import json
import requests
import gradio as gr
import random
import time
import os
import datetime
from datetime import datetime
from PIL import Image
from PIL import ImageOps
from PIL import Image, ImageDraw, ImageFont
from textwrap import wrap
import json
from io import BytesIO

print('for update')

API_TOKEN = os.getenv("API_TOKEN")
HRA_TOKEN=os.getenv("HRA_TOKEN")


from huggingface_hub import InferenceApi
inference = InferenceApi("bigscience/bloom",token=API_TOKEN)

headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
url_hraprompts='https://us-central1-createinsightsproject.cloudfunctions.net/gethrahfprompts'

data={"prompt_type":'stable_diffusion_tee_shirt_text',"hra_token":HRA_TOKEN}
try:
    r = requests.post(url_hraprompts, data=json.dumps(data), headers=headers)
except requests.exceptions.ReadTimeout as e:
    print(e)
#print(r.content)


prompt_text=str(r.content, 'UTF-8')
print(prompt_text)
data={"prompt_type":'stable_diffusion_tee_shirt_image',"hra_token":HRA_TOKEN}
try:
    r = requests.post(url_hraprompts, data=json.dumps(data), headers=headers)
except requests.exceptions.ReadTimeout as e:
    print(e)
#print(r.content)

prompt_image=str(r.content, 'UTF-8')
print(prompt_image)

ENDPOINT_URL="https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2-1" # url of your endpoint
#ENDPOINT_URL="https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-1-5" # url of your endpoint
HF_TOKEN=API_TOKEN# token where you deployed your endpoint

def generate_image(prompt_SD:str):
    print(prompt_SD)
    payload = {"inputs": prompt_SD,}
    headers = {
        "Authorization": f"Bearer {HF_TOKEN}",
        "Content-Type": "application/json",
        "Accept": "image/png" # important to get an image back
    }
    response = requests.post(ENDPOINT_URL, headers=headers, json=payload)
    print(response.content)
    img = Image.open(BytesIO(response.content))

    return img
    
def infer(prompt,
          max_length = 250,
          top_k = 0,
          num_beams = 0,
          no_repeat_ngram_size = 2,
          top_p = 0.9,
          seed=42,
          temperature=0.7,
          greedy_decoding = False,
          return_full_text = False):
    
    print(seed)
    top_k = None if top_k == 0 else top_k
    do_sample = False if num_beams > 0 else not greedy_decoding
    num_beams = None if (greedy_decoding or num_beams == 0) else num_beams
    no_repeat_ngram_size = None if num_beams is None else no_repeat_ngram_size
    top_p = None if num_beams else top_p
    early_stopping = None if num_beams is None else num_beams > 0

    params = {
        "max_new_tokens": max_length,
        "top_k": top_k,
        "top_p": top_p,
        "temperature": temperature,
        "do_sample": do_sample,
        "seed": seed,
        "early_stopping":early_stopping,
        "no_repeat_ngram_size":no_repeat_ngram_size,
        "num_beams":num_beams,
        "return_full_text":return_full_text
    }
    
    s = time.time()
    response = inference(prompt, params=params)
    #print(response)
    proc_time = time.time()-s
    #print(f"Processing time was {proc_time} seconds")
    return response

def getadline(text_inp):
    print(text_inp)
    print(datetime.today().strftime("%d-%m-%Y"))
  
    text = prompt_text+"\nInput:"+text_inp + "\nOutput:"
    resp = infer(text,seed=random.randint(0,100))
    
    generated_text=resp[0]['generated_text']
    result = generated_text.replace(text,'').strip()
    result = result.replace("Output:","")
    parts = result.split("###")
    topic = parts[0].strip()
    topic="\n".join(topic.split('\n'))
    
    response_nsfw = requests.get('https://github.com/coffee-and-fun/google-profanity-words/raw/main/data/list.txt')
    data_nsfw = response_nsfw.text
    nsfwlist=data_nsfw.split('\n')
    nsfwlowerlist=[]
    for each in nsfwlist:
      if each!='':
        nsfwlowerlist.append(each.lower())
    nsfwlowerlist.extend(['bra','gay','lesbian',])
    print(topic)
    mainstring=text_inp
    foundnsfw=0
    for each_word in nsfwlowerlist:
        raw_search_string = r"\b" + each_word + r"\b"
        match_output = re.search(raw_search_string, mainstring)
        no_match_was_found = ( match_output is None )
        if no_match_was_found:
          foundnsfw=0
        else:
          foundnsfw=1
          print(each_word)
          break
    if foundnsfw==1:
        topic="Unsafe content found. Please try again with different prompts."
        print(topic)
    return(topic)

def getadvertisement(topic):
    if topic!='':
        input_keyword=topic
    else:
        input_keyword=getadline(random.choice('abcdefghijklmnopqrstuvwxyz'))
        if 'Unsafe content found' in input_keyword:
            input_keyword='Abstarct art with splash of colors'
    prompt_SD=input_keyword+','+prompt_image
    # generate image
    image = generate_image(prompt_SD)
    
    # save to disk
    image.save("finalimage.png")
    
    return 'finalimage.png'


with gr.Blocks() as demo:
    gr.Markdown("<h1><center>Tee Shirt Designs</center></h1>")
    gr.Markdown(
        """Enter a prompt  and get the tee shirt design. Use examples as a guide. We use an equally powerful AI model bigscience/bloom."""
        )
    textbox = gr.Textbox(placeholder="Enter prompt...", lines=1,label='Your prompt')
    btn = gr.Button("Generate")    
    #output1 = gr.Textbox(lines=2,label='Market Sizing Framework')
    output_image = gr.components.Image(label="Your tee shirt")


    btn.click(getadvertisement,inputs=[textbox], outputs=[output_image])
    examples = gr.Examples(examples=['anime art of man fighting','intricate skull','heavy metal band cover','abstract art of plants',],
                           inputs=[textbox])
    

demo.launch()