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import streamlit as st
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Chargement du modèle DialoGPT-medium
@st.cache_resource # pour cacher le modèle et ne pas le recharger à chaque run
def load_model():
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
return tokenizer, model
tokenizer, model = load_model()
# Initialisation de l'historique
def init_chat_session():
if "chat_history" not in st.session_state:
st.session_state["chat_history"] = []
# Fonction qui génère la réponse
def chatbot_response(prompt):
# Encodage
input_ids = tokenizer.encode(prompt + tokenizer.eos_token, return_tensors="pt")
# Historique du chat
chat_history_ids = model.generate(
input_ids,
max_length=1000,
pad_token_id=tokenizer.eos_token_id,
do_sample=True,
top_k=50,
top_p=0.95,
temperature=0.7
)
# Décodage
response = tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
return response
# Interface Streamlit
def chat_interface():
init_chat_session()
# Affichage de l'historique
for msg in st.session_state["chat_history"]:
with st.chat_message(msg["role"]):
st.markdown(msg["content"])
# Input utilisateur
if prompt := st.chat_input("Posez votre question :"):
# Ajouter la question
st.session_state["chat_history"].append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
# Générer réponse
answer = chatbot_response(prompt)
# Ajouter la réponse à l'historique
st.session_state["chat_history"].append({"role": "assistant", "content": answer})
with st.chat_message("assistant"):
st.markdown(answer) |