auth

app.py

@@ -4,11 +4,8 @@ import gradio as gr
 from tools import create_agent
 from langchain_core.messages import RemoveMessage
 from langchain_core.messages import trim_messages
-load_dotenv()
 
 # Global params
-AUTH_ID = os.environ.get("AUTH_ID")
-AUTH_PASS = os.environ.get("AUTH_PASS")
 AGENT = create_agent()
 theme = gr.themes.Default(primary_hue="red", secondary_hue="red")
 default_msg = "Bonjour ! Je suis là pour répondre à vos questions sur l'actuariat. Comment puis-je vous aider aujourd'hui ?"
@@ -83,4 +80,7 @@ with gr.Blocks(theme=theme, js=js_func, title="Dataltist", fill_height=True) as
     iface.unload(delete_agent)
 
 if __name__ == "__main__":
+    load_dotenv()
+    AUTH_ID = os.environ.get("AUTH_ID")
+    AUTH_PASS = os.environ.get("AUTH_PASS")
     iface.launch(auth=(AUTH_ID, AUTH_PASS)) 

tools.py

@@ -41,13 +41,13 @@ memoires_ds = load_dataset("eliot-hub/memoires_vec_800", split="data", token=HF_
 batched_ds = memoires_ds.batch(batch_size=41000)
 client = chromadb.Client()
 collection = client.get_or_create_collection(name="embeddings_mxbai") 
-# for batch in tqdm(batched_ds, desc="Processing dataset batches"): 
-#     collection.add(
-#         ids=batch["id"],
-#         metadatas=batch["metadata"],
-#         documents=batch["document"],
-#         embeddings=batch["embedding"],      
-#     )
+for batch in tqdm(batched_ds, desc="Processing dataset batches"): 
+    collection.add(
+        ids=batch["id"],
+        metadatas=batch["metadata"],
+        documents=batch["document"],
+        embeddings=batch["embedding"],      
+    )
 print(f"Collection complete: {collection.count()}")
 del memoires_ds, batched_ds