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# model under development
import os
import streamlit as st
from langchain.document_loaders.csv_loader import CSVLoader
#from langchain.text_splitter import RecursiveCharacterTextSplitter
#from langchain_text_splitters import CharacterTextSplitter
from langchain_experimental.text_splitter import SemanticChunker
#from langchain_core.prompts import ChatPromptTemplate
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import CTransformers
from langchain.chains import ConversationalRetrievalChain, LLMChain
#from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
#from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
# adding separator
def add_vertical_space(spaces=1):
for _ in range(spaces):
st.sidebar.markdown("---")
# main method
def main():
# page title
st.set_page_config(page_title="Chatbot for NHPC", layout="wide")
st.title("Chatbot for NHPC")
st.write("##### 🚧 Under development 🚧")
# faiss db directory
DB_FAISS_PATH = "vectorstore/db_faiss"
TEMP_DIR = "temp"
# embedding model path
EMBEDDING_MODEL_PATH = "embeddings/MiniLM-L6-v2"
# creating faiss db direcoty if it doesnot exist already
if not os.path.exists(TEMP_DIR):
os.makedirs(TEMP_DIR)
# uploading csv file
uploaded_file = st.sidebar.file_uploader("Upload CSV file", type=['csv'], help="Upload a CSV file")
# adding vertical space
add_vertical_space(1)
# creating faiss vectorstore
if uploaded_file is not None:
file_path = os.path.join(TEMP_DIR, uploaded_file.name)
with open(file_path, "wb") as f:
f.write(uploaded_file.getvalue())
st.write(f"Uploaded file: {uploaded_file.name}")
st.write("Processing CSV file...")
st.sidebar.markdown('##### The model may sometime generate excessive or incorrect response.')
# calling CSVLoader for loading CSV file
loader = CSVLoader(file_path=file_path, encoding="utf-8", csv_args={'delimiter': ','})
# loading the CSV file data
data = loader.load()
# creating embeddings using huggingface
embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
# creating chunks from CSV file
#text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=50)
text_splitter = SemanticChunker(embeddings, breakpoint_threshold_type="interquartile")
#text_splitter = CharacterTextSplitter(chunk_size=800, chunk_overlap=100)
text_chunks = text_splitter.split_documents(data)
# chunks message to output
st.write(f"Total text chunks: {len(text_chunks)}")
st.write("---")
# creating vectorstore from the text chunks
docsearch = FAISS.from_documents(text_chunks, embeddings)
# saving the vector store to local directory
docsearch.save_local(DB_FAISS_PATH)
# loading local llama model
llm = CTransformers(#model="models/llama-2-7b-chat.ggmlv3.q8_0.bin",
model="models/llama-2-7b-chat.ggmlv3.q4_0.bin",
model_type="llama",
#callbacks=[StreamingStdOutCallbackHandler()],
config={'max_new_tokens': 1024,
'temperature': 0.5,
'context_length' : 4096
#'repetition_penalty': 1.1
}
)
# loading remote zephyr model
#llm = AutoModelForCausalLM.from_pretrained("TheBloke/zephyr-7B-beta-GGUF",
# model_file="zephyr-7b-beta.Q5_K_M.gguf",
# model_type="mistral",
# gpu_layers=50,
# max_new_tokens = 1000,
# context_length = 6000)
# question answering chain
#memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
# custom prompt
custom_template="""
You are a smart personal assistant and your task is to provide the answer of the given question based only on the given context. \n
If you can't find the answer in the context, just say that "I don't know, please look up the policy." and don't try to make up an answer. \n\n
Please, give the answer in plain english and don't repeat your answer and don't mention that you found the answer form the context and don't mention that the answer can be found in the context. \n
Question: "{question}" \n\n
Context: "{context}" \n\n
Helpful Answer:
"""
QA_PROMPT = PromptTemplate(template=custom_template,input_variables=["question", "context"])
# main llm chain
qa = ConversationalRetrievalChain.from_llm(llm,
#chain_type = "stuff",
chain_type = "stuff",
verbose=True,
#retriever=docsearch.as_retriever()
retriever=docsearch.as_retriever(search_kwargs = {"k" : 4, "search_type" : "similarity"}),
combine_docs_chain_kwargs={"prompt": QA_PROMPT}
#retriever=docsearch.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.1})
#memory=memory
)
# taking question from user
#st.write("### Enter your query:")
query = st.chat_input("Ask a question to the chatbot.")
if query:
st.write("#### Query: "+query)
with st.spinner("Processing your question..."):
chat_history = []
result = qa({"question": query, "chat_history": chat_history})
#st.write("---")
#st.write("### Response:")
#st.write("#### Query: "+query)
st.write(f"> {result['answer']}")
os.remove(file_path)
if __name__ == "__main__":
main()
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