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d510c1d 2ce1061 d510c1d 2ce1061 d510c1d 2ce1061 d510c1d 2ce1061 d510c1d 2ce1061 d510c1d 2ce1061 d510c1d 2ce1061 d510c1d 2ce1061 d510c1d 2ce1061 d510c1d 2ce1061 d510c1d 2ce1061 d510c1d 2ce1061 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | # models.py — Typed Pydantic models for Action, Observation, and State
from typing import Optional
from pydantic import Field
from openenv.core.env_server.types import Action, Observation, State
class DebugAction(Action):
"""Action submitted by the agent: fixed code + optional explanation."""
fixed_code: str = Field(
...,
description="Complete corrected Python function. Must be valid Python including imports."
)
explanation: Optional[str] = Field(
default=None,
description="Required for hard tasks. Explain what was wrong and why your fix is correct."
)
class DebugObservation(Observation):
"""Observation returned after reset() and step()."""
task_id: str = Field(..., description="Unique task identifier e.g. easy_003")
difficulty: str = Field(..., description="Task difficulty: easy | medium | hard")
buggy_code: str = Field(..., description="The buggy Python code the agent must fix")
instructions: str = Field(..., description="Natural language instructions for the task")
test_cases_description: str = Field(..., description="What the test cases check")
# Step feedback fields
reward: Optional[float] = Field(default=None, description="Immediate reward 0.0-1.0 (null on reset)")
cumulative_reward: float = Field(default=0.0, description="Total reward accumulated this episode")
best_reward: float = Field(default=0.0, description="Best reward achieved this episode")
passed_tests: Optional[int] = Field(default=None, description="Tests passed (null on reset)")
total_tests: Optional[int] = Field(default=None, description="Total test cases (always 3)")
feedback: Optional[str] = Field(default=None, description="Per-test feedback: Input, Expected, Got")
done: bool = Field(default=False, description="True when episode complete")
class DebugState(State):
"""Internal environment state returned by GET /state."""
episode_id: str = ""
task_id: str = "none"
difficulty: str = "easy"
step_count: int = 0
max_steps: int = 5
current_reward: float = 0.0
cumulative_reward: float = 0.0
best_reward: float = 0.0
done: bool = False
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