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import os
import torch
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
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.prompts import PromptTemplate
from langchain.schema.runnable import RunnablePassthrough
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.") |