import sys
import numpy as np
import gradio as gr
import requests
from accelerate import Accelerator

accelerator = Accelerator(gradient_accumulation_steps=2)
dataloader, model, optimizer, scheduler = accelerator.prepare(dataloader, model, optimizer, scheduler)

with accelerator.accumulate():
    for input, output in dataloader:
        outputs = model(input)
        loss = loss_func(outputs)
        loss.backward()
        optimizer.step()
        scheduler.step()
        optimizer.zero_grad()


def greet(payload):
	response = requests.post("https://api-inference.huggingface.co/models/PoseyATX/Fenrir_Alpha", headers={"Authorization": "Bearer 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"}, json=payload)
	return response.json()
	super().load(name=PoseyATX/Fenrir_Alpha, src=huggingface, api_key=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, alias=FoxHunter, **kwargs)
output = greet({
	"inputs": "https://capitol.texas.gov/tlodocs/88R/billtext/html/HB00025I.htm",
})

with gr.Blocks() as demo:
    name = gr.Textbox(label="Paste Your Bill Text In Here:")
    output = gr.Textbox(label="Analysis")
    greet_btn = gr.Button("ANALYZE")
    greet_btn.click(fn=greet, inputs=name, outputs=output)

demo.launch("share=True")