electro199
3 months ago
parent
1f410033ea
commit
35d2ff2559
@ -0,0 +1,417 @@
|
||||
from typing import Annotated, Literal, Optional
|
||||
from pydantic import BaseModel, Field, StringConstraints
|
||||
|
||||
|
||||
class RedditCreds(BaseModel):
|
||||
client_id: Annotated[
|
||||
str,
|
||||
StringConstraints(
|
||||
min_length=12, max_length=30, pattern=r"^[-a-zA-Z0-9._~+/]+=*$"
|
||||
),
|
||||
] = Field(..., description="The ID of your Reddit app of SCRIPT type")
|
||||
|
||||
client_secret: Annotated[
|
||||
str,
|
||||
StringConstraints(
|
||||
min_length=20, max_length=40, pattern=r"^[-a-zA-Z0-9._~+/]+=*$"
|
||||
),
|
||||
] = Field(..., description="The SECRET of your Reddit app of SCRIPT type")
|
||||
|
||||
username: Annotated[
|
||||
str, StringConstraints(min_length=3, max_length=20, pattern=r"^[-_0-9a-zA-Z]+$")
|
||||
] = Field(..., description="The username of your Reddit account")
|
||||
|
||||
password: Annotated[str, StringConstraints(min_length=8)] = Field(
|
||||
..., description="The password of your Reddit account"
|
||||
)
|
||||
|
||||
twofa: Optional[bool] = Field(False, description="Whether Reddit 2FA is enabled")
|
||||
|
||||
|
||||
class RedditThread(BaseModel):
|
||||
random: Optional[bool] = Field(
|
||||
False, description="If true, picks a random thread instead of asking for URL"
|
||||
)
|
||||
|
||||
subreddit: Annotated[
|
||||
str, StringConstraints(min_length=3, max_length=20, pattern=r"[_0-9a-zA-Z\+]+$")
|
||||
] = Field(..., description="Name(s) of subreddit(s), '+' separated")
|
||||
|
||||
post_id: Annotated[Optional[str], StringConstraints(pattern=r"^[+a-zA-Z0-9]*$")] = (
|
||||
Field("", description="Specify a Reddit post ID if desired")
|
||||
)
|
||||
|
||||
max_comment_length: Annotated[int, Field(ge=10, le=10000)] = Field(
|
||||
500, description="Max number of characters per comment"
|
||||
)
|
||||
|
||||
min_comment_length: Annotated[int, Field(ge=0, le=10000)] = Field(
|
||||
1, description="Min number of characters per comment"
|
||||
)
|
||||
|
||||
post_lang: Optional[str] = Field(
|
||||
"", description="Target language code for translation (e.g., 'es-cr')"
|
||||
)
|
||||
|
||||
min_comments: Annotated[int, Field(ge=10)] = Field(
|
||||
20, description="Minimum number of comments required"
|
||||
)
|
||||
|
||||
|
||||
class RedditThreadExtras(BaseModel):
|
||||
min_comments: Annotated[
|
||||
int,
|
||||
Field(
|
||||
default=20,
|
||||
ge=10,
|
||||
le=999999,
|
||||
description="The minimum number of comments a post should have to be included. Default is 20.",
|
||||
examples=[29],
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
class AIConfig(BaseModel):
|
||||
ai_similarity_enabled: Annotated[
|
||||
bool,
|
||||
Field(
|
||||
default=False,
|
||||
description="Threads read from Reddit are sorted based on their similarity to the keywords given below.",
|
||||
),
|
||||
]
|
||||
ai_similarity_keywords: Annotated[
|
||||
str,
|
||||
Field(
|
||||
default="",
|
||||
description="Every keyword or sentence, separated by commas, is used to sort Reddit threads based on similarity.",
|
||||
examples=["Elon Musk, Twitter, Stocks"],
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
class SettingsTTS(BaseModel):
|
||||
voice_choice: Annotated[
|
||||
Literal[
|
||||
"elevenlabs",
|
||||
"streamlabspolly",
|
||||
"tiktok",
|
||||
"googletranslate",
|
||||
"awspolly",
|
||||
"pyttsx",
|
||||
],
|
||||
Field(
|
||||
default="tiktok",
|
||||
description="The voice platform used for TTS generation.",
|
||||
examples=["tiktok"],
|
||||
),
|
||||
]
|
||||
random_voice: Annotated[
|
||||
bool,
|
||||
Field(
|
||||
default=True,
|
||||
description="Randomizes the voice used for each comment.",
|
||||
examples=[True],
|
||||
),
|
||||
]
|
||||
elevenlabs_voice_name: Annotated[
|
||||
Literal[
|
||||
"Adam", "Antoni", "Arnold", "Bella", "Domi", "Elli", "Josh", "Rachel", "Sam"
|
||||
],
|
||||
Field(
|
||||
default="Bella",
|
||||
description="The voice used for ElevenLabs.",
|
||||
examples=["Bella"],
|
||||
),
|
||||
]
|
||||
elevenlabs_api_key: Annotated[
|
||||
str,
|
||||
Field(
|
||||
default="",
|
||||
description="ElevenLabs API key.",
|
||||
examples=["21f13f91f54d741e2ae27d2ab1b99d59"],
|
||||
),
|
||||
]
|
||||
aws_polly_voice: Annotated[
|
||||
str,
|
||||
Field(
|
||||
default="Matthew",
|
||||
description="The voice used for AWS Polly.",
|
||||
examples=["Matthew"],
|
||||
),
|
||||
]
|
||||
streamlabs_polly_voice: Annotated[
|
||||
str,
|
||||
Field(
|
||||
default="Matthew",
|
||||
description="The voice used for Streamlabs Polly.",
|
||||
examples=["Matthew"],
|
||||
),
|
||||
]
|
||||
tiktok_voice: Annotated[
|
||||
str,
|
||||
Field(
|
||||
default="en_us_001",
|
||||
description="The voice used for TikTok TTS.",
|
||||
examples=["en_us_006"],
|
||||
),
|
||||
]
|
||||
tiktok_sessionid: Annotated[
|
||||
str,
|
||||
Field(
|
||||
default="",
|
||||
description="TikTok sessionid needed for TikTok TTS.",
|
||||
examples=["c76bcc3a7625abcc27b508c7db457ff1"],
|
||||
),
|
||||
]
|
||||
python_voice: Annotated[
|
||||
str,
|
||||
Field(
|
||||
default="1",
|
||||
description="The index of the system TTS voices (starts from 0).",
|
||||
examples=["1"],
|
||||
),
|
||||
]
|
||||
py_voice_num: Annotated[
|
||||
str,
|
||||
Field(
|
||||
default="2",
|
||||
description="The number of system voices available.",
|
||||
examples=["2"],
|
||||
),
|
||||
]
|
||||
silence_duration: Annotated[
|
||||
float,
|
||||
Field(
|
||||
default=0.3,
|
||||
description="Time in seconds between TTS comments.",
|
||||
examples=["0.1"],
|
||||
),
|
||||
]
|
||||
no_emojis: Annotated[
|
||||
bool,
|
||||
Field(
|
||||
default=False,
|
||||
description="Whether to remove emojis from the comments.",
|
||||
examples=[False],
|
||||
),
|
||||
]
|
||||
openai_api_url: Annotated[
|
||||
str,
|
||||
Field(
|
||||
default="https://api.openai.com/v1/",
|
||||
description="The API endpoint URL for OpenAI TTS generation.",
|
||||
examples=["https://api.openai.com/v1/"],
|
||||
),
|
||||
]
|
||||
openai_api_key: Annotated[
|
||||
str,
|
||||
Field(
|
||||
default="",
|
||||
description="Your OpenAI API key for TTS generation.",
|
||||
examples=["sk-abc123def456..."],
|
||||
),
|
||||
]
|
||||
openai_voice_name: Annotated[
|
||||
Literal[
|
||||
"alloy", "ash", "coral", "echo", "fable", "onyx", "nova", "sage", "shimmer"
|
||||
],
|
||||
Field(
|
||||
default="alloy",
|
||||
description="The voice used for OpenAI TTS generation.",
|
||||
examples=["alloy"],
|
||||
),
|
||||
]
|
||||
openai_model: Annotated[
|
||||
Literal["tts-1", "tts-1-hd"],
|
||||
Field(
|
||||
default="tts-1",
|
||||
description="The model variant used for OpenAI TTS generation.",
|
||||
examples=["tts-1"],
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
class SettingsBackground(BaseModel):
|
||||
background_video: Annotated[
|
||||
str,
|
||||
Field(
|
||||
default="minecraft",
|
||||
description="Sets the background for the video based on game name",
|
||||
examples=["rocket-league"],
|
||||
),
|
||||
StringConstraints(strip_whitespace=True),
|
||||
] = "minecraft"
|
||||
|
||||
background_audio: Annotated[
|
||||
str,
|
||||
Field(
|
||||
default="lofi",
|
||||
description="Sets the background audio for the video",
|
||||
examples=["chill-summer"],
|
||||
),
|
||||
StringConstraints(strip_whitespace=True),
|
||||
] = "lofi"
|
||||
|
||||
background_audio_volume: Annotated[
|
||||
float,
|
||||
Field(
|
||||
default=0.15,
|
||||
ge=0,
|
||||
le=1,
|
||||
description="Sets the volume of the background audio. If you don't want background audio, set it to 0.",
|
||||
examples=[0.05],
|
||||
),
|
||||
] = 0.15
|
||||
|
||||
enable_extra_audio: Annotated[
|
||||
bool,
|
||||
Field(
|
||||
default=False,
|
||||
description="Used if you want to render another video without background audio in a separate folder",
|
||||
),
|
||||
] = False
|
||||
|
||||
background_thumbnail: Annotated[
|
||||
bool,
|
||||
Field(
|
||||
default=False,
|
||||
description="Generate a thumbnail for the video (put a thumbnail.png file in the assets/backgrounds directory.)",
|
||||
),
|
||||
] = False
|
||||
|
||||
background_thumbnail_font_family: Annotated[
|
||||
str,
|
||||
Field(
|
||||
default="arial",
|
||||
description="Font family for the thumbnail text",
|
||||
examples=["arial"],
|
||||
),
|
||||
] = "arial"
|
||||
|
||||
background_thumbnail_font_size: Annotated[
|
||||
int,
|
||||
Field(
|
||||
default=96,
|
||||
description="Font size in pixels for the thumbnail text",
|
||||
examples=[96],
|
||||
),
|
||||
] = 96
|
||||
|
||||
background_thumbnail_font_color: Annotated[
|
||||
str,
|
||||
Field(
|
||||
default="255,255,255",
|
||||
description="Font color in RGB format for the thumbnail text",
|
||||
examples=["255,255,255"],
|
||||
),
|
||||
] = "255,255,255"
|
||||
|
||||
|
||||
class Settings(BaseModel):
|
||||
allow_nsfw: Annotated[
|
||||
bool,
|
||||
Field(
|
||||
default=False,
|
||||
description="Whether to allow NSFW content. True or False.",
|
||||
examples=[False],
|
||||
),
|
||||
]
|
||||
theme: Annotated[
|
||||
Literal["dark", "light", "transparent"],
|
||||
Field(
|
||||
default="dark",
|
||||
description="Sets the Reddit theme. For story mode, 'transparent' is also allowed.",
|
||||
examples=["light"],
|
||||
),
|
||||
]
|
||||
times_to_run: Annotated[
|
||||
int,
|
||||
Field(
|
||||
default=1,
|
||||
ge=1,
|
||||
description="Used if you want to run multiple times. Must be an int >= 1.",
|
||||
examples=[2],
|
||||
),
|
||||
]
|
||||
opacity: Annotated[
|
||||
float,
|
||||
Field(
|
||||
default=0.9,
|
||||
ge=0.0,
|
||||
le=1.0,
|
||||
description="Sets the opacity of comments when overlaid over the background.",
|
||||
examples=[0.8],
|
||||
),
|
||||
]
|
||||
storymode: Annotated[
|
||||
bool,
|
||||
Field(
|
||||
default=False,
|
||||
description="Only read out title and post content. Great for story-based subreddits.",
|
||||
examples=[False],
|
||||
),
|
||||
]
|
||||
storymodemethod: Annotated[
|
||||
Literal[0, 1],
|
||||
Field(
|
||||
default=1,
|
||||
description="Style used for story mode: 0 = static image, 1 = fancy video.",
|
||||
examples=[1],
|
||||
),
|
||||
]
|
||||
storymode_max_length: Annotated[
|
||||
int,
|
||||
Field(
|
||||
default=1000,
|
||||
ge=1,
|
||||
description="Max length (in characters) of the story mode video.",
|
||||
examples=[1000],
|
||||
),
|
||||
]
|
||||
resolution_w: Annotated[
|
||||
int,
|
||||
Field(
|
||||
default=1080,
|
||||
description="Sets the width in pixels of the final video.",
|
||||
examples=[1440],
|
||||
),
|
||||
]
|
||||
resolution_h: Annotated[
|
||||
int,
|
||||
Field(
|
||||
default=1920,
|
||||
description="Sets the height in pixels of the final video.",
|
||||
examples=[2560],
|
||||
),
|
||||
]
|
||||
zoom: Annotated[
|
||||
float,
|
||||
Field(
|
||||
default=1.0,
|
||||
ge=0.1,
|
||||
le=2.0,
|
||||
description="Sets the browser zoom level. Useful for making text larger.",
|
||||
examples=[1.1],
|
||||
),
|
||||
]
|
||||
channel_name: Annotated[
|
||||
str,
|
||||
Field(
|
||||
default="Reddit Tales",
|
||||
description="Sets the channel name for the video.",
|
||||
examples=["Reddit Stories"],
|
||||
),
|
||||
]
|
||||
tts: SettingsTTS
|
||||
background: SettingsBackground
|
||||
|
||||
|
||||
class Reddit(BaseModel):
|
||||
creds: RedditCreds
|
||||
thread: RedditThread
|
||||
|
||||
|
||||
class Config(BaseModel):
|
||||
reddit: Reddit
|
||||
ai: AIConfig
|
||||
settings: Settings
|
||||
@ -1,170 +1,143 @@
|
||||
import re
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import Dict, Tuple
|
||||
from typing import Any, Dict
|
||||
|
||||
import toml
|
||||
from rich.console import Console
|
||||
|
||||
from utils.console import handle_input
|
||||
from utils.config_model import Config
|
||||
from utils.console import print_substep
|
||||
|
||||
console = Console()
|
||||
config = dict # autocomplete
|
||||
|
||||
config: dict # autocomplete
|
||||
from typing import Any
|
||||
|
||||
from pydantic import ValidationError, BaseModel
|
||||
from pydantic_core import PydanticUndefined
|
||||
|
||||
|
||||
def prompt_recursive(obj: BaseModel):
|
||||
"""
|
||||
Recursively prompt for missing or invalid fields in a Pydantic model instance 'obj'.
|
||||
"""
|
||||
for field_name, field in obj.model_fields.items():
|
||||
value = getattr(obj, field_name, None)
|
||||
# If field is a nested BaseModel, recurse into it
|
||||
if hasattr(field.annotation, "model_fields"):
|
||||
nested_obj = value or field.annotation.model_construct()
|
||||
fixed_nested = prompt_recursive(nested_obj)
|
||||
setattr(obj, field_name, fixed_nested)
|
||||
continue
|
||||
|
||||
def crawl(obj: dict, func=lambda x, y: print(x, y, end="\n"), path=None):
|
||||
if path is None: # path Default argument value is mutable
|
||||
path = []
|
||||
for key in obj.keys():
|
||||
if type(obj[key]) is dict:
|
||||
crawl(obj[key], func, path + [key])
|
||||
# If the value is valid and not None, skip prompt
|
||||
if value not in [None, "", [], {}]:
|
||||
continue
|
||||
func(path + [key], obj[key])
|
||||
|
||||
|
||||
def check(value, checks, name):
|
||||
def get_check_value(key, default_result):
|
||||
return checks[key] if key in checks else default_result
|
||||
|
||||
incorrect = False
|
||||
if value == {}:
|
||||
incorrect = True
|
||||
if not incorrect and "type" in checks:
|
||||
try:
|
||||
value = eval(checks["type"])(value) # fixme remove eval
|
||||
except:
|
||||
incorrect = True
|
||||
|
||||
if (
|
||||
not incorrect and "options" in checks and value not in checks["options"]
|
||||
): # FAILSTATE Value is not one of the options
|
||||
incorrect = True
|
||||
if (
|
||||
not incorrect
|
||||
and "regex" in checks
|
||||
and (
|
||||
(isinstance(value, str) and re.match(checks["regex"], value) is None)
|
||||
or not isinstance(value, str)
|
||||
)
|
||||
): # FAILSTATE Value doesn't match regex, or has regex but is not a string.
|
||||
incorrect = True
|
||||
|
||||
if (
|
||||
not incorrect
|
||||
and not hasattr(value, "__iter__")
|
||||
and (
|
||||
("nmin" in checks and checks["nmin"] is not None and value < checks["nmin"])
|
||||
or ("nmax" in checks and checks["nmax"] is not None and value > checks["nmax"])
|
||||
)
|
||||
):
|
||||
incorrect = True
|
||||
if (
|
||||
not incorrect
|
||||
and hasattr(value, "__iter__")
|
||||
and (
|
||||
("nmin" in checks and checks["nmin"] is not None and len(value) < checks["nmin"])
|
||||
or ("nmax" in checks and checks["nmax"] is not None and len(value) > checks["nmax"])
|
||||
)
|
||||
):
|
||||
incorrect = True
|
||||
|
||||
if incorrect:
|
||||
value = handle_input(
|
||||
message=(
|
||||
(("[blue]Example: " + str(checks["example"]) + "\n") if "example" in checks else "")
|
||||
+ "[red]"
|
||||
+ ("Non-optional ", "Optional ")["optional" in checks and checks["optional"] is True]
|
||||
)
|
||||
+ "[#C0CAF5 bold]"
|
||||
+ str(name)
|
||||
+ "[#F7768E bold]=",
|
||||
extra_info=get_check_value("explanation", ""),
|
||||
check_type=eval(get_check_value("type", "False")), # fixme remove eval
|
||||
default=get_check_value("default", NotImplemented),
|
||||
match=get_check_value("regex", ""),
|
||||
err_message=get_check_value("input_error", "Incorrect input"),
|
||||
nmin=get_check_value("nmin", None),
|
||||
nmax=get_check_value("nmax", None),
|
||||
oob_error=get_check_value(
|
||||
"oob_error", "Input out of bounds(Value too high/low/long/short)"
|
||||
),
|
||||
options=get_check_value("options", None),
|
||||
optional=get_check_value("optional", False),
|
||||
)
|
||||
return value
|
||||
|
||||
description = field.description or ""
|
||||
default_str = (
|
||||
f" (default: {field.default})"
|
||||
if (field.default is not None) or field.default == PydanticUndefined
|
||||
else ""
|
||||
)
|
||||
prompt_msg = f"🧩 {field_name}\n 📘 {description}{default_str}\n ⚠️ Required: {field.is_required()}\n ❓ Enter value: "
|
||||
|
||||
while True:
|
||||
user_input = input(prompt_msg).strip()
|
||||
if not user_input:
|
||||
if field.default is not None:
|
||||
value_to_set = field.default
|
||||
elif not field.is_required():
|
||||
value_to_set = None
|
||||
else:
|
||||
print("⚠️ This field is required.")
|
||||
continue
|
||||
else:
|
||||
# Convert input based on type, you can expand this logic
|
||||
try:
|
||||
value_to_set = parse_value(user_input, field.annotation)
|
||||
except Exception as e:
|
||||
print(f"⚠️ Invalid input: {e}")
|
||||
continue
|
||||
|
||||
# Validate the assignment
|
||||
try:
|
||||
obj.__pydantic_validator__.validate_assignment(
|
||||
obj, field_name, value_to_set
|
||||
)
|
||||
setattr(obj, field_name, value_to_set)
|
||||
break
|
||||
except ValidationError as ve:
|
||||
for err in ve.errors():
|
||||
print(f"❌ {err['loc'][0]}: {err['msg']}")
|
||||
|
||||
def crawl_and_check(obj: dict, path: list, checks: dict = {}, name=""):
|
||||
if len(path) == 0:
|
||||
return check(obj, checks, name)
|
||||
if path[0] not in obj.keys():
|
||||
obj[path[0]] = {}
|
||||
obj[path[0]] = crawl_and_check(obj[path[0]], path[1:], checks, path[0])
|
||||
return obj
|
||||
|
||||
|
||||
def check_vars(path, checks):
|
||||
global config
|
||||
crawl_and_check(config, path, checks)
|
||||
def parse_value(raw: str, expected_type: type):
|
||||
from typing import get_args, get_origin
|
||||
|
||||
origin = get_origin(expected_type)
|
||||
args = get_args(expected_type)
|
||||
|
||||
def check_toml(template_file, config_file) -> Tuple[bool, Dict]:
|
||||
global config
|
||||
config = None
|
||||
try:
|
||||
template = toml.load(template_file)
|
||||
except Exception as error:
|
||||
console.print(f"[red bold]Encountered error when trying to to load {template_file}: {error}")
|
||||
return False
|
||||
try:
|
||||
config = toml.load(config_file)
|
||||
except toml.TomlDecodeError:
|
||||
console.print(
|
||||
f"""[blue]Couldn't read {config_file}.
|
||||
Overwrite it?(y/n)"""
|
||||
)
|
||||
if not input().startswith("y"):
|
||||
print("Unable to read config, and not allowed to overwrite it. Giving up.")
|
||||
if expected_type == bool:
|
||||
if raw.lower() in ("true", "yes", "1"):
|
||||
return True
|
||||
elif raw.lower() in ("false", "no", "0"):
|
||||
return False
|
||||
else:
|
||||
try:
|
||||
with open(config_file, "w") as f:
|
||||
f.write("")
|
||||
except:
|
||||
console.print(
|
||||
f"[red bold]Failed to overwrite {config_file}. Giving up.\nSuggestion: check {config_file} permissions for the user."
|
||||
)
|
||||
return False
|
||||
except FileNotFoundError:
|
||||
console.print(
|
||||
f"""[blue]Couldn't find {config_file}
|
||||
Creating it now."""
|
||||
)
|
||||
try:
|
||||
with open(config_file, "x") as f:
|
||||
f.write("")
|
||||
config = {}
|
||||
except:
|
||||
console.print(
|
||||
f"[red bold]Failed to write to {config_file}. Giving up.\nSuggestion: check the folder's permissions for the user."
|
||||
)
|
||||
return False
|
||||
raise ValueError("Expected boolean value (true/false)")
|
||||
elif expected_type == int:
|
||||
return int(raw)
|
||||
elif expected_type == float:
|
||||
return float(raw)
|
||||
elif expected_type == str:
|
||||
return raw
|
||||
elif origin == list and args:
|
||||
return [parse_value(x.strip(), args[0]) for x in raw.split(",")]
|
||||
else:
|
||||
raise ValueError(f"Unsupported field type: {expected_type}")
|
||||
|
||||
|
||||
def check_toml(config_file: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Load the template and config TOML files.
|
||||
Validate config with Pydantic.
|
||||
If invalid, prompt for missing or invalid fields.
|
||||
Save fixed config back.
|
||||
Return the valid Config model.
|
||||
"""
|
||||
try:
|
||||
config_dict = toml.load(config_file)
|
||||
except Exception as e:
|
||||
print(f"Failed to load config {config_file}: {e}")
|
||||
config_dict = {}
|
||||
|
||||
console.print(
|
||||
"""\
|
||||
[blue bold]###############################
|
||||
# #
|
||||
# Checking TOML configuration #
|
||||
# #
|
||||
###############################
|
||||
If you see any prompts, that means that you have unset/incorrectly set variables, please input the correct values.\
|
||||
"""
|
||||
)
|
||||
crawl(template, check_vars)
|
||||
with open(config_file, "w") as f:
|
||||
toml.dump(config, f)
|
||||
try:
|
||||
config_instance = Config.model_validate(config_dict)
|
||||
except ValidationError as e:
|
||||
print("Config validation failed, will prompt for missing/invalid fields:")
|
||||
print(e)
|
||||
# Start from a clean model
|
||||
config_instance = Config.model_construct()
|
||||
# Update model with any valid partial data loaded from config
|
||||
for k, v in config_dict.items():
|
||||
if hasattr(config_instance, k):
|
||||
setattr(config_instance, k, v)
|
||||
|
||||
# Prompt for missing or invalid fields recursively
|
||||
config_instance = prompt_recursive(config_instance)
|
||||
|
||||
# Validate again to be sure
|
||||
config_instance = Config.model_validate(config_instance.model_dump())
|
||||
|
||||
# Save fixed config back to file
|
||||
with open(config_file, "w", encoding="utf-8") as f:
|
||||
toml.dump(config_instance.model_dump(), f)
|
||||
print(f"Updated config saved to {config_file}")
|
||||
config = config_instance.model_dump()
|
||||
return config
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
directory = Path().absolute()
|
||||
check_toml(f"{directory}/utils/.config.template.toml", "config.toml")
|
||||
check_toml("config.toml")
|
||||
Loading…
Reference in new issue