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simulate email campaign
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""Clothing Brand Ctr Env Environment Client."""
from typing import Dict
from openenv.core.client_types import StepResult
from openenv.core.env_server.types import State
from openenv.core import EnvClient
from .models import ClothingBrandCtrAction, ClothingBrandCtrObservation
class ClothingBrandCtrEnv(
EnvClient[ClothingBrandCtrAction, ClothingBrandCtrObservation, State]
):
"""
Client for the Clothing Brand Ctr Env Environment.
This client maintains a persistent WebSocket connection to the environment server,
enabling efficient multi-step interactions with lower latency.
Each client instance has its own dedicated environment session on the server.
Example:
>>> # Connect to a running server
>>> with ClothingBrandCtrEnv(base_url="http://localhost:8000") as client:
... result = client.reset()
... print(result.observation.preview_text)
...
... result = client.step(
... ClothingBrandCtrAction(
... brand_name="AIRPORT CLUB",
... target_audience="style-focused people that travel",
... brand_voice="bold, easy-going, fun, wholesome",
... key_value_prop="premium t-shirts when traveling by plane",
... call_to_action="Shop the launch collection now",
... )
... )
... print(result.observation.validation_passed)
Example with Docker:
>>> # Automatically start container and connect
>>> client = ClothingBrandCtrEnv.from_docker_image("clothing_brand_ctr_env-env:latest")
>>> try:
... result = client.reset()
... result = client.step(
... ClothingBrandCtrAction(
... brand_name="ARPRT CLUB",
... target_audience="traveler",
... brand_voice="bold",
... key_value_prop="premium essentials with runway-level polish",
... call_to_action="Shop the launch collection now",
... )
... )
... finally:
... client.close()
"""
def _step_payload(self, action: ClothingBrandCtrAction) -> Dict:
"""
Convert ClothingBrandCtrAction to JSON payload for step message.
Args:
action: ClothingBrandCtrAction instance
Returns:
Dictionary representation suitable for JSON encoding
"""
return {
"brand_name": action.brand_name,
"target_audience": action.target_audience,
"brand_voice": action.brand_voice,
"key_value_prop": action.key_value_prop,
"call_to_action": action.call_to_action,
"metadata": action.metadata,
}
def _parse_result(self, payload: Dict) -> StepResult[ClothingBrandCtrObservation]:
"""
Parse server response into StepResult[ClothingBrandCtrObservation].
Args:
payload: JSON response data from server
Returns:
StepResult with ClothingBrandCtrObservation
"""
obs_data = payload.get("observation", {})
observation = ClothingBrandCtrObservation(
subject_line=obs_data.get("subject_line", ""),
preview_text=obs_data.get("preview_text", ""),
email_copy=obs_data.get("email_copy", ""),
word_count=obs_data.get("word_count", 0),
validation=obs_data.get("validation", {}),
validation_passed=obs_data.get("validation_passed", False),
ctr_proxy_score=obs_data.get("ctr_proxy_score", 0.0),
done=payload.get("done", False),
reward=payload.get("reward"),
metadata=obs_data.get("metadata", {}),
)
return StepResult(
observation=observation,
reward=payload.get("reward"),
done=payload.get("done", False),
)
def _parse_state(self, payload: Dict) -> State:
"""
Parse server response into State object.
Args:
payload: JSON response from state request
Returns:
State object with episode_id and step_count
"""
return State(
episode_id=payload.get("episode_id"),
step_count=payload.get("step_count", 0),
)