app.py

import gradio as gr
from gradio_huggingfacehub_search import HuggingfaceHubSearch
import requests

processed_inputs = {}

def process_inputs(model_id, q_method, email, oauth_token: gr.OAuthToken | None, profile: gr.OAuthProfile | None):
    if 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, email, 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": 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_S", "Q3_K_M", "Q3_K_L", "Q4_0", "Q4_K_S", "Q4_K_M", "Q5_0", "Q5_K_S", "Q5_K_M", "Q6_K", "Q8_0"],
            label="Quantization Method",
            info="GGML quantisation type",
            value="Q4_K_M",
            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.",),
    title="Create your own GGUF Quants, blazingly fast ⚡!",
    allow_flagging="never"
)

theme = gr.themes.Base()
with gr.Blocks(theme=theme) as demo:
    gr.Markdown("You must be logged in to use this service.")
    gr.LoginButton(min_width=250)
    iface.render()

demo.launch(share=True)