mirror of
https://github.com/hwchase17/langchain.git
synced 2025-11-26 12:51:03 +00:00
a0c2281540
```python
"""python scripts/update_mypy_ruff.py"""
import glob
import tomllib
from pathlib import Path
import toml
import subprocess
import re
ROOT_DIR = Path(__file__).parents[1]
def main():
for path in glob.glob(str(ROOT_DIR / "libs/**/pyproject.toml"), recursive=True):
print(path)
with open(path, "rb") as f:
pyproject = tomllib.load(f)
try:
pyproject["tool"]["poetry"]["group"]["typing"]["dependencies"]["mypy"] = (
"^1.10"
)
pyproject["tool"]["poetry"]["group"]["lint"]["dependencies"]["ruff"] = (
"^0.5"
)
except KeyError:
continue
with open(path, "w") as f:
toml.dump(pyproject, f)
cwd = "/".join(path.split("/")[:-1])
completed = subprocess.run(
"poetry lock --no-update; poetry install --with typing; poetry run mypy . --no-color",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
logs = completed.stdout.split("\n")
to_ignore = {}
for l in logs:
if re.match("^(.*)\:(\d+)\: error:.*\[(.*)\]", l):
path, line_no, error_type = re.match(
"^(.*)\:(\d+)\: error:.*\[(.*)\]", l
).groups()
if (path, line_no) in to_ignore:
to_ignore[(path, line_no)].append(error_type)
else:
to_ignore[(path, line_no)] = [error_type]
print(len(to_ignore))
for (error_path, line_no), error_types in to_ignore.items():
all_errors = ", ".join(error_types)
full_path = f"{cwd}/{error_path}"
try:
with open(full_path, "r") as f:
file_lines = f.readlines()
except FileNotFoundError:
continue
file_lines[int(line_no) - 1] = (
file_lines[int(line_no) - 1][:-1] + f" # type: ignore[{all_errors}]\n"
)
with open(full_path, "w") as f:
f.write("".join(file_lines))
subprocess.run(
"poetry run ruff format .; poetry run ruff --select I --fix .",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
if __name__ == "__main__":
main()
```
68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
from __future__ import annotations
|
|
|
|
import logging
|
|
from typing import Optional
|
|
|
|
from langchain_core.callbacks import CallbackManagerForToolRun
|
|
|
|
from langchain_community.tools.edenai.edenai_base_tool import EdenaiTool
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class EdenAiExplicitImageTool(EdenaiTool):
|
|
"""Tool that queries the Eden AI Explicit image detection.
|
|
|
|
for api reference check edenai documentation:
|
|
https://docs.edenai.co/reference/image_explicit_content_create.
|
|
|
|
To use, you should have
|
|
the environment variable ``EDENAI_API_KEY`` set with your API token.
|
|
You can find your token here: https://app.edenai.run/admin/account/settings
|
|
|
|
"""
|
|
|
|
name: str = "edenai_image_explicit_content_detection"
|
|
|
|
description: str = (
|
|
"A wrapper around edenai Services Explicit image detection. "
|
|
"""Useful for when you have to extract Explicit Content from images.
|
|
it detects adult only content in images,
|
|
that is generally inappropriate for people under
|
|
the age of 18 and includes nudity, sexual activity,
|
|
pornography, violence, gore content, etc."""
|
|
"Input should be the string url of the image ."
|
|
)
|
|
|
|
combine_available: bool = True
|
|
feature: str = "image"
|
|
subfeature: str = "explicit_content"
|
|
|
|
def _parse_json(self, json_data: dict) -> str:
|
|
result_str = f"nsfw_likelihood: {json_data['nsfw_likelihood']}\n"
|
|
for idx, found_obj in enumerate(json_data["items"]):
|
|
label = found_obj["label"].lower()
|
|
likelihood = found_obj["likelihood"]
|
|
result_str += f"{idx}: {label} likelihood {likelihood},\n"
|
|
|
|
return result_str[:-2]
|
|
|
|
def _parse_response(self, json_data: list) -> str:
|
|
if len(json_data) == 1:
|
|
result = self._parse_json(json_data[0])
|
|
else:
|
|
for entry in json_data:
|
|
if entry.get("provider") == "eden-ai":
|
|
result = self._parse_json(entry)
|
|
|
|
return result
|
|
|
|
def _run(
|
|
self,
|
|
query: str,
|
|
run_manager: Optional[CallbackManagerForToolRun] = None,
|
|
) -> str:
|
|
"""Use the tool."""
|
|
query_params = {"file_url": query, "attributes_as_list": False}
|
|
return self._call_eden_ai(query_params)
|