.

app.py

@@ -21,91 +21,91 @@ embeddings = OpenAIEmbeddings()
 vector_store = FAISS.load_local("docs.faiss", embeddings)
 
 
-@cl.oauth_callback
-def oauth_callback(
-    provider_id: str,
-    token: str,
-    raw_user_data: Dict[str, str],
-    default_app_user: cl.AppUser,
-) -> Optional[cl.AppUser]:
-    # set AppUser tags as regular_user
-    match default_app_user.username:
-        case "Broomva":
-            default_app_user.tags = ["admin_user"]
-            default_app_user.role = "ADMIN"
-        case _:
-            default_app_user.tags = ["regular_user"]
-            default_app_user.role = "USER"
-    print(default_app_user)
-    return default_app_user
-
-
-@cl.header_auth_callback
-def header_auth_callback(headers) -> Optional[cl.AppUser]:
-    # Verify the signature of a token in the header (ex: jwt token)
-    # or check that the value is matching a row from your database
-    print(headers)
-    if (
-        headers.get("cookie")
-        == "ajs_user_id=5011e946-0d0d-5bd4-a293-65742db98d3d; ajs_anonymous_id=67d2569d-3f50-48f3-beaf-b756286276d9"
-    ):
-        return cl.AppUser(username="Broomva", role="ADMIN", provider="header")
-    else:
-        return None
-
-
-@cl.password_auth_callback
-def auth_callback(
-    username: str = "guest", password: str = "guest"
-) -> Optional[cl.AppUser]:
-    # Fetch the user matching username from your database
-    # and compare the hashed password with the value stored in the database
-    import hashlib
-
-    # Create a new sha256 hash object
-    hash_object = hashlib.sha256()
-
-    # Hash the password
-    hash_object.update(password.encode())
-
-    # Get the hexadecimal representation of the hash
-    hashed_password = hash_object.hexdigest()
-
-    if (username, hashed_password) == (
-        "broomva",
-        "b68cacbadaee450b8a8ce2dd44842f1de03ee9993ad97b5e99dea64ef93960ba",
-    ):
-        return cl.AppUser(username="Broomva", role="ADMIN", provider="credentials")
-    elif (username, password) == ("guest", "guest"):
-        return cl.AppUser(username="Guest", role="USER", provider="credentials")
-    else:
-        return None
-
-
-@cl.set_chat_profiles
-async def chat_profile(current_user: cl.AppUser):
-    if "ADMIN" not in current_user.role:
-        # Default to 3.5 when not admin
-        return [
-            cl.ChatProfile(
-                name="Broomva Book Agent",
-                markdown_description="The underlying LLM model is **GPT-3.5**.",
-                # icon="https://picsum.photos/200",
-            ),
-        ]
-
-    return [
-        cl.ChatProfile(
-            name="Broomva Book Agent Lite",
-            markdown_description="The underlying LLM model is **GPT-3.5**.",
-            # icon="https://picsum.photos/200",
-        ),
-        cl.ChatProfile(
-            name="Broomva Book Agent Turbo",
-            markdown_description="The underlying LLM model is **GPT-4 Turbo**.",
-            # icon="https://picsum.photos/250",
-        ),
-    ]
+# @cl.oauth_callback
+# def oauth_callback(
+#     provider_id: str,
+#     token: str,
+#     raw_user_data: Dict[str, str],
+#     default_app_user: cl.AppUser,
+# ) -> Optional[cl.AppUser]:
+#     # set AppUser tags as regular_user
+#     match default_app_user.username:
+#         case "Broomva":
+#             default_app_user.tags = ["admin_user"]
+#             default_app_user.role = "ADMIN"
+#         case _:
+#             default_app_user.tags = ["regular_user"]
+#             default_app_user.role = "USER"
+#     print(default_app_user)
+#     return default_app_user
+
+
+# @cl.header_auth_callback
+# def header_auth_callback(headers) -> Optional[cl.AppUser]:
+#     # Verify the signature of a token in the header (ex: jwt token)
+#     # or check that the value is matching a row from your database
+#     print(headers)
+#     if (
+#         headers.get("cookie")
+#         == "ajs_user_id=5011e946-0d0d-5bd4-a293-65742db98d3d; ajs_anonymous_id=67d2569d-3f50-48f3-beaf-b756286276d9"
+#     ):
+#         return cl.AppUser(username="Broomva", role="ADMIN", provider="header")
+#     else:
+#         return None
+
+
+# @cl.password_auth_callback
+# def auth_callback(
+#     username: str = "guest", password: str = "guest"
+# ) -> Optional[cl.AppUser]:
+#     # Fetch the user matching username from your database
+#     # and compare the hashed password with the value stored in the database
+#     import hashlib
+
+#     # Create a new sha256 hash object
+#     hash_object = hashlib.sha256()
+
+#     # Hash the password
+#     hash_object.update(password.encode())
+
+#     # Get the hexadecimal representation of the hash
+#     hashed_password = hash_object.hexdigest()
+
+#     if (username, hashed_password) == (
+#         "broomva",
+#         "b68cacbadaee450b8a8ce2dd44842f1de03ee9993ad97b5e99dea64ef93960ba",
+#     ):
+#         return cl.AppUser(username="Broomva", role="ADMIN", provider="credentials")
+#     elif (username, password) == ("guest", "guest"):
+#         return cl.AppUser(username="Guest", role="USER", provider="credentials")
+#     else:
+#         return None
+
+
+# @cl.set_chat_profiles
+# async def chat_profile(current_user: cl.AppUser):
+#     if "ADMIN" not in current_user.role:
+#         # Default to 3.5 when not admin
+#         return [
+#             cl.ChatProfile(
+#                 name="Broomva Book Agent",
+#                 markdown_description="The underlying LLM model is **GPT-3.5**.",
+#                 # icon="https://picsum.photos/200",
+#             ),
+#         ]
+
+#     return [
+#         cl.ChatProfile(
+#             name="Broomva Book Agent Lite",
+#             markdown_description="The underlying LLM model is **GPT-3.5**.",
+#             # icon="https://picsum.photos/200",
+#         ),
+#         cl.ChatProfile(
+#             name="Broomva Book Agent Turbo",
+#             markdown_description="The underlying LLM model is **GPT-4 Turbo**.",
+#             # icon="https://picsum.photos/250",
+#         ),
+#     ]
 
 
 @cl.on_settings_update
@@ -115,6 +115,7 @@ async def setup_agent(settings):
 
 @cl.on_chat_start
 async def init():
+    cl.AppUser(username="Broomva", role="ADMIN", provider="header")
     settings = await cl.ChatSettings(
         [
             Select(
@@ -148,12 +149,12 @@ async def init():
         ]
     ).send()
 
-    chat_profile = cl.user_session.get("chat_profile")
+    # chat_profile = cl.user_session.get("chat_profile")
 
-    if chat_profile == "Broomva Book Agent Lite":
-        settings["model"] = "gpt-3.5-turbo"
-    elif chat_profile == "Broomva Book Agent Turbo":
-        settings["model"] = "gpt-4-1106-preview"
+    # if chat_profile == "Broomva Book Agent Lite":
+    #     settings["model"] = "gpt-3.5-turbo"
+    # elif chat_profile == "Broomva Book Agent Turbo":
+    #     settings["model"] = "gpt-4-1106-preview"
 
     chain = RetrievalQAWithSourcesChain.from_chain_type(
         ChatOpenAI(
@@ -178,15 +179,16 @@ async def main(message):
     )
     cb.answer_reached = True
 
-    res = await chain.acall(message.content, callbacks=[cb])
+    await chain.acall(message.content, callbacks=[cb])
 
-    if cb.has_streamed_final_answer:
-        await cb.final_stream.update()
-    else:
-        answer = res["answer"]
-        await cl.Message(
-            content=answer,
-        ).send()
+    # if cb.has_streamed_final_answer:
+    #     await cb.final_stream.update()
+    # else:
+    #     
+    #answer = res["answer"]
+    #await cl.Message(
+    #    content=answer,
+    #).send()
 
 
 # # Instantiate the LLM