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# import pathlib
import gradio as gr
# import transformers
# from transformers import AutoTokenizer
# from transformers import ModelForCausalLM
# from transformers import GenerationConfig
# from typing import List, Dict, Union
# from typing import Any, TypeVar
# Pathable = Union[str, pathlib.Path]
# def load_model(name: str) -> Any:
# return ModelForCausalLM.from_pretrained(name)
# def load_tokenizer(name: str) -> Any:
# return AutoTokenizer.from_pretrained(name)
# def create_generator():
# return GenerationConfig(
# temperature=1.0,
# top_p=0.75,
# num_beams=4,
# )
# def generate_prompt(instruction, input=None):
# if input:
# return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
# ### Instruction:
# {instruction}
# ### Input:
# {input}
# ### Response:"""
# else:
# return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
# ### Instruction:
# {instruction}
# ### Response:"""
# def evaluate(instruction, input=None):
# prompt = generate_prompt(instruction, input)
# inputs = tokenizer(prompt, return_tensors="pt")
# input_ids = inputs["input_ids"].cuda()
# generation_output = model.generate(
# input_ids=input_ids,
# generation_config=generation_config,
# return_dict_in_generate=True,
# output_scores=True,
# max_new_tokens=256
# )
# for s in generation_output.sequences:
# output = tokenizer.decode(s)
# print("Response:", output.split("### Response:")[1].strip())
# def inference(text):
# output = evaluate(instruction = instruction, input = input)
# return output
# io = gr.Interface(
# inference,
# gr.Textbox(
# lines = 3, max_lines = 10,
# placeholder = "Add question here",
# interactive = True,
# show_label = False
# ),
# gr.Textbox(
# lines = 3,
# max_lines = 25,
# placeholder = "add context here",
# interactive = True,
# show_label = False
# ),
# outputs =[
# gr.Textbox(lines = 2, label = 'Pythia410m output', interactive = False)
# ]
# ),
# title = title,
# description = description,
# article = article,
# examples = examples,
# cache_examples = False,
# )
# io.launch()
gr.Interface.load("models/s3nh/pythia-410m-70k-steps-self-instruct-polish").launch()
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