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from abc import ABC, abstractmethod
from concurrent.futures import ThreadPoolExecutor
from typing import Dict, List, Union
import gradio as gr
import ai
class Component(ABC):
def __init__(self, id_: int):
# Internal state
self._id = id_
self._source = self.__class__.__name__
self.vname: str
# Gradio state
self.component_id: gr.Number
self.gr_component: Union[gr.Box, gr.Textbox]
self.output: gr.Textbox
self.visible: gr.Number
def render(self) -> None:
self.component_id = gr.Number(value=self._id, visible=False)
self.visible = gr.Number(0, visible=False)
self.gr_component = self._render(self._id)
@abstractmethod
def _render(self, id_: int) -> Union[gr.Box, gr.Textbox]:
...
class Input(Component):
vname = "v"
def _render(self, id_: int) -> gr.Textbox:
self.output = gr.Textbox(
label=f"Input: {{{self.vname}{id_}}}",
interactive=True,
placeholder="Variable value",
visible=False,
)
return self.output
class TaskComponent(ABC):
vname = "t"
def __init__(self):
self.name: str
self.gr_component: gr.Box
self.input: gr.Textbox
self.output: gr.Textbox
self._source = self.__class__.__name__
def render(self, id_: int) -> None:
self.gr_component = self._render(id_)
@abstractmethod
def _render(self, id_) -> gr.Box:
...
@abstractmethod
def execute(self, input):
...
class AITask(TaskComponent):
name = "AI Task"
def _render(self, id_: int) -> gr.Box:
with gr.Box(visible=False) as gr_component:
gr.Markdown("Send a message to ChatGPT.")
with gr.Row():
self.input = gr.Textbox(
label="Prompt",
lines=10,
interactive=True,
placeholder="Example: summarize this text: {v0}",
)
self.output = gr.Textbox(
label=f"Output: {{{self.vname}{id_}}}",
lines=10,
interactive=True,
)
return gr_component
def execute(self, prompt: str) -> str:
return ai.llm.next([{"role": "user", "content": prompt}])
class CodeTask(TaskComponent):
name = "Code Task"
def _render(self, id_: int) -> gr.Column:
with gr.Column(visible=False) as gr_component:
code_prompt = gr.Textbox(
label="What would you like to do?",
interactive=True,
)
generate_code = gr.Button("Generate code")
with gr.Row():
with gr.Column():
with gr.Accordion(label="Generated code", open=False):
raw_prompt_output = gr.Textbox(
label="Raw output",
lines=5,
interactive=True,
)
self.packages = gr.Textbox(
label="The following packages will be installed",
interactive=True,
)
self.function = gr.Textbox(
label="Code to be executed",
lines=10,
interactive=True,
)
error_message = gr.HighlightedText(value=None, visible=False)
self.input = gr.Textbox(
interactive=True,
placeholder="Input to the code",
show_label=False,
)
with gr.Column():
self.output = gr.Textbox(
label=f"Output: {{{self.vname}{id_}}}",
lines=10,
interactive=True,
)
generate_code.click(
self.generate_code,
inputs=[code_prompt],
outputs=[
raw_prompt_output,
self.packages,
self.function,
error_message,
],
)
return gr_component
@staticmethod
def generate_code(code_prompt: str):
raw_prompt_output = ""
packages = ""
function = ""
error_message = gr.HighlightedText.update(None, visible=False)
if not code_prompt:
return (
raw_prompt_output,
packages,
function,
error_message,
)
try:
raw_prompt_output = ai.llm.next(
[
{
"role": "user",
"content": f"""
Write a python function for the following request:
{code_prompt}
Do't save anything to disk. Instead, the function should return the necessary data.
Include all the necessary imports but put them inside the function itself.
""",
}
],
temperature=0,
)
def llm_call(prompt):
return ai.llm.next([{"role": "user", "content": prompt}], temperature=0)
with ThreadPoolExecutor(max_workers=2) as executor:
packages, function = tuple(
executor.map(
llm_call,
[
f"""
The following text should have a python function with some imports that might need to be installed:
{raw_prompt_output}
Extract all the python packages, nothing else. Print them in a single python list what can be used with eval().
""",
f"""
The following text should have a python function:
{raw_prompt_output}
Exclusively extract the function, nothing else.
""",
],
)
)
except Exception as e:
error_message = gr.HighlightedText.update(
value=[(str(e), "ERROR")], visible=True
)
return (
raw_prompt_output,
packages,
function,
error_message,
)
def execute(self, url: str) -> str:
...
class Task(Component):
available_tasks = [AITask, CodeTask]
vname = "t"
def __init__(self, id_: int, visible: bool = False):
super().__init__(id_)
self._inner_tasks = [t() for t in self.available_tasks]
self.gr_component: gr.Box
def _render(self, id_: int) -> gr.Box:
with gr.Box(visible=False) as gr_component:
self.active_index = gr.Dropdown(
[AITask.name, CodeTask.name],
label="Pick a new Task",
type="index",
)
for t in self._inner_tasks:
t.render(id_)
self.active_index.select(
self.pick_task,
inputs=[self.active_index],
outputs=[t.gr_component for t in self._inner_tasks],
)
return gr_component
@staticmethod
def pick_task(idx: int) -> List[Dict]:
update = [gr.Box.update(visible=False)] * len(Task.available_tasks)
update[idx] = gr.Box.update(visible=True)
return update
def inputs(self) -> List[gr.Textbox]:
return [t.input for t in self._inner_tasks]
def outputs(self) -> List[gr.Textbox]:
return [t.output for t in self._inner_tasks]
def execute(self, active_index, input):
inner_task = self._inner_tasks[active_index]
print(f"Executing {self._source}: {self._id}")
return inner_task.execute(input)
MAX_TASKS = 10
all_tasks = {i: Task(i) for i in range(MAX_TASKS)}
class Tasks:
@classmethod
def visibilities(cls) -> List[gr.Number]:
return [t.visible for t in all_tasks.values()]
@classmethod
def active_indexes(cls) -> List[gr.Dropdown]:
return [t.active_index for t in all_tasks.values()]
@classmethod
def gr_components(cls) -> List[gr.Box]:
return [t.gr_component for t in all_tasks.values()]
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