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 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 "### Oops! 😲 Looks like you've already submitted this task 🚀. Please hang tight! We'll send you an email with all the details once it's ready 💌. Thanks for your patience! 😊"

    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,
    }
    """
        # 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
    """  
    response = requests.post(url, json=data)
    
    if response.status_code == 200:
        processed_inputs[input_hash] = 200  
        return "### Your request has been submitted successfully! 🌟 We'll notify you by email 📧 once everything is processed. 😊"
    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.Soft(text_size="lg", spacing_size="lg")
with gr.Blocks(theme=theme) as demo:
    with gr.Row(variant="panel'"):
        gr.Markdown(value="## 🚀 Unleash the Power of Custom GGML Quantized Models! ⚡"),
        gr.LoginButton(min_width=380)

    gr.Markdown(value="🚨 **IMPORTANT:** You **MUST** grant access to the model repository before use.")
    gr.Markdown(value="🔔 You **MUST** be logged in to use this service.")
    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)