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import os
import streamlit as st
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_huggingface import HuggingFaceEndpoint
from langchain.prompts import PromptTemplate
from langchain.schema.runnable import RunnablePassthrough
from langchain.chains import LLMChain
from huggingface_hub import login
login(token=st.secrets["HF_TOKEN"])
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
# Montez Google Drive
loader = PyPDFLoader("test-1.pdf")
data = loader.load()
# split the documents into chunks
text_splitter1 = CharacterTextSplitter(chunk_size=512, chunk_overlap=0,separator="\n\n")
texts = text_splitter1.split_documents(data)
db = FAISS.from_documents(texts,
HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2'))
retriever = db.as_retriever(
search_type="mmr",
search_kwargs={'k': 1}
)
prompt_template = """
### [INST]
Instruction: You are a Q&A assistant. Your goal is to answer questions as accurately as possible based on the instructions and context provided without using prior knowledge.You answer in FRENCH
Analyse carefully the context and provide a direct answer based on the context.
Answer in french only
{context}
Vous devez répondre aux questions en français.
### QUESTION:
{question}
[/INST]
Answer in french only
Vous devez répondre aux questions en français.
"""
repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
mistral_llm = HuggingFaceEndpoint(
repo_id=repo_id, max_length=128, temperature=0.5, huggingfacehub_api_token=st.secrets["HF_TOKEN"]
)
# Create prompt from prompt template
prompt = PromptTemplate(
input_variables=["question"],
template=prompt_template,
)
# Create llm chain
llm_chain = LLMChain(llm=mistral_llm, prompt=prompt)
retriever.search_kwargs = {'k':1}
qa = RetrievalQA.from_chain_type(
llm=mistral_llm,
chain_type="stuff",
retriever=retriever,
chain_type_kwargs={"prompt": prompt},
)
import streamlit as st
# Streamlit interface
st.title("Chatbot Interface")
# Define function to handle user input and display chatbot response
def chatbot_response(user_input):
response = qa.run(user_input)
return response
# Streamlit components
user_input = st.text_input("You:", "")
submit_button = st.button("Send")
# Handle user input
if submit_button:
if user_input.strip() != "":
bot_response = chatbot_response(user_input)
st.text_area("Bot:", value=bot_response, height=200)
else:
st.warning("Please enter a message.")
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