reorganisation

app.py

@@ -1,91 +1,108 @@
-import streamlit as st 
-from llama_cpp import Llama
-import os 
-from rag_model import RAGEngine
-
-#from rag_model_optimise import RAGEngine
+import os
 import logging
+import streamlit as st
 from huggingface_hub import hf_hub_download
-import time
-
 
-import os
-os.environ["NLTK_DATA"] = "/home/appuser/nltk_data"
+# ✅ Nouveau moteur RAG (sans ollama_opts)
+from rag_model_ollama_v1 import RAGEngine
 
-# Appliquer le patch avant tout import de llama_index
-from patches.llama_patch import patch_llamaindex_nltk
-patch_llamaindex_nltk()
+# --- 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)
-logger.addHandler(handler)
-
+if not logger.handlers:
+    logger.addHandler(handler)
 
-ENV = os.getenv("ENV", "space")
-logger.info(f"ENV :{ENV}")
+st.set_page_config(page_title="Chatbot RAG (Ollama)", page_icon="🤖")
 
-#time.sleep(5)
+# --- ENV ---
+ENV = os.getenv("ENV", "local")
+logger.info(f"ENV: {ENV}")
 
+# --- Chemins FAISS & chunks ---
 if ENV == "local":
-    model_path = "chatbot-models/Nous-Hermes-2-Mistral-7B-DPO.Q4_K_M.gguf"
-    faiss_index_path="chatbot-models/vectordb_docling/index.faiss"
-    vectors_path="chatbot-models/vectordb_docling/chunks.pkl"
-    
+    faiss_index_path = "chatbot-models/vectordb_docling/index.faiss"
+    vectors_path = "chatbot-models/vectordb_docling/chunks.pkl"
 else:
-  # Télécharger le modèle GGUF
-    model_path = hf_hub_download(
-        repo_id="rkonan/chatbot-models",
-        filename="chatbot-models/Nous-Hermes-2-Mistral-7B-DPO.Q4_K_M.gguf",
-        repo_type="dataset"
-    )
-
-    # Télécharger les fichiers FAISS
     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"
     )
 
-
-
-
-st.set_page_config(page_title="Chatbot RAG local",page_icon="🤖")
-
-
-
-
-
-@st.cache_resource
-def load_rag_engine():
+# --- 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_path=model_path,
+        model_name=_model_name,
         vector_path=vectors_path,
         index_path=faiss_index_path,
-        model_threads=8  # ✅ plus rapide
+        model_threads=_threads,
+        ollama_host=_host
+        # ❌ pas d'ollama_opts → Ollama choisit les defaults
     )
-    
-    # 🔥 Warmup pour éviter latence au 1er appel
-    rag.llm("Bonjour", max_tokens=1)
     return rag
 
-
-rag=load_rag_engine()
-
-st.title("🤖 Chatbot LLM Local (CPU)")
-
-user_input=st.text_area("Posez votre question :", height=100)
-
-if st.button("Envoyer") and user_input.strip():
-       with st.spinner("Génération en cours..."):
-            response = rag.ask(user_input)
-            st.markdown("**Réponse :**")
-            st.success(response)
+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.")

app_ollama_v1.py

@@ -1,108 +0,0 @@
-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.")

sauve/app_old.py

@@ -0,0 +1,91 @@
+import streamlit as st 
+from llama_cpp import Llama
+import os 
+from rag_model import RAGEngine
+
+#from rag_model_optimise import RAGEngine
+import logging
+from huggingface_hub import hf_hub_download
+import time
+
+
+import os
+os.environ["NLTK_DATA"] = "/home/appuser/nltk_data"
+
+# Appliquer le patch avant tout import de llama_index
+from patches.llama_patch import patch_llamaindex_nltk
+patch_llamaindex_nltk()
+
+logger = logging.getLogger("Streamlit")
+logger.setLevel(logging.INFO)
+handler = logging.StreamHandler()
+formatter = logging.Formatter("[%(asctime)s] %(levelname)s - %(message)s")
+handler.setFormatter(formatter)
+logger.addHandler(handler)
+
+
+ENV = os.getenv("ENV", "space")
+logger.info(f"ENV :{ENV}")
+
+#time.sleep(5)
+
+if ENV == "local":
+    model_path = "chatbot-models/Nous-Hermes-2-Mistral-7B-DPO.Q4_K_M.gguf"
+    faiss_index_path="chatbot-models/vectordb_docling/index.faiss"
+    vectors_path="chatbot-models/vectordb_docling/chunks.pkl"
+    
+else:
+  # Télécharger le modèle GGUF
+    model_path = hf_hub_download(
+        repo_id="rkonan/chatbot-models",
+        filename="chatbot-models/Nous-Hermes-2-Mistral-7B-DPO.Q4_K_M.gguf",
+        repo_type="dataset"
+    )
+
+    # Télécharger les fichiers FAISS
+    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"
+    )
+
+
+
+
+st.set_page_config(page_title="Chatbot RAG local",page_icon="🤖")
+
+
+
+
+
+@st.cache_resource
+def load_rag_engine():
+    rag = RAGEngine(
+        model_path=model_path,
+        vector_path=vectors_path,
+        index_path=faiss_index_path,
+        model_threads=8  # ✅ plus rapide
+    )
+    
+    # 🔥 Warmup pour éviter latence au 1er appel
+    rag.llm("Bonjour", max_tokens=1)
+    return rag
+
+
+rag=load_rag_engine()
+
+st.title("🤖 Chatbot LLM Local (CPU)")
+
+user_input=st.text_area("Posez votre question :", height=100)
+
+if st.button("Envoyer") and user_input.strip():
+       with st.spinner("Génération en cours..."):
+            response = rag.ask(user_input)
+            st.markdown("**Réponse :**")
+            st.success(response)