from load import load_decorator as load

@load('numpy', 'pandas', 'plt=matplotlib.pyplot')
def analyze_data():
    import numpy as np
    data = np.random.rand(10, 3)
    plt.plot(data)
    plt.show()

# The required packages will be automatically installed when the function is first called
analyze_data()

# Traditional way
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegression

# With Load
from load import load, import_aliases

# Single import
np = load('numpy')

# Multiple imports with aliases
pd, plt, sns = import_aliases('pandas', 'plt=matplotlib.pyplot', 'seaborn')

# Direct attribute access
model = load('sklearn.linear_model.LinearRegression')()

# Now use them as usual
data = pd.DataFrame({'x': [1, 2, 3], 'y': [1, 2, 3]})
model.fit(data[['x']], data['y'])
plt.scatter(data['x'], data['y'])
plt.plot(data['x'], model.predict(data[['x']]), 'r')
plt.show()

from load import load, configure_private_registry

# Configure private registry
configure_private_registry(
    name="company",
    index_url="https://pypi.company.com/simple/"
)

# Import standard and private packages
fastapi = load('fastapi')
internal_auth = load('company-auth', registry="company")

app = fastapi.FastAPI()

@app.get("/")
async def root():
    return {"message": "Hello World"}

# Prywatny PyPI firmy
configure_private_registry(
    name="company",
    index_url="https://pypi.company.com/simple/"
)

# Prywatny GitLab z tokenem  
configure_private_registry(
    name="internal", 
    base_url="https://gitlab.company.com/",
    token="your-token"  # lub GITLAB_TOKEN env var
)

# Użyj
company_lib = load("internal-package", registry="company")
secret_tool = load("team/secret-sauce", registry="internal")

load("json")                    # → stdlib (nie instaluje)
load("requests")                # → PyPI  
load("user/repo")               # → GitHub
load("gitlab.com/user/proj")    # → GitLab
load("./file.py")               # → Lokalny plik
load("https://example.com/x.py") # → URL

# Najnowsze z GitHub zamiast PyPI
ml_lib = load("huggingface/transformers")
selenium = load("SeleniumHQ/selenium/py") 
playwright = load("microsoft/playwright-python")

# Skonfiguruj rejestry firmy
configure_private_registry("nexus", 
    index_url="https://nexus.company.com/pypi/simple/")

configure_private_registry("artifactory",
    index_url="https://company.jfrog.io/pypi/simple/")

# Używaj
auth_lib = load("company-auth", registry="nexus")
internal_api = load("team-api-client", registry="artifactory")

def setup_project():
    return {
        # PyPI - stabilne wersje
        'web': load("fastapi"),
        'db': load("sqlalchemy"),
        
        # GitHub - najnowsze funkcje  
        'ai': load("openai/openai-python"),
        'scraping': load("microsoft/playwright-python"),
        
        # Prywatne - firmowe narzędzia
        'auth': load("auth-service", registry="company"),
        'monitoring': load("team/observability", registry="internal"),
        
        # Lokalne - logika biznesowa
        'models': load("./models.py"),
        'utils': load("./utils.py")
    }

# Lista dostępnych rejestrów
list_registries()

# Dodaj własny rejestr
add_registry("custom", {
    'index_url': 'https://pypi.custom.com/simple/',
    'install_cmd': [sys.executable, "-m", "pip", "install", "--index-url"],
    'description': 'Custom PyPI mirror'
})

# Szybka konfiguracja
configure_private_registry("maven-central", 
    index_url="https://maven.central.com/pypi/")

def setup_ds():
    return {
        'pd': load("pandas", "pd"),                    # PyPI
        'np': load("numpy", "np"),                     # PyPI  
        'latest_sklearn': load("scikit-learn/scikit-learn"), # GitHub
        'utils': load("./ds_utils.py")                 # Local
    }

def setup_web():
    return {
        'api': load("fastapi"),                        # PyPI
        'auth': load("company-sso", registry="nexus"), # Private
        'monitoring': load("team/apm-client", registry="gitlab"), # GitLab
        'models': load("./models.py")                  # Local
    }

def setup_ai():
    return {
        'torch': load("pytorch/pytorch"),              # GitHub latest
        'transformers': load("huggingface/transformers"), # GitHub
        'custom_models': load("team/ml-models", registry="company"), # Private
        'preprocessing': load("./preprocess.py")        # Local
    }

# Kontroluj źródła
ALLOWED_REGISTRIES = ['pypi', 'company', 'github-trusted']

def secure_load(name, registry=None):
    if registry not in ALLOWED_REGISTRIES:
        raise SecurityError(f"Registry {registry} not allowed")
    return load(name, registry=registry)

from load import *

# Podstawowe
http = requests()                           # PyPI
data = pd()                                # PyPI + alias

# GitHub (najnowsze wersje)
ai = load("openai/openai-python")          # GitHub
ml = load("huggingface/transformers")      # GitHub

# Prywatne firmy
auth = load("company-auth", registry="nexus")
api = load("team/api", registry="gitlab")

# Lokalne
utils = load("./utils.py")

# Skonfiguruj raz
configure_private_registry("company", 
    index_url="https://pypi.company.com/simple/")

# Używaj wszędzie
internal_lib = load("secret-package", registry="company")