from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import gradio as gr

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

def text_generation(input_text, seed):
  input_ids = tokenizer(input_text, return_tensors="pt").input_ids
  torch.manual_seed(seed) # Max value: 18446744073709551615
  outputs = model.generate(input_ids, do_sample=True, min_length=50, max_length=200)
  generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
  return generated_text

title = "Text Generator Demo GPT-Neo"
description = "Text Generator Application"

# gr.Interface(
#     text_generation,
#     [gr.inputs.Textbox(lines=2, label="Enter input text"), gr.inputs.Number(default=10, label="Enter seed number")],
#     [gr.outputs.Textbox(type="text", label="Text Generated")],
#     title=title,
#     description=description,
#     theme="huggingface"
# ).launch()

examples = [
    ["Once upon a time", 123],
    ["In a galaxy far, far away", 42],
    # ["Lorem ipsum dolor sit amet", 999],
    ["The owners were also directed", 23],
]

iface = gr.Interface(
    fn=text_generation,
    inputs=[
        gr.inputs.Textbox(lines=2, label="Enter input text"),
        gr.inputs.Number(default=10, label="Enter seed number")
    ],
    outputs=gr.outputs.Textbox(type="text", label="Text Generated"),
    title=title,
    description=description,
    theme="huggingface",
    examples=examples
)

iface.launch()