app.py

import os
import torch
from transformers import (
  BitsAndBytesConfig,
  pipeline
)
import streamlit as st
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFacePipeline
from transformers import BitsAndBytesConfig
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFaceEndpoint

from langchain.prompts import PromptTemplate
from langchain.schema.runnable import RunnablePassthrough
from langchain.chains import LLMChain
import transformers
from ctransformers import AutoModelForCausalLM, AutoTokenizer

import transformers
from transformers import pipeline
from datasets import load_dataset

import transformers
repo_id = "mistralai/Mistral-7B-Instruct-v0.3"

from huggingface_hub import login
login(token=st.secrets["HF_TOKEN"])
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
from google.colab import drive
from langchain.document_loaders import PyPDFLoader, OnlinePDFLoader

# 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'))


# Connect query to FAISS index using a retriever
retriever = db.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 1}
retriever = db.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 1}
)


from langchain.llms import HuggingFacePipeline
from langchain.prompts import PromptTemplate
from langchain.embeddings.huggingface import HuggingFaceEmbeddings

text_generation_pipeline = transformers.pipeline(
    model=model,
    tokenizer=tokenizer,
    task="text-generation",

    temperature=0.02,
    repetition_penalty=1.1,
    return_full_text=True,
    max_new_tokens=512,
)

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.

 """

mistral_llm = HuggingFacePipeline(pipeline=text_generation_pipeline)

# Create prompt from prompt template
prompt = PromptTemplate(
    input_variables=["question"],
    template=prompt_template,
)

# Create llm chain
llm_chain = LLMChain(llm=mistral_llm, prompt=prompt)
from langchain.chains import RetrievalQA


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.get_answer(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.")