import gradio as gr
import requests

# GPT-J-6B API
API_URL = "https://api-inference.huggingface.co/models/EleutherAI/gpt-j-6B"
headers = {"Authorization": "Bearer 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"}
prompt = """Oh, my life 
is changing every day
Every possible way
And oh, my dreams, 
it's never quite as it seems
Never quite as it seems"""

#examples = [["mind"], ["memory"], ["sleep"],["wellness"],["nutrition"]]


def poem2_generate(word):
  p = word.lower() + "\n" + "poem using word: "
  gr.Markdown("Prompt is :{p}")
  json_ = {"inputs": p,
            "parameters":
            {
            "top_p": 0.9,
          "temperature": 1.1,
          "max_new_tokens": 50,
          "return_full_text": False
          }}
  response = requests.post(API_URL, headers=headers, json=json_)
  output = response.json()
  gr.Markdown("error? Reason is : {output}")
  output_tmp = output[0]['generated_text']
  gr.Markdown("GPTJ response without splits is: {output_tmp}")
  poem = output[0]['generated_text'].split("\n\n")[0] # +"."
  if "\n\n" not in output_tmp:
    if output_tmp.find('.') != -1:
      idx = output_tmp.find('.')
      poem = output_tmp[:idx+1]
    else:
      idx = output_tmp.rfind('\n')
      poem = output_tmp[:idx]
  else:
    poem = output_tmp.split("\n\n")[0] # +"."
  poem = poem.replace('?','')
  gr.Markdown("Returned is: {poem}")
  return poem
  

def poem_generate(word):

  p = prompt + word.lower() + "\n" + "poem using word: "
  gr.Markdown("Generate - Prompt is :{p}")
  json_ = {"inputs": p,
            "parameters":
            {
            "top_p": 0.9,
          "temperature": 1.1,
          "max_new_tokens": 50,
          "return_full_text": False
          }}
  response = requests.post(API_URL, headers=headers, json=json_)
  output = response.json()
  gr.Markdown("error? Reason is : {output}")
  output_tmp = output[0]['generated_text']
  gr.Markdown("Response without splits is: {output_tmp}")
  poem = output[0]['generated_text'].split("\n\n")[0] # +"."
  if "\n\n" not in output_tmp:
    if output_tmp.find('.') != -1:
      idx = output_tmp.find('.')
      poem = output_tmp[:idx+1]
    else:
      idx = output_tmp.rfind('\n')
      poem = output_tmp[:idx]
  else:
    poem = output_tmp.split("\n\n")[0] # +"."
  poem = poem.replace('?','')
  gr.Markdown("Returned is: {poem}")
  return poem

def poem_to_image(poem):
  gr.Markdown("toimage")
  poem = " ".join(poem.split('\n'))
  poem = poem + " oil on canvas."
  steps, width, height, images, diversity = '50','256','256','1',15
  img = gr.Interface().load("spaces/multimodalart/latentdiffusion")(poem, steps, width, height, images, diversity)[0]
  return img

def set_example(example: list) -> dict:
    return gr.Textbox.update(value=example[0]) 


demo = gr.Blocks()

with demo:
  gr.Markdown("<h1><center>Few Shot Learning Text to Word Image Search</center></h1>")
  gr.Markdown("https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api,  https://github.com/EleutherAI/the-pile")
  with gr.Row():
    input_word = gr.Textbox(lines=7, value=prompt)
    poem_txt = gr.Textbox(lines=7)
    output_image = gr.Image(type="filepath", shape=(256,256))
  
  b1 = gr.Button("Generate Text")
  b2 = gr.Button("Generate Image")
  
  b1.click(poem2_generate, input_word, poem_txt)
  b2.click(poem_to_image, poem_txt, output_image)

  examples=[["living, loving,"], ["I want to live.  I want to give."],["Ive been to Hollywood.  Ive been to Redwood"]]
  example_text = gr.Dataset(components=[input_word], samples=examples)
  example_text.click(fn=set_example,inputs = example_text,outputs= example_text.components)


demo.launch(enable_queue=True, debug=True)