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import gradio as gr
from gradio_huggingfacehub_search import HuggingfaceHubSearch
import requests
"""
# OAuth Token Information:
# - This is an OAuth token, not a user's password.
# - We need the OAuth token to clone the related repository and access its contents.
# - As mentioned in the README.md, only read permission is requested, which includes:
# - Read access to your public profile
# - Read access to the content of all your public repos
# - The token expires after 60 minutes.
# - For more information about OAuth, please refer to the official documentation:
# https://huggingface.co/docs/hub/en/spaces-oauth
"""
processed_inputs = {}
def process_inputs(model_id, q_method, email, oauth_token: gr.OAuthToken | None, profile: gr.OAuthProfile | None):
if oauth_token is None or oauth_token.token is None or profile.username is None:
return "#### You must be logged in to use this service."
if not model_id or not q_method or not email:
return "#### All fields are required!"
input_hash = hash((model_id, q_method, oauth_token.token, profile.username))
if input_hash in processed_inputs and processed_inputs[input_hash] == 200:
return "#### This request has already been submitted successfully. Please do not submit the same request multiple times."
url = "https://sdk.nexa4ai.com/task"
data = {
"repository_url": f"https://huggingface.co/{model_id}",
"username": profile.username,
"access_token": oauth_token.token,
"email": email,
"quantization_option": q_method,
}
response = requests.post(url, json=data)
if response.status_code == 200:
processed_inputs[input_hash] = 200
return "#### Your request has been submitted successfully. We will notify you by email once processing is complete. There is no need to submit the same request multiple times."
else:
processed_inputs[input_hash] = response.status_code
return f"#### Failed to submit request: {response.text}"
iface = gr.Interface(
fn=process_inputs,
inputs=[
HuggingfaceHubSearch(
label="Hub Model ID",
placeholder="Search for model id on Huggingface",
search_type="model",
),
gr.Dropdown(
["q2_K", "q3_K", "q3_K_S", "q3_K_M", "q3_K_L", "q4_0", "q4_1", "q4_K", "q4_K_S", "q4_K_M", "q5_0", "q5_1", "q5_K", "q5_K_S", "q5_K_M", "q6_K", "q8_0", "f16"],
label="Quantization Option",
info="GGML quantisation options",
value="q4_0",
filterable=False
),
gr.Textbox(label="Email", placeholder="Enter your email here")
],
outputs = gr.Markdown(
label="output",
value="#### Please enter the model URL, select a quantization method, and provide your email address."
),
allow_flagging="never"
)
theme = gr.themes.Base(text_size="lg")
with gr.Blocks(theme=theme) as demo:
gr.Markdown(value="# 🚀 Unleash the Power of Custom GGML Quantized Models! ⚡")
gr.LoginButton(min_width=300)
gr.Markdown(value="### 🔔 You must be logged in to use this service.")
gr.Markdown(value="### 🚨 IMPORTANT: You must grant access to the model repository before use.")
iface.render()
gr.Markdown(value="We sincerely thank our community members, [Perry](https://huggingface.co/PerryCheng614), [Brian](https://huggingface.co/JoyboyBrian), [Qi](https://huggingface.co/qiqiWav), [David](https://huggingface.co/Davidqian123), for their extraordinary contributions to this GGUF converter project.")
demo.launch(share=True)
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