import PIL
import requests
import torch
import gradio as gr
import random
from PIL import Image
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()

counter = 0


help_text = """ Note: I will try to add the functionality to revert your changes to previous/original image in future versions of space. For now only forward editing is available.

From the official Space by the authors [instruct-pix2pix](https://huggingface.co/spaces/timbrooks/instruct-pix2pix) 
and from 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 chat(image_in, message, history, progress=gr.Progress(track_tqdm=True)):
    progress(0, desc="Starting...")
    global counter 
    #global seed 
    #img_nm = f"./edited_image_{seed}.png"
    counter += 1
    #print(f"seed is : {seed}")
    #print(f"image_in name is :{img_nm}")
    
    #if message == "revert": --to add revert functionality later
    if counter > 1:
      # Open the image
      image_in = Image.open("edited_image.png") #(img_nm) 
    prompt = message #eg - "turn him into cyborg"
    edited_image = pipe(prompt, image=image_in, num_inference_steps=20, image_guidance_scale=1).images[0]
    edited_image.save("edited_image.png") # (img_nm) #("./edited_image.png")
    history = history or []
    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? "]
    #Resizing the image for better display
    #response = random.choice(add_text_list) + '<img src="/file=' + img_nm[2:] + '" style="width: 200px; height: 200px;">'
    response =  random.choice(add_text_list) + '<img src="/file=edited_image.png" style="width: 200px; height: 200px;">'
    history.append((message, response))
    return history, history

with gr.Blocks() as demo:
    gr.Markdown("""<h1><center> Chat Interface with InstructPix2Pix: Give Image Editing Instructions </h1></center>
    <p>For faster inference without waiting in the queue, you may duplicate the space and upgrade to GPU in settings.<br/>
    <a href="https://huggingface.co/spaces/ysharma/InstructPix2Pix_Chatbot?duplicate=true">
    <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
    <p/>""")
    with gr.Row():
      with gr.Column():
        image_in = gr.Image(type='pil', label="Original Image")
        text_in = gr.Textbox()
        state_in = gr.State()
        b1 = gr.Button('Edit the image!')
      chatbot = gr.Chatbot() 
    b1.click(chat,[image_in, text_in, state_in], [chatbot, state_in])
    gr.Markdown(help_text)
    
demo.queue(concurrency_count=10) 
demo.launch(debug=True, width="80%", height=1500)