Update main.py

main.py

@@ -7,14 +7,14 @@ from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_openai import ChatOpenAI
 from langchain_core.runnables import RunnablePassthrough, RunnableLambda
 from langchain_core.output_parsers import StrOutputParser
-import os
-from dotenv import load_dotenv
 from prompt_engineering import build_prompt
 
 # ── État global ───────────────────────────────────────────────────────────────
 rag_chain = None
 retriever = None
-load_dotenv()  # ← charge le fichier .env
+
+# NOTE: On Hugging Face Spaces, set MISTRAL_API_KEY in your Space's Settings > Secrets.
+# Do NOT use python-dotenv or .env files on HF Spaces.
 
 # ── Helper format docs ────────────────────────────────────────────────────────
 def format_docs(docs) -> str:
@@ -65,24 +65,19 @@ async def lifespan(app: FastAPI):
     )
 
     vectorstore = FAISS.load_local(
-        r"faiss_index",
+        "faiss_index",
         embeddings=embedding,
         allow_dangerous_deserialization=True
     )
 
-    
-
-
     llm = ChatOpenAI(
-    base_url="https://api.mistral.ai/v1",
-    api_key=os.getenv("MISTRAL_API_KEY"),
-    model_name="mistral-medium"
-)
+        base_url="https://api.mistral.ai/v1",
+        api_key=os.environ["MISTRAL_API_KEY"],  # set in HF Spaces Secrets
+        model_name="mistral-medium"
+    )
 
     retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
 
-    # ✅ FIX : retriever | format_docs (via RunnableLambda) pour convertir les
-    #          documents en texte avant de les injecter dans le prompt
     rag_chain = (
         {
             "context": retriever | RunnableLambda(format_docs),