app.py

import os
import torch
from transformers import (
  AutoTokenizer,
  AutoModelForCausalLM,
  BitsAndBytesConfig,
  pipeline
)
import gradio as gr

from transformers import BitsAndBytesConfig
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS

from langchain.prompts import PromptTemplate
from langchain.schema.runnable import RunnablePassthrough
from langchain.llms import HuggingFacePipeline
from langchain.chains import LLMChain
import transformers

import transformers
from transformers import pipeline

import transformers
model_name='mistralai/Mistral-7B-Instruct-v0.1'
from huggingface_hub import login
login(token=st.secrets["HF_TOKEN"])
model_config = transformers.AutoConfig.from_pretrained(
    model_name,
)

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
#################################################################
# bitsandbytes parameters
#################################################################

# Activate 4-bit precision base model loading
use_4bit = True

# Compute dtype for 4-bit base models
bnb_4bit_compute_dtype = "float16"

# Quantization type (fp4 or nf4)
bnb_4bit_quant_type = "nf4"

# Activate nested quantization for 4-bit base models (double quantization)
use_nested_quant = False
#################################################################
# Set up quantization config
#################################################################
compute_dtype = getattr(torch, bnb_4bit_compute_dtype)

bnb_config = BitsAndBytesConfig(
    load_in_4bit=use_4bit,
    bnb_4bit_quant_type=bnb_4bit_quant_type,
    bnb_4bit_compute_dtype=compute_dtype,
    bnb_4bit_use_double_quant=use_nested_quant,
)
#############################################################
# Load pre-trained config
#################################################################
model = AutoModelForCausalLM.from_pretrained(
   "mistralai/Mistral-7B-Instruct-v0.1",
    quantization_config=bnb_config,
)
# Connect query to FAISS index using a retriever
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.")