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Fix precommit check after moving to ruff (#927)

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Lint check in main branch is failing. This fixes the lint check after we
moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We
need to move to a `ruff.toml` file as well as fixing and ignoring some
additional checks.

Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
This commit is contained in:
Yuan Tang 2025-02-02 09:46:45 -05:00 • committed by GitHub
parent 4773092dd1
commit 34ab7a3b6c
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GPG key ID: B5690EEEBB952194
217 changed files with 981 additions and 2681 deletions
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8
llama_stack/distribution/ui/app.py
View file

@ -22,15 +22,11 @@ def main():
)
# Playground pages
chat_page = st.Page(
"page/playground/chat.py", title="Chat", icon="💬", default=True
)
chat_page = st.Page("page/playground/chat.py", title="Chat", icon="💬", default=True)
rag_page = st.Page("page/playground/rag.py", title="RAG", icon="💬", default=False)
# Distribution pages
resources_page = st.Page(
"page/distribution/resources.py", title="Resources", icon="🔍", default=False
)
resources_page = st.Page("page/distribution/resources.py", title="Resources", icon="🔍", default=False)
provider_page = st.Page(
"page/distribution/providers.py",
title="API Providers",

8
llama_stack/distribution/ui/modules/api.py
View file

@ -23,15 +23,11 @@ class LlamaStackApi:
},
)
def run_scoring(
self, row, scoring_function_ids: list[str], scoring_params: Optional[dict]
):
def run_scoring(self, row, scoring_function_ids: list[str], scoring_params: Optional[dict]):
"""Run scoring on a single row"""
if not scoring_params:
scoring_params = {fn_id: None for fn_id in scoring_function_ids}
return self.client.scoring.score(
input_rows=[row], scoring_functions=scoring_params
)
return self.client.scoring.score(input_rows=[row], scoring_functions=scoring_params)
llama_stack_api = LlamaStackApi()

4
llama_stack/distribution/ui/page/distribution/datasets.py
View file

@ -11,9 +11,7 @@ from modules.api import llama_stack_api
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 llama_stack_api.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)

8
llama_stack/distribution/ui/page/distribution/eval_tasks.py
View file

@ -12,12 +12,8 @@ def eval_tasks():
# Eval Tasks Section
st.header("Eval Tasks")
eval_tasks_info = {
d.identifier: d.to_dict() for d in llama_stack_api.client.eval_tasks.list()
}
eval_tasks_info = {d.identifier: d.to_dict() for d in llama_stack_api.client.eval_tasks.list()}
if len(eval_tasks_info) > 0:
selected_eval_task = st.selectbox(
"Select an eval task", list(eval_tasks_info.keys()), key="eval_task_inspect"
)
selected_eval_task = st.selectbox("Select an eval task", list(eval_tasks_info.keys()), key="eval_task_inspect")
st.json(eval_tasks_info[selected_eval_task], expanded=True)

4
llama_stack/distribution/ui/page/distribution/models.py
View file

@ -11,9 +11,7 @@ from modules.api import llama_stack_api
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 llama_stack_api.client.models.list()}
selected_model = st.selectbox("Select a model", list(models_info.keys()))
st.json(models_info[selected_model])

9
llama_stack/distribution/ui/page/distribution/scoring_functions.py
View file

@ -11,12 +11,7 @@ from modules.api import llama_stack_api
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 llama_stack_api.client.scoring_functions.list()}
selected_scoring_function = st.selectbox(
"Select a scoring function", list(scoring_functions_info.keys())
)
selected_scoring_function = st.selectbox("Select a scoring function", list(scoring_functions_info.keys()))
st.json(scoring_functions_info[selected_scoring_function], expanded=True)

4
llama_stack/distribution/ui/page/distribution/shields.py
View file

@ -12,9 +12,7 @@ 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 llama_stack_api.client.shields.list()}
selected_shield = st.selectbox("Select a shield", list(shields_info.keys()))
st.json(shields_info[selected_shield])

8
llama_stack/distribution/ui/page/distribution/vector_dbs.py
View file

@ -10,14 +10,10 @@ from modules.api import llama_stack_api
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 llama_stack_api.client.vector_dbs.list()}
if len(vector_dbs_info) > 0:
selected_vector_db = st.selectbox(
"Select a vector database", list(vector_dbs_info.keys())
)
selected_vector_db = st.selectbox("Select a vector database", list(vector_dbs_info.keys()))
st.json(vector_dbs_info[selected_vector_db])
else:
st.info("No vector databases found")

9
llama_stack/distribution/ui/page/evaluations/app_eval.py
View file

@ -14,7 +14,6 @@ from modules.utils import process_dataset
def application_evaluation_page():
st.set_page_config(page_title="Evaluations (Scoring)", page_icon="🦙")
st.title("📊 Evaluations (Scoring)")
@ -83,9 +82,7 @@ def application_evaluation_page():
try:
new_params[param_name] = json.loads(value)
except json.JSONDecodeError:
st.error(
f"Invalid JSON for **{param_name}** in {scoring_fn_id}"
)
st.error(f"Invalid JSON for **{param_name}** in {scoring_fn_id}")
st.json(new_params)
scoring_params[scoring_fn_id] = new_params
@ -128,9 +125,7 @@ def application_evaluation_page():
output_res[fn_id].append(score_res.results[fn_id].score_rows[0])
# Display current row results using separate containers
progress_text_container.write(
f"Expand to see current processed result ({i + 1} / {len(rows)})"
)
progress_text_container.write(f"Expand to see current processed result ({i + 1} / {len(rows)})")
results_container.json(
score_res.to_json(),
expanded=2,

6
llama_stack/distribution/ui/page/evaluations/native_eval.py
View file

@ -195,7 +195,6 @@ def run_evaluation_3():
# Add run button and handle evaluation
if st.button("Run Evaluation"):
progress_text = "Running evaluation..."
progress_bar = st.progress(0, text=progress_text)
rows = rows.rows
@ -233,9 +232,7 @@ def run_evaluation_3():
output_res[scoring_fn] = []
output_res[scoring_fn].append(eval_res.scores[scoring_fn].score_rows[0])
progress_text_container.write(
f"Expand to see current processed result ({i + 1} / {len(rows)})"
)
progress_text_container.write(f"Expand to see current processed result ({i + 1} / {len(rows)})")
results_container.json(eval_res, expanded=2)
progress_bar.progress(1.0, text="Evaluation complete!")
@ -247,7 +244,6 @@ def run_evaluation_3():
def native_evaluation_page():
st.set_page_config(page_title="Evaluations (Generation + Scoring)", page_icon="🦙")
st.title("📊 Evaluations (Generation + Scoring)")

8
llama_stack/distribution/ui/page/playground/chat.py
View file

@ -11,9 +11,7 @@ from modules.api import llama_stack_api
with st.sidebar:
st.header("Configuration")
available_models = llama_stack_api.client.models.list()
available_models = [
model.identifier for model in available_models if model.model_type == "llm"
]
available_models = [model.identifier for model in available_models if model.model_type == "llm"]
selected_model = st.selectbox(
"Choose a model",
available_models,
@ -128,6 +126,4 @@ if prompt := st.chat_input("Example: What is Llama Stack?"):
full_response = response
message_placeholder.markdown(full_response.completion_message.content)
st.session_state.messages.append(
{"role": "assistant", "content": full_response}
)
st.session_state.messages.append({"role": "assistant", "content": full_response})

12
llama_stack/distribution/ui/page/playground/rag.py
View file

@ -74,9 +74,7 @@ def rag_chat_page():
)
available_models = llama_stack_api.client.models.list()
available_models = [
model.identifier for model in available_models if model.model_type == "llm"
]
available_models = [model.identifier for model in available_models if model.model_type == "llm"]
selected_model = st.selectbox(
"Choose a model",
available_models,
@ -137,9 +135,7 @@ def rag_chat_page():
dict(
name="builtin::rag",
args={
"vector_db_ids": [
vector_db_id for vector_db_id in selected_vector_dbs
],
"vector_db_ids": [vector_db_id for vector_db_id in selected_vector_dbs],
},
)
],
@ -186,9 +182,7 @@ def rag_chat_page():
message_placeholder.markdown(full_response + "▌")
message_placeholder.markdown(full_response)
st.session_state.messages.append(
{"role": "assistant", "content": full_response}
)
st.session_state.messages.append({"role": "assistant", "content": full_response})
rag_chat_page()