner("Initializing, please wait a moment!!!"):
|
128 |
+
st.session_state.vector_store = load_embeddings()
|
129 |
+
st.success("Finish!!!")
|
130 |
+
st.session_state["messages"] = [{"role": "assistant", "content": "Tôi có thể giúp gì được cho bạn?"}]
|
131 |
+
|
132 |
+
messages = st.session_state.messages
|
133 |
+
feedback_kwargs = {
|
134 |
+
"feedback_type": "thumbs",
|
135 |
+
"optional_text_label": "Please provide extra information",
|
136 |
+
"on_submit": _submit_feedback,
|
137 |
+
}
|
138 |
+
|
139 |
+
for n, msg in enumerate(messages):
|
140 |
+
st.chat_message(msg["role"]).write(msg["content"])
|
141 |
+
|
142 |
+
if msg["role"] == "assistant" and n > 1:
|
143 |
+
feedback_key = f"feedback_{int(n/2)}"
|
144 |
+
|
145 |
+
if feedback_key not in st.session_state:
|
146 |
+
st.session_state[feedback_key] = None
|
147 |
+
|
148 |
+
streamlit_feedback(
|
149 |
+
**feedback_kwargs,
|
150 |
+
key=feedback_key,
|
151 |
+
)
|
152 |
+
|
153 |
+
chat_history_placeholder = st.empty()
|
154 |
+
if "history" not in st.session_state:
|
155 |
+
st.session_state.history = []
|
156 |
+
|
157 |
+
if prompt := st.chat_input():
|
158 |
+
if "vector_store" in st.session_state:
|
159 |
+
vector_store = st.session_state["vector_store"]
|
160 |
+
|
161 |
+
q = prompt
|
162 |
+
|
163 |
+
st.session_state.messages.append({"role": "user", "content": prompt})
|
164 |
+
st.chat_message("user").write(prompt)
|
165 |
+
|
166 |
+
response = ask_with_memory(vector_store, q, st.session_state.history)
|
167 |
+
|
168 |
+
if len(st.session_state.history) >= chat_context_length:
|
169 |
+
st.session_state.history = st.session_state.history[1:]
|
170 |
+
|
171 |
+
st.session_state.history.append((q, response['answer']))
|
172 |
+
|
173 |
+
chat_history_str = format_chat_history(st.session_state.history)
|
174 |
+
|
175 |
+
msg = {"role": "assistant", "content": response['answer']}
|
176 |
+
st.session_state.messages.append(msg)
|
177 |
+
st.chat_message("assistant").write(msg["content"])
|
178 |
+
|
179 |
+
# Display the feedback component after the chatbot responds
|
180 |
+
feedback_key = f"feedback_{len(st.session_state.messages) - 1}"
|
181 |
+
streamlit_feedback(
|
182 |
+
**feedback_kwargs,
|
183 |
+
key=feedback_key,
|
184 |
+
)
|
screens/index.py
ADDED
@@ -0,0 +1,26 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from screens.search import Search_Property
|
2 |
+
from screens.chat_bot import run_chatbot
|
3 |
+
from screens.chat_bot_2 import run_chatbot_2
|
4 |
+
from utils.index import get_hash
|
5 |
+
|
6 |
+
def get_routes():
|
7 |
+
screens = [
|
8 |
+
|
9 |
+
{
|
10 |
+
"component": Search_Property,
|
11 |
+
"name": "Search",
|
12 |
+
"icon": "search"
|
13 |
+
},
|
14 |
+
{
|
15 |
+
"component": run_chatbot,
|
16 |
+
"name": "Chatbot (news,law)",
|
17 |
+
"icon": "chat"
|
18 |
+
},
|
19 |
+
{
|
20 |
+
"component": run_chatbot_2,
|
21 |
+
"name": "Chatbot (property)",
|
22 |
+
"icon": "chat"
|
23 |
+
}
|
24 |
+
]
|
25 |
+
|
26 |
+
return get_hash(screens)
|
screens/search.py
ADDED
@@ -0,0 +1,234 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import streamlit as st
|
2 |
+
import os
|
3 |
+
import streamlit.components.v1 as components
|
4 |
+
from io import BytesIO
|
5 |
+
import requests
|
6 |
+
import ast
|
7 |
+
|
8 |
+
from langchain import PromptTemplate
|
9 |
+
from langchain.chains import RetrievalQA
|
10 |
+
from langchain.vectorstores import Chroma
|
11 |
+
from langchain.embeddings import SentenceTransformerEmbeddings
|
12 |
+
from bardapi import Bard
|
13 |
+
from typing import Any, List, Mapping, Optional
|
14 |
+
|
15 |
+
os.environ['_BARD_API_KEY'] = "aAhD1NyQqzeoXs8PclDOD_hvEI3N9uHnsn2F0isADM5FFwBfYxatJf1csSUTMo4TXLjOxA."
|
16 |
+
|
17 |
+
from langchain.llms.base import LLM
|
18 |
+
from langchain.callbacks.manager import CallbackManagerForLLMRun
|
19 |
+
class BardLLM(LLM):
|
20 |
+
|
21 |
+
|
22 |
+
@property
|
23 |
+
def _llm_type(self) -> str:
|
24 |
+
return "custom"
|
25 |
+
|
26 |
+
def _call(
|
27 |
+
self,
|
28 |
+
prompt: str,
|
29 |
+
stop: Optional[List[str]] = None,
|
30 |
+
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
31 |
+
) -> str:
|
32 |
+
response = Bard(token=os.environ['_BARD_API_KEY']).get_answer(prompt)['content']
|
33 |
+
return response
|
34 |
+
|
35 |
+
@property
|
36 |
+
def _identifying_params(self) -> Mapping[str, Any]:
|
37 |
+
"""Get the identifying parameters."""
|
38 |
+
return {}
|
39 |
+
|
40 |
+
@st.cache_data
|
41 |
+
def get_image(url):
|
42 |
+
r = requests.get(url)
|
43 |
+
return BytesIO(r.content)
|
44 |
+
|
45 |
+
|
46 |
+
# Define global variables
|
47 |
+
embeddings = None
|
48 |
+
index = None
|
49 |
+
QUESTION_PROMPT = None
|
50 |
+
qa = None
|
51 |
+
result = []
|
52 |
+
|
53 |
+
# Custom session state class for managing pagination
|
54 |
+
class SessionState:
|
55 |
+
def __init__(self):
|
56 |
+
self.page_index = 0 # Initialize page index
|
57 |
+
self.database_loaded = False # Initialize database loaded state
|
58 |
+
self.all_results_displayed = False
|
59 |
+
|
60 |
+
# Create a session state object
|
61 |
+
session_state = SessionState()
|
62 |
+
|
63 |
+
# Define the search function outside of Search_Property
|
64 |
+
def display_search_results(result, start_idx, end_idx):
|
65 |
+
if result:
|
66 |
+
st.subheader("Search Results:")
|
67 |
+
for idx in range(start_idx, end_idx):
|
68 |
+
if idx >= len(result):
|
69 |
+
break
|
70 |
+
property_info = result[idx]
|
71 |
+
st.markdown(f"**Result {idx + 1}**")
|
72 |
+
|
73 |
+
# Display property information
|
74 |
+
image_path_urls = property_info.metadata['Image URL']
|
75 |
+
if image_path_urls is not None and not isinstance(image_path_urls, float):
|
76 |
+
# Convert the string to a Python list
|
77 |
+
imageUrls = ast.literal_eval(image_path_urls)
|
78 |
+
|
79 |
+
# Now, imageUrls is a list of strings
|
80 |
+
st.image(imageUrls[0],width=700)
|
81 |
+
|
82 |
+
st.markdown(f"🏡 {property_info.metadata['Title']}")
|
83 |
+
st.write(f"📍 Address: {property_info.metadata['Location']}")
|
84 |
+
st.markdown(f"💰 Price: {property_info.metadata['Price']} VND | 📏 Size: {property_info.metadata['Area']}")
|
85 |
+
st.markdown(f"📅 Published Date: {property_info.metadata['Time stamp']}")
|
86 |
+
col3, col4 = st.columns([2, 1])
|
87 |
+
with col3:
|
88 |
+
with st.expander("Full Property Information"):
|
89 |
+
st.write(f"🏡 Property Title: {property_info.metadata['Title']}")
|
90 |
+
st.write(f"📏 Size: {property_info.metadata['Area']}")
|
91 |
+
st.write(f"🏢 Category: {property_info.metadata['Category']}")
|
92 |
+
st.write(f"📝 Description: {property_info.metadata['Description']}")
|
93 |
+
st.write(f"💰 Price: {property_info.metadata['Price']} VND")
|
94 |
+
st.write(f"📅 Date: {property_info.metadata['Time stamp']}")
|
95 |
+
st.write(f"📍 Address: {property_info.metadata['Location']}")
|
96 |
+
st.write(f"🆔 ID: {property_info.metadata['ID']}")
|
97 |
+
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):
|
98 |
+
st.write(f"🏠 Housing Type: {property_info.metadata['Estate type']}")
|
99 |
+
if 'Email' in property_info.metadata and property_info.metadata['Email'] is not None and not isinstance(property_info.metadata['Email'], float):
|
100 |
+
st.write(f"✉️ Email: {property_info.metadata['Email']}")
|
101 |
+
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):
|
102 |
+
st.write(f"📞 Phone: {property_info.metadata['Mobile Phone']}")
|
103 |
+
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):
|
104 |
+
st.write(f"🏆 Certification status: {property_info.metadata['Certification status']}")
|
105 |
+
if 'Direction' in property_info.metadata and property_info.metadata['Direction'] is not None and not isinstance(property_info.metadata['Direction'], float):
|
106 |
+
st.write(f"🧭 Direction: {property_info.metadata['Direction']}")
|
107 |
+
if 'Rooms' in property_info.metadata and property_info.metadata['Rooms'] is not None and not isinstance(property_info.metadata['Rooms'], float):
|
108 |
+
st.write(f"🚪 Rooms: {property_info.metadata['Rooms']}")
|
109 |
+
if 'Bedrooms' in property_info.metadata and property_info.metadata['Bedrooms'] is not None and not isinstance(property_info.metadata['Bedrooms'], float):
|
110 |
+
st.write(f"🛏️ Bedrooms: {property_info.metadata['Bedrooms']}")
|
111 |
+
if 'Kitchen' in property_info.metadata and property_info.metadata['Kitchen'] is not None and not isinstance(property_info.metadata['Kitchen'], float):
|
112 |
+
st.write(f"🍽️ Kitchen: {property_info.metadata['Kitchen']}")
|
113 |
+
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):
|
114 |
+
st.write(f"🛋️ Living room: {property_info.metadata['Living room']}")
|
115 |
+
if 'Bathrooms' in property_info.metadata and property_info.metadata['Bathrooms'] is not None and not isinstance(property_info.metadata['Bathrooms'], float):
|
116 |
+
st.write(f"🚽 Bathrooms: {property_info.metadata['Bathrooms']}")
|
117 |
+
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):
|
118 |
+
st.write(f"📐 Front width: {property_info.metadata['Front width']}")
|
119 |
+
if 'Floor' in property_info.metadata and property_info.metadata['Floor'] is not None and not isinstance(property_info.metadata['Floor'], float):
|
120 |
+
st.write(f"🧱 Floor: {property_info.metadata['Floor']}")
|
121 |
+
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):
|
122 |
+
st.write(f"🚗 Parking Slot: {property_info.metadata['Parking Slot']}")
|
123 |
+
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):
|
124 |
+
st.write(f"👤 Seller Name: {property_info.metadata['Seller name']}")
|
125 |
+
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):
|
126 |
+
st.write(f"👨💼 Seller type: {property_info.metadata['Seller type']}")
|
127 |
+
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):
|
128 |
+
st.write(f"📌 Seller Address: {property_info.metadata['Seller Address']}")
|
129 |
+
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):
|
130 |
+
st.write(f"🌄 Balcony Direction: {property_info.metadata['Balcony Direction']}")
|
131 |
+
if 'Furniture' in property_info.metadata and property_info.metadata['Furniture'] is not None and not isinstance(property_info.metadata['Furniture'], float):
|
132 |
+
st.write(f"🛋️ Furniture: {property_info.metadata['Furniture']}")
|
133 |
+
if 'Toilet' in property_info.metadata and property_info.metadata['Toilet'] is not None and not isinstance(property_info.metadata['Toilet'], float):
|
134 |
+
st.write(f"🚽 Toilet: {property_info.metadata['Toilet']}")
|
135 |
+
|
136 |
+
with col4:
|
137 |
+
st.empty()
|
138 |
+
|
139 |
+
imageCarouselComponent = components.declare_component("image-carousel-component", path="frontend/public")
|
140 |
+
image_path_urls = property_info.metadata['Image URL']
|
141 |
+
if image_path_urls is not None and not isinstance(image_path_urls, float):
|
142 |
+
# Convert the string to a Python list
|
143 |
+
imageUrls = ast.literal_eval(image_path_urls)
|
144 |
+
if len(imageUrls) > 1:
|
145 |
+
selectedImageUrl = imageCarouselComponent(imageUrls=imageUrls, height=200)
|
146 |
+
if selectedImageUrl is not None:
|
147 |
+
st.image(selectedImageUrl)
|
148 |
+
|
149 |
+
# Add a divider after displaying property info
|
150 |
+
st.markdown("<hr style='border: 2px solid white'>", unsafe_allow_html=True) # Horizontal rule as a divider
|
151 |
+
|
152 |
+
|
153 |
+
|
154 |
+
|
155 |
+
def Search_Property():
|
156 |
+
global embeddings, index, result, QUESTION_PROMPT, qa
|
157 |
+
|
158 |
+
st.title("🏘️ Property Search ")
|
159 |
+
# Load data and create the search
|
160 |
+
if not session_state.database_loaded:
|
161 |
+
st.info("Loading database... This may take a moment.")
|
162 |
+
embeddings = SentenceTransformerEmbeddings(model_name="keepitreal/vietnamese-sbert")
|
163 |
+
# Create a Chroma object with persistence
|
164 |
+
db = Chroma(persist_directory="./chroma_index_1", embedding_function=embeddings)
|
165 |
+
# Get documents from the database
|
166 |
+
db.get()
|
167 |
+
llm=BardLLM()
|
168 |
+
qa = RetrievalQA.from_chain_type(
|
169 |
+
llm=llm,
|
170 |
+
chain_type="stuff",
|
171 |
+
retriever=db.as_retriever(search_type="similarity", search_kwargs={"k":4}),
|
172 |
+
return_source_documents=True)
|
173 |
+
question_template = """
|
174 |
+
Context: You are a helpful and informative bot that answers questions posed below using page_content information from real estate documents.
|
175 |
+
Do not create your own answer, just answer using page_content and metadata information from related documents in Vietnamese.
|
176 |
+
Be sure to respond in a complete sentence, being comprehensive, including all metadata information.
|
177 |
+
Imagine you're talking to a friend and use natural language and phrasing.
|
178 |
+
You can only use Vietnamese do not use other languages.
|
179 |
+
|
180 |
+
QUESTION: '{question}'
|
181 |
+
|
182 |
+
ANSWER:
|
183 |
+
"""
|
184 |
+
QUESTION_PROMPT = PromptTemplate(
|
185 |
+
template=question_template, input_variables=["question"]
|
186 |
+
)
|
187 |
+
session_state.database_loaded = True
|
188 |
+
|
189 |
+
if session_state.database_loaded:
|
190 |
+
col1, col2 = st.columns([2, 1]) # Create a two-column layout
|
191 |
+
|
192 |
+
with col1:
|
193 |
+
query = st.text_input("Enter your property search query:")
|
194 |
+
search_button = st.button("Search", help="Click to start the search")
|
195 |
+
|
196 |
+
if search_button:
|
197 |
+
with st.spinner("Searching..."):
|
198 |
+
if query is not None: # Check if model_embedding is not None
|
199 |
+
qa.combine_documents_chain.llm_chain.prompt = QUESTION_PROMPT
|
200 |
+
qa.combine_documents_chain.verbose = True
|
201 |
+
qa.return_source_documents = True
|
202 |
+
results = qa({"query":query,})
|
203 |
+
result = results["source_documents"]
|
204 |
+
session_state.page_index = 0 # Reset page index when a new search is performed
|
205 |
+
|
206 |
+
with col2:
|
207 |
+
if len(result) > 0:
|
208 |
+
st.write(f'Total Results: {len(result)} properties found.') # Display "Total Results" in the second column
|
209 |
+
|
210 |
+
if result:
|
211 |
+
N = 5
|
212 |
+
prev_button, next_button = st.columns([4,1])
|
213 |
+
last_page = len(result) // N
|
214 |
+
|
215 |
+
|
216 |
+
# Update page index based on button clicks
|
217 |
+
if prev_button.button("Previous", key="prev_button"):
|
218 |
+
if session_state.page_index - 1 < 0:
|
219 |
+
session_state.page_index = last_page
|
220 |
+
else:
|
221 |
+
session_state.page_index -= 1
|
222 |
+
|
223 |
+
if next_button.button("Next", key="next_button"):
|
224 |
+
if session_state.page_index > last_page:
|
225 |
+
session_state.page_index = 0
|
226 |
+
else:
|
227 |
+
session_state.page_index += 1
|
228 |
+
|
229 |
+
# Calculate the range of results to display (5 properties at a time)
|
230 |
+
start_idx = session_state.page_index * N
|
231 |
+
end_idx = (1 + session_state.page_index) * N
|
232 |
+
|
233 |
+
# Display results for the current page
|
234 |
+
display_search_results(result, start_idx, end_idx)
|