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# \______ \ ____ ______ | | ____ ___.__. _____ ____ _____/ |_
# | | \_/ __ \\____ \| | / _ < | |/ \_/ __ \ / \ __\
# | ` \ ___/| |_> > |_( <_> )___ | Y Y \ ___/| | \ |
# /_______ /\___ > __/|____/\____// ____|__|_| /\___ >___| /__|
# \/ \/|__| \/ \/ \/ \/
# Load package
import streamlit as st
#from dotenv import load_dotenv
from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts import PromptTemplate
from langchain.document_loaders import PyPDFLoader, DataFrameLoader
import pandas as pd
import openai
#import os
import keyboard
import time
# Load .env
#load_dotenv()
# Inisialisasi api key
#KEY = os.getenv("MY_KEY")
# Masukan api key
openai.api_key = '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'
# Buat object embedding
embedding = OpenAIEmbeddings(openai_api_key='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')
# Model
llm = ChatOpenAI(model="gpt-4", openai_api_key='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', temperature=0)
# Load data csv used car
df = pd.read_csv(r'clean_usedcar_data.csv')
# Load data csv FAQ
loader = PyPDFLoader(r"question-answer.pdf")
data_faq = loader.load()
# Buat page seperti pdf dari used car data
loader = DataFrameLoader(df, page_content_column="combined_info")
data_car = loader.load()
# Memotong karakter pada pdf per 1000 karakter
text_spliter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0,
separators=[",","\n\n", "\n", "(?<=\. )", " "],
length_function=len)
# Proses chunk
text_chunk_recom = text_spliter.split_documents(data_car)
text_chunk_faq = text_spliter.split_documents(data_faq)
# Vector DB data used car
vectorStore_recom = FAISS.from_documents(text_chunk_recom, embedding)
vectorStore_faq = FAISS.from_documents(text_chunk_faq, embedding)
# Merge vectorestore
vectorStore_recom.merge_from(vectorStore_faq)
# Mmbuka file prompt
with open(r'prompt_combined.txt', 'r') as file:
prompt_template = file.read()
# Membuat objek retriever
retrieve = vectorStore_recom.as_retriever(search_type="similarity", search_kwargs={"k": 3})
# _________ __ .__ .__ __
# / _____// |________ ____ _____ _____ | | |__|/ |_
# \_____ \\ __\_ __ \_/ __ \\__ \ / \| | | \ __\
# / \| | | | \/\ ___/ / __ \| Y Y \ |_| || |
# /_______ /|__| |__| \___ >____ /__|_| /____/__||__|
# \/ \/ \/ \/
class BotCRC:
def __init__(self):
global prompt_template
global retrieve
self.prompt = PromptTemplate(input_variables=["context", "question", "chat_history"], template=prompt_template)
self.memory = ConversationBufferMemory(memory_key="chat_history", input_key="question", return_messages=True)
self.qa_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retrieve, memory=self.memory,
combine_docs_chain_kwargs={'prompt': self.prompt})
# Fungsi untuk berinteraksi dengan bot recommendation
def conversation(self, user_input):
result = self.qa_chain({"question": user_input})
response = result["answer"]
return response
# Fungsi interface
def chatbot_chain(self):
USER = "user"
ASSISTANT = "assistant"
initial_context = "Anda adalah asisten dari Carsome, platform jual beli mobil bekas. Anda memiliki dua tugas utama: menjawab pertanyaan pelanggan dan memberikan rekomendasi mobil berdasarkan preferensi mereka."
# self.memory.chat_memory.add_user_message(initial_context)
self.memory.chat_memory.add_ai_message(
"Halo! Saya adalah asisten dari Carsome. Bagaimana saya bisa membantu Anda hari ini?")
exit_input = ['keluar', 'sampai jumpa lagi', 'sampai jumpa kembali', 'bye']
# Interface dengan streamlit
st.title("AutoBuddy")
st.write("Carsome Assistant Chatbot")
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
if 'chat_history' not in st.session_state:
st.session_state.chat_history = self.memory.chat_memory
else:
self.memory.chat_memory = st.session_state.chat_history
user_input = st.chat_input("Masukkan pesan yang ingin kamu tulis.", key="chat_input")
if user_input:
st.session_state.messages.append({"role": "user", "content": user_input})
if any(word in user_input.lower() for word in exit_input):
st.session_state.messages.append({"role": "user", "content": user_input})
st.chat_message(ASSISTANT).write("Sampai jumpa lagi..", key="chat_output")
time.sleep(2.5)
keyboard.press_and_release('ctrl+w')
else:
result = self.conversation(user_input)
# st.session_state.messages.append({"role": "user", "content": user_input})
# output = st.chat_message(ASSISTANT).write(result, key="chat_output")
st.session_state.messages.append({"role": "assistant", "content": result})
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
chat = BotCRC()
chat.chatbot_chain() |