import os
os.system("pip install deepspeed")
os.system("pip freeze")

import gradio as gr
import re
from magma import Magma
from magma.image_input import ImageInput

from huggingface_hub import hf_hub_url, cached_download

checkpoint_url = hf_hub_url(repo_id="osanseviero/magma", filename="model.pt")
checkpoint_path = cached_download(checkpoint_url)

model = Magma.from_checkpoint(
    config_path = "configs/MAGMA_v1.yml",
    checkpoint_path = checkpoint_path,
    device = 'cuda:0'
)

def generate(image,context, length, temperature, top_k):
  # context = context.strip()

  # url_regex = r'https?:\/\/(www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)'
  # lines = context.split('\n')
  # inputs = []
  # for line in lines:
  #   if re.match(url_regex, line):
  #     try:
  #       inputs.append(ImageInput(line))
  #     except Exception as e:
  #       return str(e)
  #   else:
  #     inputs.append(line)

  inputs =[
    ## supports urls and path/to/image
    ImageInput(image),
    context
  ]

  ## returns a tensor of shape: (1, 149, 4096)
  embeddings = model.preprocess_inputs(inputs)  

  ## returns a list of length embeddings.shape[0] (batch size)
  output = model.generate(
    embeddings = embeddings,
    max_steps = length,
    temperature = (0.01 if temperature == 0 else temperature),
    top_k = top_k
  )

  return context + output[0]

iface = gr.Interface(
  fn=generate,
  inputs=[
    [gr.inputs.Image(type="filepath"),gr.inputs.Textbox(
      label="Prompt:",
      default="Describe the painting:",
      lines=7)],
    gr.inputs.Slider(minimum=1, maximum=100, default=15, step=1, label="Output tokens:"),
    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.7, label='Temperature'),
    gr.inputs.Slider(minimum=0, maximum=100, default=0, step=1, label='Top K')
  ],
  outputs=["textbox"]
).launch()