import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextDataset, DataCollatorForLanguageModeling, Trainer, TrainingArguments, pipeline
from accelerate import Accelerator
accelerator = Accelerator(cpu=True)

# def greet(name):
#     return "Hello " + name + "!!"

tokenizer = accelerator.prepare(AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125m"))
model = accelerator.prepare(AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-125m"))

def plex(input_text):    
    mnputs = tokenizer(input_text, return_tensors='pt')
    prediction = model.generate(mnputs['input_ids'], min_length=20, max_length=150, num_return_sequences=1)
    lines = tokenizer.decode(prediction[0]).splitlines()
    return lines[0]

iface=gr.Interface(
    fn=plex,
    inputs=gr.Textbox(label="Prompt", value="Once upon a"),
    outputs=gr.Textbox(label="Generated_Text"),
    title="GPT-Neo-125M",
    description="Prompt"
    )
iface.queue(max_size=1,api_open=False)
iface.launch(max_threads=1)

# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
# iface.launch()