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import streamlit as st
#from streamlit_chat import message as st_message
from streamlit_chat import message as st_message
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
from peft import PeftModel, PeftConfig
st.title("Chatbot Produit")
if "history" not in st.session_state:
st.session_state.history = []
def get_models():
peft_model_id = "tbboukhari/chatbot-produit-fr"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)
return tokenizer, model
def generate_answer():
tokenizer, model = get_models()
user_message = st.session_state.input_text
inputs = tokenizer(st.session_state.input_text, return_tensors="pt")
result = model.generate(**inputs)
message_bot = tokenizer.decode(result[0], skip_special_tokens=True) # .replace("<s>", "").replace("</s>", "")
st.session_state.history.append({"message": user_message, "is_user": True})
st.session_state.history.append({"message": message_bot, "is_user": False})
st.text_input("Response", key="input_text", on_change=generate_answer)
for chat in st.session_state.history:
st_message(**chat) |