fix security update

llama_stack/distribution/ui/app.py

@@ -73,8 +73,14 @@ if __name__ == "__main__":
             url=keycloak_url,
             realm=keycloak_realm,
             client_id=keycloak_client_id,
+            custom_labels={
+                "labelButton": "Sign in to kvant",
+                "labelLogin": "Please sign in to your kvant account.",
+                "errorNoPopup": "Unable to open the authentication popup. Allow popups and refresh the page to proceed.",
+                "errorPopupClosed": "Authentication popup was closed manually.",
+                "errorFatal": "Unable to connect to Keycloak using the current configuration."   
+            },
             auto_refresh=True,
-            init_options={"checkLoginIframe": False},
         )
 
         if keycloak.authenticated:

llama_stack/distribution/ui/modules/api.py

@@ -30,5 +30,3 @@ class LlamaStackApi:
             scoring_params = {fn_id: None for fn_id in scoring_function_ids}
         return self.client.scoring.score(input_rows=[row], scoring_functions=scoring_params)
 
-
-llama_stack_api = LlamaStackApi()

llama_stack/distribution/ui/page/distribution/datasets.py

@@ -6,13 +6,13 @@
 
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import llama_stack_api
+from llama_stack.distribution.ui.modules.api import LlamaStackApi
 
 
 def datasets():
     st.header("Datasets")
 
-    datasets_info = {d.identifier: d.to_dict() for d in llama_stack_api.client.datasets.list()}
+    datasets_info = {d.identifier: d.to_dict() for d in LlamaStackApi().client.datasets.list()}
     if len(datasets_info) > 0:
         selected_dataset = st.selectbox("Select a dataset", list(datasets_info.keys()))
         st.json(datasets_info[selected_dataset], expanded=True)

llama_stack/distribution/ui/page/distribution/eval_tasks.py

@@ -6,14 +6,14 @@
 
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import llama_stack_api
+from llama_stack.distribution.ui.modules.api import LlamaStackApi
 
 
 def benchmarks():
     # Benchmarks Section
     st.header("Benchmarks")
 
-    benchmarks_info = {d.identifier: d.to_dict() for d in llama_stack_api.client.benchmarks.list()}
+    benchmarks_info = {d.identifier: d.to_dict() for d in LlamaStackApi().client.benchmarks.list()}
 
     if len(benchmarks_info) > 0:
         selected_benchmark = st.selectbox("Select an eval task", list(benchmarks_info.keys()), key="benchmark_inspect")

llama_stack/distribution/ui/page/distribution/models.py

@@ -6,13 +6,13 @@
 
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import llama_stack_api
+from llama_stack.distribution.ui.modules.api import LlamaStackApi
 
 
 def models():
     # Models Section
     st.header("Models")
-    models_info = {m.identifier: m.to_dict() for m in llama_stack_api.client.models.list()}
+    models_info = {m.identifier: m.to_dict() for m in LlamaStackApi().client.models.list()}
 
     selected_model = st.selectbox("Select a model", list(models_info.keys()))
     st.json(models_info[selected_model])

llama_stack/distribution/ui/page/distribution/providers.py

@@ -6,12 +6,12 @@
 
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import llama_stack_api
+from llama_stack.distribution.ui.modules.api import LlamaStackApi
 
 
 def providers():
     st.header("🔍 API Providers")
-    apis_providers_lst = llama_stack_api.client.providers.list()
+    apis_providers_lst = LlamaStackApi().client.providers.list()
     api_to_providers = {}
     for api_provider in apis_providers_lst:
         if api_provider.api in api_to_providers:

llama_stack/distribution/ui/page/distribution/scoring_functions.py

@@ -6,13 +6,13 @@
 
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import llama_stack_api
+from llama_stack.distribution.ui.modules.api import LlamaStackApi
 
 
 def scoring_functions():
     st.header("Scoring Functions")
 
-    scoring_functions_info = {s.identifier: s.to_dict() for s in llama_stack_api.client.scoring_functions.list()}
+    scoring_functions_info = {s.identifier: s.to_dict() for s in LlamaStackApi().client.scoring_functions.list()}
 
     selected_scoring_function = st.selectbox("Select a scoring function", list(scoring_functions_info.keys()))
     st.json(scoring_functions_info[selected_scoring_function], expanded=True)

llama_stack/distribution/ui/page/distribution/shields.py

@@ -6,14 +6,14 @@
 
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import llama_stack_api
+from llama_stack.distribution.ui.modules.api import LlamaStackApi
 
 
 def shields():
     # Shields Section
     st.header("Shields")
 
-    shields_info = {s.identifier: s.to_dict() for s in llama_stack_api.client.shields.list()}
+    shields_info = {s.identifier: s.to_dict() for s in LlamaStackApi().client.shields.list()}
 
     selected_shield = st.selectbox("Select a shield", list(shields_info.keys()))
     st.json(shields_info[selected_shield])

llama_stack/distribution/ui/page/distribution/vector_dbs.py

@@ -6,12 +6,12 @@
 
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import llama_stack_api
+from llama_stack.distribution.ui.modules.api import LlamaStackApi
 
 
 def vector_dbs():
     st.header("Vector Databases")
-    vector_dbs_info = {v.identifier: v.to_dict() for v in llama_stack_api.client.vector_dbs.list()}
+    vector_dbs_info = {v.identifier: v.to_dict() for v in LlamaStackApi().client.vector_dbs.list()}
 
     if len(vector_dbs_info) > 0:
         selected_vector_db = st.selectbox("Select a vector database", list(vector_dbs_info.keys()))

llama_stack/distribution/ui/page/evaluations/app_eval.py

@@ -9,7 +9,7 @@ import json
 import pandas as pd
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import llama_stack_api
+from llama_stack.distribution.ui.modules.api import LlamaStackApi
 from llama_stack.distribution.ui.modules.utils import process_dataset
 
 
@@ -39,7 +39,7 @@ def application_evaluation_page():
 
     # Select Scoring Functions to Run Evaluation On
     st.subheader("Select Scoring Functions")
-    scoring_functions = llama_stack_api.client.scoring_functions.list()
+    scoring_functions = LlamaStackApi().client.scoring_functions.list()
     scoring_functions = {sf.identifier: sf for sf in scoring_functions}
     scoring_functions_names = list(scoring_functions.keys())
     selected_scoring_functions = st.multiselect(
@@ -48,7 +48,7 @@ def application_evaluation_page():
         help="Choose one or more scoring functions.",
     )
 
-    available_models = llama_stack_api.client.models.list()
+    available_models = LlamaStackApi().client.models.list()
     available_models = [m.identifier for m in available_models]
 
     scoring_params = {}
@@ -108,7 +108,7 @@ def application_evaluation_page():
                 progress_bar.progress(progress, text=progress_text)
 
                 # Run evaluation for current row
-                score_res = llama_stack_api.run_scoring(
+                score_res = LlamaStackApi().run_scoring(
                     r,
                     scoring_function_ids=selected_scoring_functions,
                     scoring_params=scoring_params,

llama_stack/distribution/ui/page/evaluations/native_eval.py

@@ -9,13 +9,13 @@ import json
 import pandas as pd
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import llama_stack_api
+from llama_stack.distribution.ui.modules.api import LlamaStackApi
 
 
 def select_benchmark_1():
     # Select Benchmarks
     st.subheader("1. Choose An Eval Task")
-    benchmarks = llama_stack_api.client.benchmarks.list()
+    benchmarks = LlamaStackApi().client.benchmarks.list()
     benchmarks = {et.identifier: et for et in benchmarks}
     benchmarks_names = list(benchmarks.keys())
     selected_benchmark = st.selectbox(
@@ -47,7 +47,7 @@ def define_eval_candidate_2():
         # Define Eval Candidate
         candidate_type = st.radio("Candidate Type", ["model", "agent"])
 
-        available_models = llama_stack_api.client.models.list()
+        available_models = LlamaStackApi().client.models.list()
         available_models = [model.identifier for model in available_models]
         selected_model = st.selectbox(
             "Choose a model",
@@ -167,7 +167,7 @@ def run_evaluation_3():
     eval_candidate = st.session_state["eval_candidate"]
 
     dataset_id = benchmarks[selected_benchmark].dataset_id
-    rows = llama_stack_api.client.datasets.iterrows(
+    rows = LlamaStackApi().client.datasets.iterrows(
         dataset_id=dataset_id,
     )
     total_rows = len(rows.data)
@@ -208,7 +208,7 @@ def run_evaluation_3():
             progress = i / len(rows)
             progress_bar.progress(progress, text=progress_text)
             # Run evaluation for current row
-            eval_res = llama_stack_api.client.eval.evaluate_rows(
+            eval_res = LlamaStackApi().client.eval.evaluate_rows(
                 benchmark_id=selected_benchmark,
                 input_rows=[r],
                 scoring_functions=benchmarks[selected_benchmark].scoring_functions,

llama_stack/distribution/ui/page/playground/chat.py

@@ -6,12 +6,12 @@
 
 import streamlit as st
 
-from llama_stack.distribution.ui.modules.api import llama_stack_api
+from llama_stack.distribution.ui.modules.api import LlamaStackApi
 
 # Sidebar configurations
 with st.sidebar:
     st.header("Configuration")
-    available_models = llama_stack_api.client.models.list()
+    available_models = LlamaStackApi().client.models.list()
     available_models = [model.identifier for model in available_models if model.model_type == "llm"]
     selected_model = st.selectbox(
         "Choose a model",
@@ -103,7 +103,7 @@ if prompt := st.chat_input("Example: What is Llama Stack?"):
         else:
             strategy = {"type": "greedy"}
 
-        response = llama_stack_api.client.inference.chat_completion(
+        response = LlamaStackApi().client.inference.chat_completion(
             messages=[
                 {"role": "system", "content": system_prompt},
                 {"role": "user", "content": prompt},

llama_stack/distribution/ui/page/playground/rag.py

@@ -10,7 +10,7 @@ import streamlit as st
 from llama_stack_client import Agent, AgentEventLogger, RAGDocument
 
 from llama_stack.apis.common.content_types import ToolCallDelta
-from llama_stack.distribution.ui.modules.api import llama_stack_api
+from llama_stack.distribution.ui.modules.api import LlamaStackApi
 from llama_stack.distribution.ui.modules.utils import data_url_from_file
 
 
@@ -57,14 +57,14 @@ def rag_chat_page():
                     for i, uploaded_file in enumerate(uploaded_files)
                 ]
 
-                providers = llama_stack_api.client.providers.list()
+                providers = LlamaStackApi().client.providers.list()
                 vector_io_provider = None
 
                 for x in providers:
                     if x.api == "vector_io":
                         vector_io_provider = x.provider_id
 
-                llama_stack_api.client.vector_dbs.register(
+                LlamaStackApi().client.vector_dbs.register(
                     vector_db_id=vector_db_name,  # Use the user-provided name
                     embedding_dimension=384,
                     embedding_model="all-MiniLM-L6-v2",
@@ -72,7 +72,7 @@ def rag_chat_page():
                 )
 
                 # insert documents using the custom vector db name
-                llama_stack_api.client.tool_runtime.rag_tool.insert(
+                LlamaStackApi().client.tool_runtime.rag_tool.insert(
                     vector_db_id=vector_db_name,  # Use the user-provided name
                     documents=documents,
                     chunk_size_in_tokens=512,
@@ -93,7 +93,7 @@ def rag_chat_page():
         )
 
         # select memory banks
-        vector_dbs = llama_stack_api.client.vector_dbs.list()
+        vector_dbs = LlamaStackApi().client.vector_dbs.list()
         vector_dbs = [vector_db.identifier for vector_db in vector_dbs]
         selected_vector_dbs = st.multiselect(
             label="Select Document Collections to use in RAG queries",
@@ -103,7 +103,7 @@ def rag_chat_page():
         )
 
         st.subheader("Inference Parameters", divider=True)
-        available_models = llama_stack_api.client.models.list()
+        available_models = LlamaStackApi().client.models.list()
         available_models = [model.identifier for model in available_models if model.model_type == "llm"]
         selected_model = st.selectbox(
             label="Choose a model",
@@ -167,7 +167,7 @@ def rag_chat_page():
     @st.cache_resource
     def create_agent():
         return Agent(
-            llama_stack_api.client,
+            LlamaStackApi().client,
             model=selected_model,
             instructions=system_prompt,
             sampling_params={
@@ -232,7 +232,7 @@ def rag_chat_page():
             st.session_state.messages.append({"role": "system", "content": system_prompt})
 
         # Query the vector DB
-        rag_response = llama_stack_api.client.tool_runtime.rag_tool.query(
+        rag_response = LlamaStackApi().client.tool_runtime.rag_tool.query(
             content=prompt, vector_db_ids=list(selected_vector_dbs)
         )
         prompt_context = rag_response.content
@@ -251,7 +251,7 @@ def rag_chat_page():
 
             # Run inference directly
             st.session_state.messages.append({"role": "user", "content": extended_prompt})
-            response = llama_stack_api.client.inference.chat_completion(
+            response = LlamaStackApi().client.inference.chat_completion(
                 messages=st.session_state.messages,
                 model_id=selected_model,
                 sampling_params={

llama_stack/distribution/ui/page/playground/tools.py

@@ -13,7 +13,7 @@ from llama_stack_client import Agent
 from llama_stack_client.lib.agents.react.agent import ReActAgent
 from llama_stack_client.lib.agents.react.tool_parser import ReActOutput
 
-from llama_stack.distribution.ui.modules.api import llama_stack_api
+from llama_stack.distribution.ui.modules.api import LlamaStackApi
 
 
 class AgentType(enum.Enum):
@@ -24,7 +24,7 @@ class AgentType(enum.Enum):
 def tool_chat_page():
     st.title("🛠 Tools")
 
-    client = llama_stack_api.client
+    client = LlamaStackApi().client
     models = client.models.list()
     model_list = [model.identifier for model in models if model.api_model_type == "llm"]
 
@@ -55,7 +55,7 @@ def tool_chat_page():
         )
 
         if "builtin::rag" in toolgroup_selection:
-            vector_dbs = llama_stack_api.client.vector_dbs.list() or []
+            vector_dbs = LlamaStackApi().client.vector_dbs.list() or []
             if not vector_dbs:
                 st.info("No vector databases available for selection.")
             vector_dbs = [vector_db.identifier for vector_db in vector_dbs]