added auth back

README.md

@@ -26,7 +26,7 @@ This chat application leverages the power of Langchain QA retriever to access an
 4. Set up environment variables for Langchain credentials.
 
 ## Usage
-- Start the app: `python app.py`
+- Start the app: `chainlit run app.py --watch`
 - Use the chat interface to ask questions like "What is MLFlow in Databricks?"
 - Receive concise, accurate answers sourced from Broomva's Tech Book.
 

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
@@ -149,12 +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(
@@ -174,39 +174,4 @@ async def init():
 async def main(message):
     chain = cl.user_session.get("chain")  # type: RetrievalQAWithSourcesChain
 
-    cb = cl.AsyncLangchainCallbackHandler(
-        stream_final_answer=True,  # answer_prefix_tokens=["FINAL", "ANSWER"]
-    )
-    cb.answer_reached = True
-
-    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()
-
-
-# # Instantiate the LLM
-# llm = HuggingFaceHub(
-#     model_kwargs={"max_length": 500},
-#     repo_id="Broomva/bart-large-translation-spa-guc",
-# )
-
-# # Add the LLM provider
-# add_llm_provider(
-#     LangchainGenericProvider(
-#         # It is important that the id of the provider matches the _llm_type
-#         id=llm._llm_type,
-#         # The name is not important. It will be displayed in the UI.
-#         name="Spa - Guc Translation",
-#         # This should always be a Langchain llm instance (correctly configured)
-#         llm=llm,
-#         # If the LLM works with messages, set this to True
-#         is_chat=True
-#     )
-# )
+    await chain.acall(message.content, callbacks=[cl.AsyncLangchainCallbackHandler()])

chainlit.md

@@ -5,4 +5,4 @@ answer questions leveraging information found in the book. Go ahead and ask thin
 
 `What is machine learning and deep learning?`
 
-`what is quantum computing?`
+`what is quantum computing?`