import os
import subprocess

GIT_TOKEN = os.environ.get("GIT_TOKEN")
GIT_USER = os.environ.get("GIT_USER")

lib_url = f"git+https://{GIT_USER}:{GIT_TOKEN}@github.com/vasudevgupta7/huggingface-task@main"
subprocess.run(f"pip3 install -q {lib_url}".split())

import torch
import numpy as np
import gradio as gr
from transformers import AutoModel, AutoTokenizer
from pytorch_pretrained_biggan import BigGAN
from huggingface_task.models import AutoEncoder
from huggingface_task.run_model import generate_image_from_text

biggan_id = 'biggan-deep-128'
text_encoder_id = "distilbert-base-uncased"
autoencoder_id = "vasudevgupta/biggan-mapping-model"

text_tokenizer = AutoTokenizer.from_pretrained(text_encoder_id)
text_model = AutoModel.from_pretrained(text_encoder_id)

autoencoder = AutoEncoder.from_pretrained(autoencoder_id)
biggan = BigGAN.from_pretrained(biggan_id)

device = "cuda" if torch.cuda.is_available() else "cpu"
biggan.to(device).eval()
text_model.to(device).eval()
autoencoder.to(device).eval()

def generate_image(text_query):
  array = generate_image_from_text(text_query, text_tokenizer, text_model, autoencoder, biggan, device=device)
  array = ((array + 1.0) / 2.0) * 256
  array.clip(0, 255, out=array)
  array = np.asarray(np.uint8(array), dtype=np.uint8)
  return array

gr.Interface(fn=generate_image, inputs="text", outputs="image").launch()