# 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()