auth

app.py

@@ -1,13 +1,18 @@
-import time
+import os
+from dotenv import load_dotenv
 import gradio as gr
 from tools import create_agent
 from langchain_core.messages import RemoveMessage
 from langchain_core.messages import trim_messages
-# from toolkits import create_agent
-# from langchain.schema import AIMessage, HumanMessage, SystemMessage
+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 ?"
+
 
 def filter_msg(msg_list:list, keep_n:int) -> list:
     """Keep only last keep_n messages from chat history. Preserves structure user msg -> tool msg -> ai msg"""
@@ -55,7 +60,7 @@ def delete_agent():
 
 with gr.Blocks(theme=theme, js=js_func, title="Dataltist", fill_height=True) as iface:
     gr.Markdown("# Dataltist Chatbot 🚀")
-    chatbot = gr.Chatbot(show_copy_button=True, show_share_button=False, type="messages", scale=1)
+    chatbot = gr.Chatbot(show_copy_button=True, show_share_button=False, value=[{"role":"assistant", "content":default_msg}], type="messages", scale=1)
     msg = gr.Textbox(lines=1, show_label=False, placeholder="Posez vos questions sur l'assurance")  #  submit_btn=True
     # clear = gr.ClearButton([msg, chatbot], value="Effacer 🗑")
     config = {"configurable": {"thread_id": "1"}} 
@@ -78,4 +83,4 @@ with gr.Blocks(theme=theme, js=js_func, title="Dataltist", fill_height=True) as
     iface.unload(delete_agent)
 
 if __name__ == "__main__":
-    iface.launch()  # share=True # auth=("admin", "admin")
+    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