import PIL
import requests
import torch
import gradio as gr
import random
from PIL import Image
import os
import time
from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler

#Loading from Diffusers Library
model_id = "timbrooks/instruct-pix2pix"
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16") #, safety_checker=None)
pipe.to("cuda")
#pipe.enable_attention_slicing()
pipe.enable_xformers_memory_efficient_attention()
pipe.unet.to(memory_format=torch.channels_last)

counter = 0


help_text = """ Some notes from the official [instruct-pix2pix](https://huggingface.co/spaces/timbrooks/instruct-pix2pix) Space by the authors and from the official [Diffusers docs](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/pix2pix) -
If you're not getting what you want, there may be a few reasons:
1. Is the image not changing enough? Your guidance_scale may be too low. It should be >1. Higher guidance scale encourages to generate images 
that are closely linked to the text `prompt`, usually at the expense of lower image quality. This value dictates how similar the output should 
be to the input. This pipeline requires a value of at least `1`. It's possible your edit requires larger changes from the original image. 
                
2. Alternatively, you can toggle image_guidance_scale. Image guidance scale is to push the generated image towards the inital image. Image guidance 
                scale is enabled by setting `image_guidance_scale > 1`. Higher image guidance scale encourages to generate images that are closely 
                linked to the source image `image`, usually at the expense of lower image quality.  
3. I have observed that rephrasing the instruction sometimes improves results (e.g., "turn him into a dog" vs. "make him a dog" vs. "as a dog").
4. Increasing the number of steps sometimes improves results.
5. Do faces look weird? The Stable Diffusion autoencoder has a hard time with faces that are small in the image. Try:
    * Cropping the image so the face takes up a larger portion of the frame.
"""

def previous(image):
    return image 

def chat(image_in, in_steps, in_guidance_scale, in_img_guidance_scale, image_hid, img_name, counter_out, image_oneup, prompt, history, progress=gr.Progress(track_tqdm=True)):
    progress(0, desc="Starting...")
    if prompt.lower() == 'reverse' : #--to add revert functionality later
        history = history or []
        temp_img_name = img_name[:-4]+str(int(time.time()))+'.png' 
        image_oneup.save(temp_img_name)
        response = 'Reverted to the last image ' + '<img src="/file=' + temp_img_name + '">'  
        history.append((prompt, response))
        return history, history, image_oneup, temp_img_name, counter_out
    if prompt.lower() == 'restart' : #--to add revert functionality later
        history = history or []
        temp_img_name = img_name[:-4]+str(int(time.time()))+'.png' 
        image_in.save(temp_img_name)
        response = 'Reverted to the last image ' + '<img src="/file=' + temp_img_name + '">'  
        history.append((prompt, response))
        return history, history, image_in, temp_img_name, counter_out
    if counter_out > 0:
      edited_image = pipe(prompt, image=image_hid, num_inference_steps=int(in_steps), guidance_scale=float(in_guidance_scale), image_guidance_scale=float(in_img_guidance_scale)).images[0]
      if os.path.exists(img_name):
        os.remove(img_name)
      temp_img_name = img_name[:-4]+str(int(time.time()))+'.png' 
      # Create a file-like object
      with open(temp_img_name, "wb") as fp:
        # Save the image to the file-like object
        edited_image.save(fp)
      #Get the name of the saved image
      saved_image_name = fp.name
      #edited_image.save(temp_img_name) #, overwrite=True)
      counter_out += 1
    else:
      seed = random.randint(0, 1000000)
      img_name = f"./edited_image_{seed}.png"
      #Resizing the image
      basewidth = 512
      wpercent = (basewidth/float(image_in.size[0]))
      hsize = int((float(image_in.size[1])*float(wpercent)))
      image_in = image_in.resize((basewidth,hsize), Image.Resampling.LANCZOS)
      edited_image = pipe(prompt, image=image_in, num_inference_steps=int(in_steps), guidance_scale=float(in_guidance_scale), image_guidance_scale=float(in_img_guidance_scale)).images[0]
      if os.path.exists(img_name):
        os.remove(img_name)
      with open(img_name, "wb") as fp:
        # Save the image to the file-like object
        edited_image.save(fp)
      #Get the name of the saved image
      saved_image_name2 = fp.name
    history = history or []
    #Resizing (or not) the image for better display and adding supportive sample text
    add_text_list = ["There you go", "Enjoy your image!", "Nice work! Wonder what you gonna do next!", "Way to go!", "Does this work for you?", "Something like this?"]
    if counter_out > 0:
        response = random.choice(add_text_list) + '<img src="/file=' + saved_image_name + '">'  
        history.append((prompt, response))
        return history, history, edited_image, temp_img_name, counter_out
    else:
        response = random.choice(add_text_list) + '<img src="/file=' + saved_image_name2 + '">'   #IMG_NAME
        history.append((prompt, response))
        counter_out += 1
        return history, history, edited_image, img_name, counter_out
        
        

with gr.Blocks() as demo:
    gr.Markdown("""<h1><center>dummy</h1></center> """)
    with gr.Row():
      with gr.Column():
        image_in = gr.Image(type='pil', label="Original Image")
        text_in = gr.Textbox()
        state_in = gr.State()
        #with gr.Row():
        b1 = gr.Button('Edit the image!')
        #b2 = gr.Button('Revert!')
        with gr.Accordion("Advance settings for Training and Inference", open=False):
          gr.Markdown("Advance settings for - Number of Inference steps, Guidanace scale, and Image guidance scale.")
          in_steps = gr.Number(label="Enter the number of Inference steps", value = 20)
          in_guidance_scale = gr.Slider(1,10, step=0.5, label="Set Guidance scale", value=7.5)
          in_img_guidance_scale = gr.Slider(1,10, step=0.5, label="Set Image Guidance scale", value=1.5)
          image_hid = gr.Image(type='pil', visible=False)
          image_oneup = gr.Image(type='pil', visible=False)
          img_name_temp_out = gr.Textbox(visible=False)
          #img_revert = gr.Checkbox(visible=True, value=False,label=to track a revert message)
          counter_out = gr.Number(visible=False, value=0, precision=0)
      chatbot = gr.Chatbot() 
    b1.click(chat,[image_in, in_steps, in_guidance_scale, in_img_guidance_scale, image_hid, img_name_temp_out,counter_out, image_oneup,  text_in, state_in], [chatbot, state_in, image_hid, img_name_temp_out, counter_out]) #, queue=True)
    b1.click(previous, [image_hid], [image_oneup])
    #b2.click(previous, image_oneup, image_hid)
    gr.Markdown(help_text)
    
demo.queue(concurrency_count=10)
demo.launch(debug=True, width="80%", height=2000)