initial commit.
This commit is contained in:
commit
bd065bfbdf
3
.gitignore
vendored
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3
.gitignore
vendored
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@ -0,0 +1,3 @@
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.venv/
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.env
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.idea/
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31
Dockerfile
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31
Dockerfile
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@ -0,0 +1,31 @@
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# Use an official Python runtime as a parent image
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FROM python:3.13-slim
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# Set environment variables to ensure Python outputs everything to the terminal and to not generate .pyc files
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ENV PYTHONUNBUFFERED=1
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ENV PYTHONDONTWRITEBYTECODE=1
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# Set the working directory in the container
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WORKDIR /app
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# Copy the current directory contents into the container
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COPY . /app/
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# Install system dependencies for Python packages
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RUN apt-get update && apt-get install -y \
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gcc \
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libpq-dev \
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libgl1-mesa-glx \
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libglib2.0-0 \
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--no-install-recommends && \
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rm -rf /var/lib/apt/lists/*
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# Install project dependencies
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RUN pip install --no-cache-dir --upgrade pip
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RUN pip install --no-cache-dir -r /app/requirements.txt
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# Expose port 8000 for the application
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EXPOSE 8000
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# Command to run the application using gunicorn
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CMD ["gunicorn", "-w", "4", "--bind", "0.0.0.0:8000", "main:app"]
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672
main.py
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672
main.py
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# Standard library imports
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import os
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import json
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import time
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import random
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import logging
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import hashlib
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from functools import wraps
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from pathlib import Path
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from typing import Tuple, Optional, Dict, Any
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from enum import Enum
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import shutil
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# Third-party library imports
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import requests
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from requests import ConnectionError
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from flask import Flask, send_file, jsonify, request
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from PIL import Image
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import numpy as np
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import cv2
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from pydantic import BaseModel, Field, HttpUrl, field_validator
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from dotenv import load_dotenv
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import psutil
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load_dotenv()
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Directory setup
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IMAGES_DIR = Path("images")
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IMAGES_DIR.mkdir(exist_ok=True)
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# Custom Exceptions
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class ImageProcessingError(Exception):
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"""Base exception for image processing errors"""
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pass
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class APIError(Exception):
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"""Exception for API-related errors"""
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pass
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class ImageNotFoundError(Exception):
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"""Exception for missing image files"""
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pass
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# Cache Configuration
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CACHE_EXPIRY_SECONDS = 3 * 60 * 60 # 3 hours
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_api_cache: Dict[str, Dict[str, Any]] = {}
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def get_cache_key(url: str, method: str = "GET", data: Optional[Dict] = None, headers: Optional[Dict] = None, params: Optional[Dict] = None) -> str:
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"""
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Generate a unique cache key for API requests.
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Args:
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url (str): The API endpoint URL
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method (str, optional): HTTP method. Defaults to "GET"
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data (Optional[Dict], optional): Request data for POST requests. Defaults to None
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headers (Optional[Dict], optional): Request headers. Defaults to None
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params (Optional[Dict], optional): URL parameters. Defaults to None
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Returns:
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str: MD5 hash of the request parameters for use as cache key
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"""
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key_data = f"{method}:{url}"
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if data:
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key_data += f":data:{json.dumps(data, sort_keys=True)}"
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if headers:
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# Sort headers to ensure consistent key generation
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sorted_headers = json.dumps(dict(sorted(headers.items())), sort_keys=True)
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key_data += f":headers:{sorted_headers}"
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if params:
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key_data += f":params:{json.dumps(params, sort_keys=True)}"
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return hashlib.md5(key_data.encode()).hexdigest()
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def is_cache_valid(cache_entry: Dict[str, Any]) -> bool:
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"""
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Check if a cache entry is still valid based on expiry time.
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Args:
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cache_entry (Dict[str, Any]): Cache entry containing timestamp and data
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Returns:
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bool: True if cache entry is still valid, False if expired
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"""
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return time.time() - cache_entry["timestamp"] < CACHE_EXPIRY_SECONDS
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def cached_request(method: str, url: str, **kwargs) -> requests.Response:
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"""
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Wrapper for requests that caches responses for 3 hours.
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Excludes 'original' endpoints where actual image data is downloaded.
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This function implements intelligent caching for API requests:
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- Caches JSON responses for non-image endpoints
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- Bypasses cache for '/original' endpoints to ensure fresh image data
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- Automatically handles cache expiry and cleanup
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Args:
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method (str): HTTP method (GET, POST, etc.)
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url (str): Target URL for the request
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**kwargs: Additional arguments passed to requests.request()
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Returns:
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requests.Response: Either cached response or fresh API response
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Raises:
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requests.RequestException: For network or HTTP errors
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"""
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# Don't cache original image downloads
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if "/original" in url:
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logger.info(f"Bypassing cache for original image request: {url}")
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return requests.request(method, url, **kwargs)
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# Generate cache key including headers and other parameters
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data = kwargs.get('json') if method.upper() == 'POST' else kwargs.get('data')
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headers = kwargs.get('headers')
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params = kwargs.get('params')
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cache_key = get_cache_key(url, method, data, headers, params)
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# Check if we have a valid cached response
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if cache_key in _api_cache and is_cache_valid(_api_cache[cache_key]):
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logger.info(f"Using cached response for: {url}")
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cached_entry = _api_cache[cache_key]
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# Create a mock response object with cached data
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response = requests.Response()
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response._content = json.dumps(cached_entry["data"]).encode()
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response.status_code = cached_entry["status_code"]
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response.headers.update(cached_entry["headers"])
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return response
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# Make the actual request
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logger.info(f"Making fresh API request to: {url}")
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response = requests.request(method, url, **kwargs)
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# Cache successful responses (excluding binary content)
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if response.status_code == 200:
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try:
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# Only cache JSON responses
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response_data = response.json()
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_api_cache[cache_key] = {
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"data": response_data,
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"status_code": response.status_code,
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"headers": dict(response.headers),
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"timestamp": time.time()
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}
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logger.info(f"Cached response for: {url}")
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except (json.JSONDecodeError, ValueError):
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# Don't cache non-JSON responses
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logger.info(f"Not caching non-JSON response for: {url}")
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return response
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def clear_expired_cache():
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"""
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Remove expired entries from the cache to prevent memory bloat.
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This function iterates through all cache entries and removes those
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that have exceeded the CACHE_EXPIRY_SECONDS threshold.
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"""
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current_time = time.time()
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expired_keys = [
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key for key, entry in _api_cache.items()
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if current_time - entry["timestamp"] >= CACHE_EXPIRY_SECONDS
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]
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for key in expired_keys:
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del _api_cache[key]
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if expired_keys:
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logger.info(f"Cleared {len(expired_keys)} expired cache entries")
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# Pydantic Models
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class ImageFormat(str, Enum):
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"""Supported image output formats."""
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PNG = "png"
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JPEG = "jpeg"
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HEIC = "heic"
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class SearchQuery(BaseModel):
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"""Model for Immich search API requests."""
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query: str = Field(..., min_length=1, description="Search query string")
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type: str = Field("IMAGE", description="Asset type to search for")
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@field_validator('query')
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def validate_query(cls, v):
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# Add query sanitization
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forbidden_chars = ['<', '>', '"', "'"]
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if any(char in v for char in forbidden_chars):
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raise ValueError("Query contains forbidden characters")
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return v.strip()
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@field_validator("type")
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def validate_asset_type(cls, v):
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allowed_types = ["IMAGE", "VIDEO"]
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if v not in allowed_types:
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raise ValueError(f"Invalid asset type: {v}. Allowed values are {allowed_types}.")
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return v
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class APIConfig(BaseModel):
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"""Configuration for Immich API connection."""
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base_url: HttpUrl = Field(os.getenv("IMMICH_URL"), description="Base URL for Immich API")
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api_key: str = Field(os.getenv("IMMICH_API_KEY"), min_length=1, description="API key for authentication")
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@field_validator("base_url")
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def validate_url(cls, v):
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if not v.endswith("/api"):
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raise ValueError("Base URL must end with '/api'.")
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return v
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class ImageDimensions(BaseModel):
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"""Target dimensions for image processing."""
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width: int = Field(..., gt=0, description="Target width in pixels")
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height: int = Field(..., gt=0, description="Target height in pixels")
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@field_validator("width", "height")
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def validate_positive(cls, v):
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if v <= 0:
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raise ValueError("Width and height must be positive integers.")
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return v
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class ProcessingConfig(BaseModel):
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"""Configuration for image processing operations."""
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target_dimensions: ImageDimensions = ImageDimensions(width=3840, height=2160)
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target_aspect_ratio: Tuple[int, int] = Field(default=(16, 9), description="Target aspect ratio as (width, height)")
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output_format: ImageFormat = ImageFormat.PNG
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output_filename: str = "current_image.png"
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@field_validator("output_filename")
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def validate_filename(cls, v):
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if not v.endswith((".png", ".jpeg", ".jpg", ".heic")):
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raise ValueError("Output filename must end with a supported file extension (.png, .jpeg, .heic).")
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return v.strip()
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@property
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def get_output_path(self) -> str:
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"""Generate full path for output file."""
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return IMAGES_DIR / self.output_filename
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# Initialize Flask app and configs
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app = Flask(__name__)
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api_config = APIConfig()
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processing_config = ProcessingConfig()
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||||
def error_handler(func):
|
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"""
|
||||
Decorator for consistent error handling across Flask routes.
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||||
|
||||
This decorator catches and properly formats various types of exceptions:
|
||||
- APIError: Network/API related issues (503 status)
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- ImageProcessingError: Image manipulation issues (500 status)
|
||||
- General exceptions: Unexpected errors (500 status)
|
||||
|
||||
Args:
|
||||
func: The Flask route function to wrap
|
||||
|
||||
Returns:
|
||||
Wrapped function with error handling
|
||||
"""
|
||||
|
||||
@wraps(func)
|
||||
def wrapper(*args, **kwargs):
|
||||
try:
|
||||
return func(*args, **kwargs)
|
||||
except APIError as e:
|
||||
logger.error(f"API Error: {str(e)}")
|
||||
return jsonify({"status": "error", "message": str(e)}), 503
|
||||
except ImageProcessingError as e:
|
||||
logger.error(f"Image Processing Error: {str(e)}")
|
||||
return jsonify({"status": "error", "message": str(e)}), 500
|
||||
except Exception as e:
|
||||
logger.error(f"Unexpected Error: {str(e)}")
|
||||
return jsonify({"status": "error", "message": "Internal server error"}), 500
|
||||
|
||||
return wrapper
|
||||
|
||||
|
||||
def resize_to_4k(img: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Resize an image to 4K resolution while maintaining aspect ratio.
|
||||
|
||||
This function calculates the optimal scale factor to fit the image
|
||||
within 4K dimensions (3840x2160) without distortion.
|
||||
|
||||
Args:
|
||||
img (np.ndarray): Input image as OpenCV array
|
||||
|
||||
Returns:
|
||||
np.ndarray: Resized image array
|
||||
|
||||
Raises:
|
||||
ImageProcessingError: If resize operation fails
|
||||
"""
|
||||
try:
|
||||
target = processing_config.target_dimensions
|
||||
h, w = img.shape[:2]
|
||||
scale = min(target.width / w, target.height / h)
|
||||
|
||||
new_w = int(w * scale)
|
||||
new_h = int(h * scale)
|
||||
|
||||
return cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LANCZOS4)
|
||||
except Exception as e:
|
||||
raise ImageProcessingError(f"Failed to resize image: {str(e)}")
|
||||
|
||||
|
||||
def get_salient_crop(img: np.ndarray, target_aspect_ratio: Optional[Tuple[int, int]] = None) -> np.ndarray:
|
||||
"""
|
||||
Crop image around the most salient region while maintaining specified aspect ratio.
|
||||
|
||||
This function uses gradient-based saliency detection to identify the most
|
||||
interesting region of the image, then crops around that area while maintaining
|
||||
the target aspect ratio.
|
||||
|
||||
Algorithm:
|
||||
1. Convert to grayscale and compute gradients using Sobel operators
|
||||
2. Calculate gradient magnitude as saliency measure
|
||||
3. Apply Gaussian blur to smooth the saliency map
|
||||
4. Find the point of maximum saliency
|
||||
5. Crop around this point with the target aspect ratio
|
||||
|
||||
Args:
|
||||
img (np.ndarray): Input image as OpenCV array
|
||||
target_aspect_ratio (Optional[Tuple[int, int]]): Desired aspect ratio as (width, height).
|
||||
Defaults to config value if None.
|
||||
|
||||
Returns:
|
||||
np.ndarray: Cropped image array
|
||||
|
||||
Raises:
|
||||
ImageProcessingError: If crop operation fails
|
||||
"""
|
||||
try:
|
||||
target_aspect_ratio = target_aspect_ratio or processing_config.target_aspect_ratio
|
||||
height, width = img.shape[:2]
|
||||
|
||||
# Convert to grayscale and calculate saliency
|
||||
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||||
grad_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)
|
||||
grad_y = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)
|
||||
gradient_magnitude = np.sqrt(grad_x ** 2 + grad_y ** 2)
|
||||
gradient_magnitude = cv2.normalize(gradient_magnitude, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
|
||||
saliency_map = cv2.GaussianBlur(gradient_magnitude, (21, 21), 0)
|
||||
|
||||
_, _, _, max_loc = cv2.minMaxLoc(saliency_map)
|
||||
|
||||
# Calculate crop dimensions
|
||||
crop_ratio = target_aspect_ratio[0] / target_aspect_ratio[1]
|
||||
crop_width = min(width, int(height * crop_ratio))
|
||||
crop_height = min(height, int(width / crop_ratio))
|
||||
|
||||
x_center, y_center = max_loc
|
||||
x1 = max(0, x_center - crop_width // 2)
|
||||
y1 = max(0, y_center - crop_height // 2)
|
||||
x2 = min(width, x1 + crop_width)
|
||||
y2 = min(height, y1 + crop_height)
|
||||
|
||||
# Adjust crop coordinates if necessary
|
||||
if x2 - x1 < crop_width:
|
||||
x1 = 0 if x1 == 0 else width - crop_width
|
||||
x2 = crop_width if x1 == 0 else width
|
||||
|
||||
if y2 - y1 < crop_height:
|
||||
y1 = 0 if y1 == 0 else height - crop_height
|
||||
y2 = crop_height if y1 == 0 else height
|
||||
|
||||
return img[y1:y2, x1:x2]
|
||||
except Exception as e:
|
||||
raise ImageProcessingError(f"Failed to crop image: {str(e)}")
|
||||
|
||||
|
||||
def get_new_random_image(query: Optional[str] = None, range_limit: Optional[int] = None) -> bool:
|
||||
"""
|
||||
Fetch and process a random image from Immich API.
|
||||
|
||||
This function performs the complete workflow:
|
||||
1. Search for images matching the query using cached requests
|
||||
2. Select a random image from search results
|
||||
3. Download the original image (bypasses cache)
|
||||
4. Fetch image metadata (cached)
|
||||
5. Process the image (crop and resize)
|
||||
6. Save the final image to the images directory
|
||||
|
||||
The function uses smart caching to reduce API calls while ensuring
|
||||
fresh image data for downloads.
|
||||
|
||||
Args:
|
||||
query (Optional[str]): Search query string. Defaults to "Mountains. Landscape. Nature"
|
||||
range_limit (Optional[int]): Maximum number of results to consider. Defaults to 50
|
||||
|
||||
Returns:
|
||||
bool: True if image processing completed successfully
|
||||
|
||||
Raises:
|
||||
APIError: If API requests fail or no images found
|
||||
ImageProcessingError: If image processing operations fail
|
||||
"""
|
||||
try:
|
||||
# Clear expired cache entries
|
||||
clear_expired_cache()
|
||||
|
||||
# Use provided query or default
|
||||
search_query_text = query or os.getenv("DEFAULT_QUERY", "Mountains")
|
||||
result_limit = range_limit or 50
|
||||
|
||||
# Prepare API request
|
||||
search_query = SearchQuery(
|
||||
query=search_query_text
|
||||
)
|
||||
|
||||
headers = {
|
||||
'x-api-key': api_config.api_key,
|
||||
'Content-Type': 'application/json'
|
||||
}
|
||||
|
||||
# Search for images - this will be cached
|
||||
response = cached_request(
|
||||
"POST",
|
||||
f"{api_config.base_url}/search/smart",
|
||||
headers=headers,
|
||||
json=search_query.model_dump()
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
# Process response
|
||||
response_data = response.json()
|
||||
asset_count = min(result_limit, response_data["assets"]["count"])
|
||||
if asset_count == 0:
|
||||
raise APIError("No images found")
|
||||
|
||||
random_asset_id = response_data["assets"]["items"][random.randint(0, asset_count - 1)]["id"]
|
||||
|
||||
# Fetch image - this will NOT be cached (bypassed for /original endpoints)
|
||||
random_asset = cached_request(
|
||||
"GET",
|
||||
f"{api_config.base_url}/assets/{random_asset_id}/original",
|
||||
headers=headers
|
||||
)
|
||||
random_asset.raise_for_status()
|
||||
|
||||
# Fetch metadata - this will be cached
|
||||
asset_metadata = cached_request(
|
||||
"GET",
|
||||
f"{api_config.base_url}/assets/{random_asset_id}",
|
||||
headers=headers
|
||||
)
|
||||
asset_metadata.raise_for_status()
|
||||
|
||||
# Process image
|
||||
actual_extension = asset_metadata.json()["originalMimeType"].split("/")[1]
|
||||
temp_image_path = IMAGES_DIR / f"temp_random_asset.{actual_extension}"
|
||||
output_path = processing_config.get_output_path
|
||||
|
||||
try:
|
||||
# Save temporary image file
|
||||
with open(temp_image_path, "wb") as f:
|
||||
f.write(random_asset.content)
|
||||
|
||||
# Handle HEIC format
|
||||
if actual_extension.lower() == 'heic':
|
||||
from pillow_heif import register_heif_opener
|
||||
register_heif_opener()
|
||||
|
||||
# Process the image
|
||||
with Image.open(temp_image_path) as img:
|
||||
# Convert to appropriate color mode
|
||||
if img.mode in ('RGBA', 'LA') or (img.mode == 'P' and 'transparency' in img.info):
|
||||
img = img.convert('RGBA')
|
||||
else:
|
||||
img = img.convert('RGB')
|
||||
|
||||
# Convert to OpenCV format and process
|
||||
opencv_img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
|
||||
cropped_img = get_salient_crop(opencv_img)
|
||||
resized_img = resize_to_4k(cropped_img)
|
||||
|
||||
# Convert back to PIL and save final image
|
||||
final_img = Image.fromarray(cv2.cvtColor(resized_img, cv2.COLOR_BGR2RGB))
|
||||
final_img.save(
|
||||
output_path,
|
||||
processing_config.output_format.value,
|
||||
optimize=True
|
||||
)
|
||||
|
||||
finally:
|
||||
# Clean up temporary file
|
||||
if temp_image_path.exists():
|
||||
temp_image_path.unlink()
|
||||
|
||||
logger.info(f"Successfully processed and saved image to: {output_path}")
|
||||
return True
|
||||
|
||||
except requests.exceptions.RequestException as e:
|
||||
raise APIError(f"Failed to fetch image: {str(e)}")
|
||||
except Exception as e:
|
||||
raise ImageProcessingError(f"Failed to process image: {str(e)}")
|
||||
|
||||
|
||||
@app.route('/new-image')
|
||||
@error_handler
|
||||
def new_image():
|
||||
"""
|
||||
Flask endpoint to generate a new processed image.
|
||||
|
||||
This endpoint triggers the complete image processing workflow:
|
||||
fetching, processing, and saving a new random image.
|
||||
|
||||
Query Parameters:
|
||||
query (str, optional): Search query for image selection
|
||||
range (int, optional): Maximum number of search results to consider (default: 50)
|
||||
|
||||
Returns:
|
||||
JSON response indicating success or error status
|
||||
"""
|
||||
# Get optional query parameters
|
||||
query = request.args.get('query')
|
||||
range_limit = request.args.get('range', type=int)
|
||||
|
||||
get_new_random_image(query=query, range_limit=range_limit)
|
||||
return jsonify({"status": "success", "message": "New image generated successfully"})
|
||||
|
||||
|
||||
@app.route('/image')
|
||||
@error_handler
|
||||
def serve_image():
|
||||
"""
|
||||
Flask endpoint to serve the current processed image.
|
||||
|
||||
Returns the most recently processed image file from the images directory.
|
||||
|
||||
Returns:
|
||||
Flask file response with the current image
|
||||
|
||||
Raises:
|
||||
ImageNotFoundError: If no processed image is available
|
||||
"""
|
||||
output_path = processing_config.get_output_path
|
||||
|
||||
if not output_path.exists():
|
||||
raise ImageNotFoundError("No image available")
|
||||
|
||||
return send_file(
|
||||
output_path,
|
||||
mimetype=f'image/{processing_config.output_format.value}'
|
||||
)
|
||||
|
||||
|
||||
@app.route('/cache-status')
|
||||
@error_handler
|
||||
def cache_status():
|
||||
"""
|
||||
Flask endpoint to check cache status and statistics.
|
||||
|
||||
Provides information about the current state of the API cache,
|
||||
including number of entries and expiry configuration.
|
||||
|
||||
Returns:
|
||||
JSON response with cache statistics
|
||||
"""
|
||||
clear_expired_cache()
|
||||
return jsonify({
|
||||
"status": "success",
|
||||
"cache_entries": len(_api_cache),
|
||||
"cache_expiry_hours": CACHE_EXPIRY_SECONDS / 3600,
|
||||
"images_directory": str(IMAGES_DIR.absolute())
|
||||
})
|
||||
|
||||
|
||||
@app.route('/images-info')
|
||||
@error_handler
|
||||
def images_info():
|
||||
"""
|
||||
Flask endpoint to get information about the images directory.
|
||||
|
||||
Returns statistics about saved images including directory path,
|
||||
current image status, and directory size.
|
||||
|
||||
Returns:
|
||||
JSON response with images directory information
|
||||
"""
|
||||
output_path = processing_config.get_output_path
|
||||
|
||||
return jsonify({
|
||||
"status": "success",
|
||||
"images_directory": str(IMAGES_DIR.absolute()),
|
||||
"current_image_exists": output_path.exists(),
|
||||
"current_image_path": str(output_path) if output_path.exists() else None,
|
||||
"current_image_size_bytes": output_path.stat().st_size if output_path.exists() else 0
|
||||
})
|
||||
|
||||
|
||||
@app.route('/health', methods=['GET'])
|
||||
@error_handler
|
||||
def health_check():
|
||||
"""
|
||||
Health check endpoint for monitoring system status.
|
||||
|
||||
Returns:
|
||||
JSON response with service status, container health, and dependency checks.
|
||||
"""
|
||||
|
||||
try:
|
||||
# Check disk space in container
|
||||
total, used, free = shutil.disk_usage("/")
|
||||
disk_ok = free / total > 0.1 # Ensure at least 10% free disk space
|
||||
|
||||
# Memory usage
|
||||
memory = psutil.virtual_memory()
|
||||
memory_ok = memory.available / memory.total > 0.1 # At least 10% memory free
|
||||
|
||||
# Check Immich API connectivity
|
||||
try:
|
||||
response = requests.get(str(api_config.base_url))
|
||||
api_accessible = response.status_code == 404
|
||||
except ConnectionError:
|
||||
api_accessible = False
|
||||
|
||||
# Determine overall status
|
||||
healthy = disk_ok and memory_ok and api_accessible
|
||||
|
||||
# Construct the response
|
||||
return jsonify({
|
||||
"status": "healthy" if healthy else "unhealthy",
|
||||
"disk_usage": {
|
||||
"total_gb": total / (1024 ** 3),
|
||||
"used_gb": used / (1024 ** 3),
|
||||
"free_gb": free / (1024 ** 3),
|
||||
"status_ok": disk_ok
|
||||
},
|
||||
"memory_usage": {
|
||||
"total_gb": memory.total / (1024 ** 3),
|
||||
"available_gb": memory.available / (1024 ** 3),
|
||||
"status_ok": memory_ok
|
||||
},
|
||||
"api_connectivity": {
|
||||
"url": api_config.base_url,
|
||||
"status_ok": api_accessible
|
||||
}
|
||||
}), 200
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Health check error: {str(e)}")
|
||||
return jsonify({
|
||||
"status": "error",
|
||||
"message": str(e)
|
||||
}), 500
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# Ensure images directory exists and generate initial image
|
||||
IMAGES_DIR.mkdir(exist_ok=True)
|
||||
get_new_random_image()
|
||||
app.run()
|
||||
17
pyproject.toml
Normal file
17
pyproject.toml
Normal file
@ -0,0 +1,17 @@
|
||||
[project]
|
||||
name = "immichsmartrandom"
|
||||
version = "0.1.0"
|
||||
description = "Add your description here"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.13"
|
||||
dependencies = [
|
||||
"flask>=3.1.1",
|
||||
"opencv-python>=4.11.0.86",
|
||||
"pillow>=11.2.1",
|
||||
"pillow-heif>=0.22.0",
|
||||
"psutil>=7.0.0",
|
||||
"pydantic>=2.11.5",
|
||||
"pydantic-settings>=2.9.1",
|
||||
"python-dotenv>=1.1.0",
|
||||
"requests>=2.32.4",
|
||||
]
|
||||
415
uv.lock
generated
Normal file
415
uv.lock
generated
Normal file
@ -0,0 +1,415 @@
|
||||
version = 1
|
||||
revision = 2
|
||||
requires-python = ">=3.13"
|
||||
resolution-markers = [
|
||||
"sys_platform == 'darwin'",
|
||||
"platform_machine == 'aarch64' and sys_platform == 'linux'",
|
||||
"(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.7.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "blinker"
|
||||
version = "1.9.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/21/28/9b3f50ce0e048515135495f198351908d99540d69bfdc8c1d15b73dc55ce/blinker-1.9.0.tar.gz", hash = "sha256:b4ce2265a7abece45e7cc896e98dbebe6cead56bcf805a3d23136d145f5445bf", size = 22460, upload-time = "2024-11-08T17:25:47.436Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/10/cb/f2ad4230dc2eb1a74edf38f1a38b9b52277f75bef262d8908e60d957e13c/blinker-1.9.0-py3-none-any.whl", hash = "sha256:ba0efaa9080b619ff2f3459d1d500c57bddea4a6b424b60a91141db6fd2f08bc", size = 8458, upload-time = "2024-11-08T17:25:46.184Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2025.4.26"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/e8/9e/c05b3920a3b7d20d3d3310465f50348e5b3694f4f88c6daf736eef3024c4/certifi-2025.4.26.tar.gz", hash = "sha256:0a816057ea3cdefcef70270d2c515e4506bbc954f417fa5ade2021213bb8f0c6", size = 160705, upload-time = "2025-04-26T02:12:29.51Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/4a/7e/3db2bd1b1f9e95f7cddca6d6e75e2f2bd9f51b1246e546d88addca0106bd/certifi-2025.4.26-py3-none-any.whl", hash = "sha256:30350364dfe371162649852c63336a15c70c6510c2ad5015b21c2345311805f3", size = 159618, upload-time = "2025-04-26T02:12:27.662Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "charset-normalizer"
|
||||
version = "3.4.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/e4/33/89c2ced2b67d1c2a61c19c6751aa8902d46ce3dacb23600a283619f5a12d/charset_normalizer-3.4.2.tar.gz", hash = "sha256:5baececa9ecba31eff645232d59845c07aa030f0c81ee70184a90d35099a0e63", size = 126367, upload-time = "2025-05-02T08:34:42.01Z" }
|
||||
wheels = [
|
||||
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|
||||
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||||
{ url = "https://files.pythonhosted.org/packages/22/2a/ea8a2095b0bafa6c5b5a55ffdc2f924455233ee7b91c69b7edfcc9e02284/charset_normalizer-3.4.2-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3fddb7e2c84ac87ac3a947cb4e66d143ca5863ef48e4a5ecb83bd48619e4634e", size = 153653, upload-time = "2025-05-02T08:33:00.342Z" },
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||||
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||||
{ url = "https://files.pythonhosted.org/packages/e2/28/ffc026b26f441fc67bd21ab7f03b313ab3fe46714a14b516f931abe1a2d8/charset_normalizer-3.4.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c9379d65defcab82d07b2a9dfbfc2e95bc8fe0ebb1b176a3190230a3ef0e07c", size = 148243, upload-time = "2025-05-02T08:33:04.063Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/c0/0f/9abe9bd191629c33e69e47c6ef45ef99773320e9ad8e9cb08b8ab4a8d4cb/charset_normalizer-3.4.2-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e635b87f01ebc977342e2697d05b56632f5f879a4f15955dfe8cef2448b51691", size = 150442, upload-time = "2025-05-02T08:33:06.418Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/67/7c/a123bbcedca91d5916c056407f89a7f5e8fdfce12ba825d7d6b9954a1a3c/charset_normalizer-3.4.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:1c95a1e2902a8b722868587c0e1184ad5c55631de5afc0eb96bc4b0d738092c0", size = 145147, upload-time = "2025-05-02T08:33:08.183Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ec/fe/1ac556fa4899d967b83e9893788e86b6af4d83e4726511eaaad035e36595/charset_normalizer-3.4.2-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:ef8de666d6179b009dce7bcb2ad4c4a779f113f12caf8dc77f0162c29d20490b", size = 153057, upload-time = "2025-05-02T08:33:09.986Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/2b/ff/acfc0b0a70b19e3e54febdd5301a98b72fa07635e56f24f60502e954c461/charset_normalizer-3.4.2-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:32fc0341d72e0f73f80acb0a2c94216bd704f4f0bce10aedea38f30502b271ff", size = 156454, upload-time = "2025-05-02T08:33:11.814Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/92/08/95b458ce9c740d0645feb0e96cea1f5ec946ea9c580a94adfe0b617f3573/charset_normalizer-3.4.2-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:289200a18fa698949d2b39c671c2cc7a24d44096784e76614899a7ccf2574b7b", size = 154174, upload-time = "2025-05-02T08:33:13.707Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/78/be/8392efc43487ac051eee6c36d5fbd63032d78f7728cb37aebcc98191f1ff/charset_normalizer-3.4.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:4a476b06fbcf359ad25d34a057b7219281286ae2477cc5ff5e3f70a246971148", size = 149166, upload-time = "2025-05-02T08:33:15.458Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/44/96/392abd49b094d30b91d9fbda6a69519e95802250b777841cf3bda8fe136c/charset_normalizer-3.4.2-cp313-cp313-win32.whl", hash = "sha256:aaeeb6a479c7667fbe1099af9617c83aaca22182d6cf8c53966491a0f1b7ffb7", size = 98064, upload-time = "2025-05-02T08:33:17.06Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/e9/b0/0200da600134e001d91851ddc797809e2fe0ea72de90e09bec5a2fbdaccb/charset_normalizer-3.4.2-cp313-cp313-win_amd64.whl", hash = "sha256:aa6af9e7d59f9c12b33ae4e9450619cf2488e2bbe9b44030905877f0b2324980", size = 105641, upload-time = "2025-05-02T08:33:18.753Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/20/94/c5790835a017658cbfabd07f3bfb549140c3ac458cfc196323996b10095a/charset_normalizer-3.4.2-py3-none-any.whl", hash = "sha256:7f56930ab0abd1c45cd15be65cc741c28b1c9a34876ce8c17a2fa107810c0af0", size = 52626, upload-time = "2025-05-02T08:34:40.053Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "click"
|
||||
version = "8.2.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "colorama", marker = "sys_platform == 'win32'" },
|
||||
]
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Reference in New Issue
Block a user