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import os
import logging
import streamlit as st
from huggingface_hub import hf_hub_download
# ✅ Nouveau moteur RAG (sans ollama_opts)
from rag_model_ollama_v1 import RAGEngine
# --- Config & logs ---
os.environ.setdefault("NLTK_DATA", "/home/appuser/nltk_data")
logger = logging.getLogger("Streamlit")
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
formatter = logging.Formatter("[%(asctime)s] %(levelname)s - %(message)s")
handler.setFormatter(formatter)
if not logger.handlers:
logger.addHandler(handler)
st.set_page_config(page_title="Chatbot RAG (Ollama)", page_icon="🤖")
# --- ENV ---
ENV = os.getenv("ENV", "local")
logger.info(f"ENV: {ENV}")
# --- Chemins FAISS & chunks ---
if ENV == "local":
faiss_index_path = "chatbot-models/vectordb_docling/index.faiss"
vectors_path = "chatbot-models/vectordb_docling/chunks.pkl"
else:
faiss_index_path = hf_hub_download(
repo_id="rkonan/chatbot-models",
filename="chatbot-models/vectordb_docling/index.faiss",
repo_type="dataset"
)
vectors_path = hf_hub_download(
repo_id="rkonan/chatbot-models",
filename="chatbot-models/vectordb_docling/chunks.pkl",
repo_type="dataset"
)
# --- UI Sidebar ---
st.sidebar.header("⚙️ Paramètres")
default_host = os.getenv("OLLAMA_HOST", "http://localhost:11435")
ollama_host = st.sidebar.text_input("Ollama host", value=default_host)
suggested_models = [
"qwen2.5:3b-instruct-q4_K_M",
"noushermes_rag",
"mistral",
"gemma3",
"deepseek-r1",
"granite3.3",
"llama3.1:8b-instruct-q4_K_M",
"nous-hermes2:Q4_K_M",
]
model_name = st.sidebar.selectbox("Modèle Ollama", options=suggested_models, index=0)
num_threads = st.sidebar.slider("Threads (hint)", min_value=2, max_value=16, value=6, step=1)
temperature = st.sidebar.slider("Température", min_value=0.0, max_value=1.5, value=0.1, step=0.1)
st.title("🤖 Chatbot RAG Local (Ollama)")
# --- Cache du moteur ---
@st.cache_resource(show_spinner=True)
def load_rag_engine(_model_name: str, _host: str, _threads: int, _temp: float):
os.environ["OLLAMA_KEEP_ALIVE"] = "15m"
rag = RAGEngine(
model_name=_model_name,
vector_path=vectors_path,
index_path=faiss_index_path,
model_threads=_threads,
ollama_host=_host
# ❌ pas d'ollama_opts → Ollama choisit les defaults
)
return rag
rag = load_rag_engine(model_name, ollama_host, num_threads, temperature)
# --- Chat simple ---
user_input = st.text_area("Posez votre question :", height=120,
placeholder="Ex: Quels sont les traitements appliqués aux images ?")
col1, col2 = st.columns([1, 1])
# if col1.button("Envoyer"):
# if user_input.strip():
# with st.spinner("Génération en cours..."):
# try:
# response = rag.ask(user_input)
# st.markdown("**Réponse :**")
# st.success(response)
# except Exception as e:
# st.error(f"Erreur pendant la génération: {e}")
# else:
# st.info("Saisissez une question.")
if col2.button("Envoyer (stream)"):
if user_input.strip():
with st.spinner("Génération en cours (stream)..."):
try:
ph = st.empty()
acc = ""
for token in rag.ask_stream(user_input):
acc += token
ph.markdown(acc)
st.balloons()
except Exception as e:
st.error(f"Erreur pendant la génération (stream): {e}")
else:
st.info("Saisissez une question.")
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