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import json import datetime from typing import Tuple, Dict, Type, Callable, Union from typing import Literal import param import instructor from openai import OpenAI from pydantic import BaseModel, Field, create_model as _create_model from pydantic.fields import FieldInfo import panel as pn DATE_TYPE = Union[datetime.datetime, datetime.date] PARAM_TYPE_MAPPING: Dict[param.Parameter, Type] = { param.String: str, param.Integer: int, param.Number: float, param.Boolean: bool, param.Event: bool, param.Date: DATE_TYPE, param.DateRange: Tuple[DATE_TYPE], param.CalendarDate: DATE_TYPE, param.CalendarDateRange: Tuple[DATE_TYPE], param.Parameter: object, param.Color: str, param.Callable: Callable, param.List: list, param.ObjectSelector: object, } pn.extension() class FieldWidgetName(BaseModel): label: str widget_name: Literal[pn.widgets.__all__] class BestMatch(BaseModel): response: str = Field(description="Be a helpful chatbot assistant.") requires_widget: bool = Field( description="Whether the query requires a widget to be created." ) field_widget: FieldWidgetName | None = Field( default=None, description=( "The most suitable widgets to use to collect " "user input in a form based on the query." ), ) def _create_model_from_widget(widget_cls: Type[pn.widgets.Widget]) -> Type[BaseModel]: param_fields = {} common_keys = pn.widgets.Widget.param.values().keys() for key in widget_cls.param.values().keys() - common_keys | {"name"}: type_ = PARAM_TYPE_MAPPING.get(type(widget_cls.param[key]), str) param_fields[key] = ( type_, FieldInfo( description=getattr(widget_cls.param, key).doc, default=None, required=False, ), ) doc = ( "Hydrate this based on the initial query. Ensure the `name` is human readable." ) return _create_model(widget_cls.__name__, __doc__=doc, **param_fields) def _hydrate_widget(widget_cls: Type[pn.widgets.Widget], **kwargs) -> pn.widgets.Widget: return widget_cls( **{key: value for key, value in kwargs.items() if value is not None} ) def _format_message(content: str, role: str = "user"): return {"role": role, "content": str(content)} def _generate_response(messages: list, response_model: Type[BaseModel]): return client.chat.completions.create( model="gpt-4", response_model=response_model, messages=messages ) def _create_widget(best_match): widget_cls = getattr(pn.widgets, best_match.widget_name) widget_label = best_match.label widget_model = _create_model_from_widget(widget_cls) messages.extend( [ _format_message( f"Creating {json.dumps(widget_model.model_json_schema())} for {widget_label}", role="assistant", ) ] ) kwargs = _generate_response(messages, widget_model) widget = _hydrate_widget(widget_cls, **dict(kwargs)) pn.bind( react_to_value, widget, name=widget.name, watch=True, ) return widget def react_to_value(value, name): content = f"Selected: {value} from {name=} widget." messages.append(_format_message(content)) chat.send(value) chat.widgets = [pn.chat.ChatAreaInput()] def respond(query: str, user: str, instance: pn.chat.ChatInterface): messages.append(_format_message(query)) best_match = _generate_response(messages, BestMatch) yield best_match.response messages.append(_format_message(best_match.response, role="assistant")) if best_match.requires_widget: widget = _create_widget(best_match.field_widget) instance.widgets = [widget] messages = [] client = instructor.patch(OpenAI()) chat = pn.chat.ChatInterface( callback=respond, auto_send_types=[], help_text="Help answer your questions using Panel widgets.", callback_exception="raise", ) chat.show()
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Screen.Recording.2024-03-04.at.5.35.11.PM.mov
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