omerXfaruq's picture
remove sleep after the fix
67909d8
from typing import List
import numpy as np
import requests
import gradio as gr
import time
import os
from huggingface_hub import (
create_repo,
get_full_repo_name,
upload_file,
)
class SpaceBuilder:
error_message = None
url = None
@classmethod
def split_space_names(cls, names: str) -> List[str]:
"""
Splits and filters the given space_names.
:param names: space names
:return: Name List
"""
name_list = names.split("\n")
filtered_list = []
for name in name_list:
if not (name == "" or name.isspace()):
name = name.replace(" ", "")
filtered_list.append(name)
return filtered_list
@classmethod
def file_as_a_string(cls, name_list: List[str], title: str, description: str) -> str:
"""
Returns the file that is going to be created in the new space as string.
:param name_list: list of space names
:param title: title
:param description: description
:return: file as a string
"""
return (
f"import gradio as gr"
f"\nname_list = {name_list}"
f"\ninterfaces = [gr.Interface.load(name) for name in name_list]"
f"\ngr.mix.Parallel(*interfaces, title=\"{title}\", description=\"{description}\").launch()"
)
@classmethod
def control_input_and_output_types(
cls, interface_list: List["gr.Interface"]
) -> bool:
"""
Controls whether if input and output types of the given interfaces are the same.
:param interface_list: list of interfaces
:return: True if all input and output types are the same
"""
first_input_types = [
type(input) for input in interface_list[0].input_components
]
first_output_types = [
type(output) for output in interface_list[0].output_components
]
for interface in interface_list:
interface_input_types = [
type(input) for input in interface.input_components
]
if not np.all(
interface_input_types == first_input_types
): # Vectorize the comparison and don't use double for loop
cls.error_message = "Provided space input types are different"
return False
interface_output_types = [
type(output) for output in interface.output_components
]
if not np.all(interface_output_types == first_output_types):
cls.error_message = "Provided space output types are different"
return False
return True
@classmethod
def check_space_name_availability(cls, hf_token: str, space_name: str) -> bool:
"""
Check whether if the space_name is currently used.
:param hf_token: hugging_face token
:param space_name:
:return: True if the space_name is available
"""
try:
repo_name = get_full_repo_name(model_id=space_name, token=hf_token)
except Exception as ex:
print(ex)
cls.error_message = "You have given an incorrect HuggingFace token"
return False
try:
url = f"https://huggingface.co/spaces/{repo_name}"
response = requests.get(url)
if response.status_code == 200:
cls.error_message = f"The {repo_name} is already used."
return False
else:
print(f"The space name {repo_name} is available")
return True
except Exception as ex:
print(ex)
cls.error_message = "Can not send a request to https://huggingface.co"
return False
@classmethod
def load_and_check_spaces(cls, names: str) -> bool:
"""
Loads given space inputs as interfaces and checks whether if they are loadable.
:param names: Input space names
:return: True if check is successful
"""
name_list = cls.split_space_names(names)
try:
# We could gather these interfaces in parallel if gradio was supporting async gathering. It will probably be possible after the migration to the FastAPI is completed.
interfaces = [gr.Interface.load(name) for name in name_list]
except Exception as ex:
print(ex)
cls.error_message = (
f"One of the given space cannot be loaded to gradio, sorry for the inconvenience. "
f"\nPlease use different input space names!"
)
return False
if not cls.control_input_and_output_types(interfaces):
return False
else:
print("Loaded and checked input spaces, great it works!")
return True
@classmethod
def create_space(cls, input_space_names: str, target_space_name: str, hf_token: str, title: str, description: str) -> bool:
"""
Creates the target space with the given space names.
:param input_space_names: Input space name_list
:param target_space_name: Target space_name
:param hf_token: HuggingFace Write Token
:param title: Target Interface Title
:param description: Target Interface Description
:return: True if success
"""
name_list = cls.split_space_names(input_space_names)
try:
create_repo(name=target_space_name, token=hf_token, repo_type="space", space_sdk="gradio")
except Exception as ex:
print(ex)
cls.error_message = "Please provide a correct space name as Only regular characters and '-', '_', '.' accepted. '--' and '..' are forbidden. '-' and '.' cannot start or end the name."
return False
repo_name = get_full_repo_name(model_id=target_space_name, token=hf_token)
try:
file_string = cls.file_as_a_string(name_list, title, description)
temp_file = open("temp_file.txt", "w")
temp_file.write(file_string)
temp_file.close()
except Exception as ex:
print(ex)
cls.error_message = "An exception occurred during temporary file writing"
return False
try:
file_url = upload_file(
path_or_fileobj="temp_file.txt",
path_in_repo="app.py",
repo_id=repo_name,
repo_type="space",
token=hf_token,
)
cls.url = f"https://huggingface.co/spaces/{repo_name}"
return True
except Exception as ex:
print(ex)
cls.error_message = (
"An exception occurred during writing app.py to the target space"
)
return False
@staticmethod
def build_space(
model_or_space_names: str, hf_token: str, target_space_name: str, interface_title: str, interface_description: str
) -> str:
"""
Creates a space with given input spaces.
:param model_or_space_names: Multiple model or space names split with new lines
:param hf_token: HuggingFace token
:param target_space_name: Target Space Name
:param interface_title: Target Interface Title
:param interface_description: Target Interface Description
:return:
"""
if (
model_or_space_names== "" or model_or_space_names.isspace()
or target_space_name == "" or target_space_name.isspace()
or interface_title == "" or interface_title.isspace()
or interface_description == "" or interface_description.isspace()
):
return "Please fill all the inputs"
if hf_token == "" or hf_token.isspace():
hf_token = os.environ['HF_SELF_TOKEN']
if not SpaceBuilder.check_space_name_availability(hf_token=hf_token, space_name=target_space_name):
return SpaceBuilder.error_message
if not SpaceBuilder.load_and_check_spaces(names=model_or_space_names):
return SpaceBuilder.error_message
if not SpaceBuilder.create_space(input_space_names=model_or_space_names, target_space_name=target_space_name, hf_token=hf_token, title=interface_title, description=interface_description):
return SpaceBuilder.error_message
url = SpaceBuilder.url
return f"<a href={url}>{url}</a>"
if __name__ == "__main__":
print(f"Gradio Version: {gr.__version__}")
iface = gr.Interface(
fn=SpaceBuilder.build_space,
inputs=[
gr.inputs.Textbox(
lines=4,
placeholder=(
f"Drop model and space links at each line and I will create a new space comparing them. Usage examples:"
f"\nspaces/onnx/GPT-2"
f"\nmodels/gpt2-large"
f"\nmodels/gpt2"
),
),
gr.inputs.Textbox(lines=1, placeholder="HuggingFace Write Token"),
gr.inputs.Textbox(lines=1, placeholder="Name for the target space, ie. space-building-space"),
gr.inputs.Textbox(lines=1, placeholder="Title for the target space interface, ie. Title"),
gr.inputs.Textbox(lines=1, placeholder="Description for the target space interface, ie. Description"),
],
title="Model Comparator Space Builder",
description="Welcome onboard 🤗, I can create a comparative space which will compare the models and/or spaces you provide to me. You can get your HF Write Token from [here](https://huggingface.co/settings/tokens). If you leave HF Token input empty, the space will release under the author's account, [farukozderim](https://huggingface.co/farukozderim). Finally, you can publish spaces as a clone of other spaces if you provide just a single model or space. Have fun :)",
outputs=gr.outputs.HTML(label="URL"),
examples= [
["spaces/onnx/GPT-2 \nmodels/gpt2-large \nmodels/EleutherAI/gpt-j-6B", "", "comparison-space", "example-title", "example-description"],
["spaces/onnx/GPT-2", "", "duplicate-space", "example-title", "example-description"],
["models/EleutherAI/gpt-j-6B", "", "space-from-a-model", "example-title", "example-description"]
],
)
iface.launch()