mirror of
https://github.com/aljazceru/gpt-engineer.git
synced 2025-12-17 12:45:26 +01:00
145 lines
3.8 KiB
Python
145 lines
3.8 KiB
Python
import json
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import random
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import tempfile
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from dataclasses import dataclass, field
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from datetime import datetime
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from pathlib import Path
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from typing import List, Optional
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from dataclasses_json import dataclass_json
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from termcolor import colored
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from gpt_engineer.db import DB, DBs
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from gpt_engineer.domain import Step
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@dataclass_json
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@dataclass
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class Review:
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ran: Optional[bool]
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perfect: Optional[bool]
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works: Optional[bool]
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comments: str
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raw: str
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@dataclass_json
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@dataclass
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class Learning:
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model: str
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temperature: float
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steps: str
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steps_file_hash: str
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prompt: str
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logs: str
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workspace: str
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feedback: Optional[str]
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session: str
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review: Optional[Review]
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timestamp: str = field(default_factory=lambda: datetime.utcnow().isoformat())
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version: str = "0.3"
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TERM_CHOICES = (
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colored("y", "green")
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+ "/"
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+ colored("n", "red")
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+ "/"
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+ colored("u", "yellow")
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+ "(ncertain): "
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)
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def human_input() -> Review:
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print()
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print(
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colored("To help gpt-engineer learn, please answer 3 questions:", "light_green")
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)
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print()
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ran = input("Did the generated code run at all? " + TERM_CHOICES)
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while ran not in ("y", "n", "u", ""):
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ran = input("Invalid input. Please enter y, n, or u: ")
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perfect = ""
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useful = ""
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if ran == "y":
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perfect = input(
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"Did the generated code do everything you wanted? " + TERM_CHOICES
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)
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while perfect not in ("y", "n", "u", ""):
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perfect = input("Invalid input. Please enter y, n, or u: ")
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if perfect != "y":
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useful = input("Did the generated code do anything useful? " + TERM_CHOICES)
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while useful not in ("y", "n", "u", ""):
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useful = input("Invalid input. Please enter y, n, or u: ")
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comments = ""
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if perfect != "y":
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comments = input(
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"If you have time, please explain what was not working "
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+ colored("(ok to leave blank)\n", "light_green")
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)
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print(colored("Thank you", "light_green"))
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return Review(
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raw=", ".join([ran, perfect, useful]),
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ran={"y": True, "n": False, "u": None, "": None}[ran],
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works={"y": True, "n": False, "u": None, "": None}[useful],
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perfect={"y": True, "n": False, "u": None, "": None}[perfect],
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comments=comments,
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)
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def logs_to_string(steps: List[Step], logs: DB):
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chunks = []
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for step in steps:
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chunks.append(f"--- {step.__name__} ---\n")
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messages = json.loads(logs[step.__name__])
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chunks.append(format_messages(messages))
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return "\n".join(chunks)
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def format_messages(messages: List[dict]) -> str:
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return "\n".join(
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[f"{message['role']}:\n\n{message['content']}" for message in messages]
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)
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def extract_learning(
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model: str, temperature: float, steps: List[Step], dbs: DBs, steps_file_hash
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) -> Learning:
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review = None
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if "review" in dbs.memory:
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review = Review.from_json(dbs.memory["review"]) # type: ignore
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learning = Learning(
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prompt=dbs.input["prompt"],
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model=model,
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temperature=temperature,
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steps=json.dumps([step.__name__ for step in steps]),
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steps_file_hash=steps_file_hash,
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feedback=dbs.input.get("feedback"),
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session=get_session(),
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logs=logs_to_string(steps, dbs.logs),
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workspace=dbs.workspace["all_output.txt"],
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review=review,
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)
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return learning
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def get_session():
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path = Path(tempfile.gettempdir()) / "gpt_engineer_user_id.txt"
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try:
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if path.exists():
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user_id = path.read_text()
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else:
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# random uuid:
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user_id = str(random.randint(0, 2**32))
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path.write_text(user_id)
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return user_id
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except IOError:
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return "ephemeral_" + str(random.randint(0, 2**32))
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