hug | Hug aims to make developing APIs | REST library
kandi X-RAY | hug Summary
kandi X-RAY | hug Summary
To run the example:. Then you can access the example from localhost:8000/v1/echo?text=Hi / localhost:8000/v2/echo?text=Hi Or access the documentation for your API from localhost:8000. Note: versioning in hug automatically supports both the version header as well as direct URL based specification. hug exposes a hug_wsgi magic method on every API module automatically. Running your hug based API on any standard wsgi server should be as simple as pointing it to module_name: hug_wsgi. To run the hello world hug example API. Building Blocks of a hug API. hug enables you to organize large projects in any manner you see fit. You can import any module that contains hug decorated functions (request handling, directives, type handlers, etc) and extend your base API with that module.
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Top functions reviewed by kandi - BETA
- Hug API
- Initialize the API server
- Ensures that all startup handlers are started
- Serve the API
- Make a HTTP request
- Rewrite the map
- Parse a Content - Type header
- Calls given function with given parameters
- Helper function for JSON converter
- Decorator for middlewares
- Create an image handler
- Set default output format
- Add user to database
- Authenticate a user with the given password
- Create a handler for a video type
- Generate a function that accepts a function
- Set the session cookie
- Return a function that matches the content type
- Add headers to response
- Construct a HTTP response type
- Initialize directory
- Generate documentation for this interface
- Return a function that accepts a request
- Extend the given http_api
- Returns a response type based on the given handlers
- Create a WSGI API server
hug Key Features
hug Examples and Code Snippets
Lines of Code : 94
License : No License
Copy# filename: versioning_example.py
"""A simple example of a hug API call with versioning"""
import hug
@hug.get('/echo', versions=1)
def echo(text):
return text
@hug.get('/echo', versions=range(2, 5))
def echo(text):
return "Echo: {text}".f @hug.type(chain=True, extend=hug.types.number, accept_context=True)
def check_if_near_the_right_number(value, context):
the_only_right_number = context['the_only_right_number']
if value not in [
the_only_right_number - 1,
the_ # filename: versioning_example.py
"""A simple example of a hug API call with versioning"""
import hug
@hug.get('/echo', versions=1)
def echo(text):
return text
@hug.get('/echo', versions=range(2, 5))
def echo(text):
return "Echo: {text}".f from tinydb import TinyDB, Query
import hug
import hashlib
import logging
import os
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
db = TinyDB("db.json")
"""
Helper Methods
"""
def hash_password(password, salt):
"""
"""A basic example of authentication requests within a hug API"""
import hug
import jwt
# Several authenticators are included in hug/authentication.py. These functions
# accept a verify_user function, which can be either an included function (such
# import hug
from demo.authentication import basic_authentication
from demo.directives import SqlalchemySession
from demo.models import TestUser, TestModel
from demo.validation import CreateUserSchema, DumpSchema, unique_username
@hug.post("/create_ Community Discussions
Trending Discussions on hug
QUESTION
I want to detect more objects than coco dataset which detects only 80 objects , I want to detect as many as possible actions also like hugging ,swimming.....etc.
I don't care about the size and I do not want to train myself ... So is there a dataset(weights) big enough already available that I can download and use or I do have to train and label for yolo?
...ANSWER
Answered 2021-Jun-13 at 13:54What you are trying to classify is represented as Action Recognition. Here [1] is a good repo that lists a lot of out-of-the-box models for this task.
An explanation: Models (like YOLO) contain two main blocks: feature extraction (CNN stuff) and classification (linear layers). When training from scratch, both feature extraction and classification will be trained from scratch. It is easy to train classification to what you want, but it is hard to train the feature extraction part (as it takes a lot of time). Hence, we typically use pre-trained models on generalized datasets (like YOLO is trained on COCO), so our feature extraction part starts from a somewhat good generalized place. Later, we replace the classification part will our own to be trained from scratch for our task.
TL;DR, you can use a pre-trained YOLO model on COCO for your task by replacing the last linear layers to classify what you want. Here are some resources for different frameworks [2, 3].
Here [4] is a simple walkthrough of how to do this.
- [1] https://github.com/jinwchoi/awesome-action-recognition/blob/master/README.md#action-recognition-and-video-understanding
- [2] TensorFlow: https://www.tensorflow.org/tutorials/images/transfer_learning
- [3] PyTorch: https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
- [4] https://blog.roboflow.com/training-yolov4-on-a-custom-dataset/
QUESTION
I have followed this tutorial for masked language modelling from Hugging Face using BERT, but I am unsure how to actually deploy the model.
Tutorial: https://github.com/huggingface/notebooks/blob/master/examples/language_modeling.ipynb
I have trained the model using my own dataset, which has worked fine, but I don't know how to actually use the model, as the notebook does not include an example on how to do this, sadly.
Example of what I want to do with my trained model
On the Hugging Face website, this is the code used in the example; hence, I want to do this exact thing but with my model:
...ANSWER
Answered 2021-Jun-06 at 16:53This depends a lot of your task. Your task seems to be masked language modelling, that, is to predict one or more masked words:
today I ate ___ .
(pizza) or (pasta) could be equally correct, so you cannot use a metric such as accuray. But (water) should be less "correct" than the other two. So what you normally do is to check how "surprised" the language model is, on an evaluation data set. This metric is called perplexity. Therefore, before and after you finetune a model on you specific dataset, you would calculate the perplexity and you would expect it to be lower after finetuning. The model should be more used to your specific vocabulary etc. And that is how you test your model.
As you can see, they calculate the perplexity in the tutorial you mentioned:
QUESTION
I am having issues with getting the items to the right. I got my brand on the left side and whatever i try. I can't get the items to the right side of the navbar.They stay hugging to the left side.
This is my code:
...ANSWER
Answered 2021-Jun-05 at 16:32Since you'd like to align the entire navigation to the right side of the navbar, you can simply add an empty div with a margin-right of auto right before the nav link section, which will push the navigation to the right side.
In your original navbar code, this implementation would look like:
QUESTION
@client.command()
async def hug(ctx, *, user: discord.Member = None):
hug_embed = discord.Embed(
title=f'❤️ {ctx.author.display_name} hugged {user.display_name} ❤️',
description=' ',
color=0xcce5ff)
hug_embed.set_image(
url=
'https://cdn.discordapp.com/attachments/787005334560309269/850480213111341056/hug_ghot.gif'
)
hug_embed.set_footer(text='hi')
if user:
await ctx.message.channel.send(embed=hug_embed)
else:
await ctx.send('hug someone ;n;')
ANSWER
Answered 2021-Jun-04 at 21:44import random
image_list = ["url", "url2", "url3", etc.]
QUESTION
I'm trying to use a component created with UIKit in SwiftUI by using UIViewRepresentable. The result I want to achieve is to have a textfield on top of my view and my UIKit view stacked at the bottom with an automatic height.
The problem is this component includes a multiline label and from what I see it makes it really hard to have an automatic height working properly, so my UIKit component takes all the available space in my VStack.
Here is my code included in a playground to test it. I tried to play with hugging priorities but nothing worked for me, and if I test with a swift UI view it works correctly.. Any ideas?
...ANSWER
Answered 2021-Jun-04 at 01:23There's a few things going on here.
InformationViewdoesn't have anintrinsicContentSize. SwiftUI relies on this property to determine aUIView's ideal size.- No
fixedSizemodifier. This modifier forces a view to maintain its ideal size rather than grow to fill its parent.
You can't add an intrinsicContentSize because the content size is dynamic; it's based on the number of lines in your label.
You can't add the fixedSize modifier, because without an intrinsicContentSize, this will set the size to (0,0).
🐓🥚
One solution is to wrap InformationView in a UIView that measures the InformationView size, and updates its own intrinsicContentSize according to that measurement.
Your InformationView should fill the width of the screen; it doesn't have an intrinsic width. On the other hand, the intrinsic height should be equal to the height of the compressed system layout size. This is "the optimal size of the view based on its constraints".
Here is the wrapper:
QUESTION
@client.command()
async def hug(ctx, *, user: discord.Member = None):
hug_embed = discord.Embed(title='-w- hug -w-', description='', color=0xafe8fa)
hug_embed.add_field(name=f'{ctx.author.mention} hugged {user.mention}', value='', inline=False)
if user:
await ctx.message.channel.send(embed=hug_embed)
else:
await ctx.send('hug someone ;n;')
ANSWER
Answered 2021-Jun-03 at 22:37As the error suggests you need to put something in the value of the field. Right now you have '', try with 'something else'
QUESTION
So I am designing what should be a simple view.
This is how I want it to be, with the title taking up only the horizontal space that it needs. However, when I set the number of lines for the Details view to 0 so that it can be multiple lines, or when I do the same for the title label, I automatically get this:
I do like using a Stack View for these labels, because it seems the most natural choice to account for dynamic text. All that I would have to do when the text gets larger is change the axis to vertical. I have already set the hugging priority of the title label to 252 and I have already set a proportionate widths constraint so that the details will have a greater or equal width to the title.
So there is no ambiguity for the widths of the labels
the title label width should equal the width of its contents, until the contents reach the point that they would exceed the width of the details label, then word wrap.
The details label should should have a width equal to its contents as well, until it would exceed the bounds allowed by the higher priority hugging on the left and the trailing constraint on the right, then it should word wrap.
Here is my xib as an xml source code
...ANSWER
Answered 2021-Jun-02 at 01:30If you set the content hugging priority of the left label to 1000 (Required), it works.
There might be something with a very high priority in the stack view that is stopping the label from hugging its content.
QUESTION
I have the following df:
...ANSWER
Answered 2021-May-29 at 05:10You can get the last interaction like this:
df['last_interaction'] = df['Interaction'].str.split(',').str[-1]
Result:
QUESTION
I have the following df:
...ANSWER
Answered 2021-May-20 at 03:11You can split the string by ,, explode it and value_counts:
QUESTION
I have an Image view that is surrounded by 2 UIVIEWS which are the green top bar and blue side bar.
I have coded a tap gesture [FOR hiding the views], so If you tap once, the top and side bar hide and when tapped again, they re-appear.
However, I want to code, that if the bars are visible, then the image view should hug itself to the bars and not extend, so as to prevent clipping. Please see attached pic link
But when we tap and the bars go away, I want the image view to hug the super view, how can I do that?
I've coded the gesture like so:
...ANSWER
Answered 2021-May-09 at 18:04You can try playing with anchor constraints.
For example, put
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Install hug
You can use hug like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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