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Clean up and formatting
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web-apps/chat/app.py

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@@ -32,44 +32,6 @@ class AppSettings(BaseModel):
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model_config = ConfigDict(protected_namespaces=(), extra="forbid")
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# class AppSettings(BaseModel):
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# hf_model_name: str = Field(
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# description="The model to use when constructing the LLM Chat client. This should match the model name running on the vLLM backend",
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# )
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# backend_url: HttpUrl = Field(
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# description="The address of the OpenAI compatible API server (either in-cluster or externally hosted)"
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# )
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# page_title: str = Field(default="Large Language Model")
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# page_description: Optional[str] = Field(default=None)
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# hf_model_instruction: str = Field(
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# default="You are a helpful and cheerful AI assistant. Please respond appropriately."
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# )
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# # Model settings
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# # For available parameters, see https://docs.vllm.ai/en/latest/dev/sampling_params.html
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# # which is based on https://platform.openai.com/docs/api-reference/completions/create
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# llm_max_tokens: int = Field(default=500)
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# llm_temperature: float = Field(default=0)
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# llm_top_p: float = Field(default=1)
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# llm_top_k: float = Field(default=-1)
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# llm_presence_penalty: float = Field(default=0, ge=-2, le=2)
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# llm_frequency_penalty: float = Field(default=0, ge=-2, le=2)
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# # UI theming
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# # Variables explicitly passed to gradio.theme.Default()
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# # For example:
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# # {"primary_hue": "red"}
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# theme_params: dict[str, Union[str, List[str]]] = Field(default_factory=dict)
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# # Overrides for theme.body_background_fill property
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# theme_background_colour: Optional[str] = Field(default=None)
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# # Provides arbitrary CSS and JS overrides to the UI,
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# # see https://www.gradio.app/guides/custom-CSS-and-JS
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# css_overrides: Optional[str] = Field(default=None)
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# custom_javascript: Optional[str] = Field(default=None)
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settings = AppSettings(**load_settings())
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logger.info(settings)
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@@ -102,7 +64,6 @@ class PossibleSystemPromptException(Exception):
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},
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streaming=True,
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)
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logger.info(llm)
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def inference(latest_message, history):
@@ -176,7 +137,6 @@ def inference(latest_message, history):
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# UI theming
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theme = gr.themes.Default(**settings.theme_params)
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theme.set(**settings.theme_params_extended)
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# theme.set(text)
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def inference_wrapper(*args):
@@ -221,44 +181,5 @@ def inference_wrapper(*args):
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)
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logger.debug("Gradio chat interface config: %s", app.config)
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app.queue(
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# Allow 10 concurrent requests to backend
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# vLLM backend should be clever enough to
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# batch these requests appropriately.
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default_concurrency_limit=10,
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).launch(server_name=settings.host_address)
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# with gr.ChatInterface(
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# inference_wrapper,
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# chatbot=gr.Chatbot(
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# # Height of conversation window in CSS units (string) or pixels (int)
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# height="68vh",
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# show_copy_button=True,
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# ),
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# textbox=gr.Textbox(
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# placeholder="Ask me anything...",
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# container=False,
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# # Ratio of text box to submit button width
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# scale=7,
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# ),
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# title=settings.page_title,
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# description=settings.page_description,
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# retry_btn="Retry",
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# undo_btn="Undo",
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# clear_btn="Clear",
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# analytics_enabled=False,
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# theme=theme,
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# css=settings.css_overrides,
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# js=settings.custom_javascript,
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# ) as app:
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# logger.debug("Gradio chat interface config: %s", app.config)
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# # For running locally in tilt dev setup
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# if len(sys.argv) > 2 and sys.argv[2] == "localhost":
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# app.launch()
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# # For running on cluster
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# else:
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# app.queue(
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# # Allow 10 concurrent requests to backend
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# # vLLM backend should be clever enough to
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# # batch these requests appropriately.
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# default_concurrency_limit=10,
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# ).launch(server_name=settings.host_address)

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