import torch
import gradio as gr
from transformers import GPT2Tokenizer, GPT2LMHeadModel, pipeline

tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
tokenizer.add_special_tokens({'pad_token': '[PAD]'})
model = GPT2LMHeadModel.from_pretrained('FredZhang7/anime-anything-promptgen-v2')

# prompt = r'1girl, genshin'

# generate text using fine-tuned model
nlp = pipeline('text-generation', model=model, tokenizer=tokenizer)

def generate(prompt):
    # generate 10 samples using contrastive search
    outs = nlp(prompt, max_length=76, num_return_sequences=3, do_sample=True, repetition_penalty=1.2, temperature=0.7, top_k=3, early_stopping=True)
    outs_json=outs.to_json(orient="records")
    print(prompt)
    print(outs_json)
    return outs


# for i in range(len(outs)):
    # remove trailing commas and double spaces
    # outs[i] = str(outs[i]['generated_text']).replace('  ', '').rstrip(',')
# print('\033[92m' + '\n\n'.join(outs) + '\033[0m\n')
    # print(str(outs[i]['generated_text']))
    
input_component = gr.Textbox(label = "Input a prompt", value = "1girl, genshin")
output_component = gr.Textbox(label = "detail Prompt")
examples = []
description = ""
gr.Interface(generate, inputs = input_component, outputs=output_component, examples=examples, title = "anything prompt", description=description).launch()