from pathlib import Path
import numpy as np
import gradio as gr
import utils.shared_utils as st


import torch
from torch import autocast
import torchvision.transforms as T
from contextlib import nullcontext
device = "cuda" if torch.cuda.is_available() else "cpu"
context = autocast if device == "cuda" else nullcontext
# Apply the transformations needed



def select_input(input_img,webcm_img):
    if input_img is None:
        img= webcm_img
    else:
        img=input_img
    return img


def infer(prompt,samples):
    images= []
    selections = ["Img_{}".format(str(i+1).zfill(2)) for i in range(samples)]
    with context(device):
        for _ in range(samples):
            back_img = st.stableDiffusionAPICall(prompt)
            images.append(back_img)
    return images





def change_bg_option(choice):
    if choice == "I have an Image":
        return gr.Image(shape=(800, 800))

    elif choice == "Generate one for me":
        return gr.update(lines=8, visible=True, value="Please enter a text prompt")
    else:
        return gr.update(visible=False)


# TEXT
title = "FSDL- One-Shot, Green-Screen,   Composition-Transfer"
DEFAULT_TEXT = "Photorealistic scenery of bookshelf in a room"
description = """
<center><a href="https://docs.google.com/document/d/1fde8XKIMT1nNU72859ytd2c58LFBxepS3od9KFBrJbM/edit?usp=sharing">[PAPER]</a> <a href="https://github.com/snknitin/FSDL-Project/blob/main/src/utils/shared_utils.py">[CODE]</a></center>
<details>
<summary><b>Instructions</b></summary>
<p style="margin-top: -3px;">With this app, you can generate a suitable background image to overlay your portrait!<br />You have several ways to set how your final auto-edited image will look like:<br /></p>
 <ul style="margin-top: -20px;margin-bottom: -15px;">
  <li style="margin-bottom: -10px;margin-left: 20px;">Use the "<i>Inputs</i>" tab to either upload an image from your device or allow the use of your webcam to capture</li>
  <li style="margin-left: 20px;">Use the "<i>Background Image Inputs</i>" to upload your own background</li>
  <li style="margin-left: 20px;">Use the "<i>Text prompt</i>" tab to generate a satisfactory bacground image.</li>
</ul> 
<p>After customization, just hit "<i>Edit</i>" and wait a few seconds.<br />The final image will be available for download <br /> <b>Enjoy!<b><p>
</details>
"""

running = """

### Instructions for running the 3 S's in sequence

* **Superimpose** - This button allows you to isolate the foreground from your image and overlay it on the background. Remove background using alpha matting 
* **Style-Transfer** - This button transfer the style from your original image to re-map your new background realistically. Uses Nvidia FastPhotoStyle
* **Smoothing** - Given than image resolutions and clarity can be an issue, this smoothing button makes your final image crisp after the stylization transfer. Fair warning - this last process can take 5-10 mins
"""


demo = gr.Blocks()

with demo:
    gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>" + title + "</h1>")
    with gr.Box():
        gr.Markdown(description)
    # First row - Inputs
    with gr.Row(scale=1):
        with gr.Column():
            with gr.Tabs():
                with gr.TabItem("Upload "):
                    input_img = gr.Image(shape=(800, 800), interactive=True, label="You")
                with gr.TabItem("Webcam Capture"):
                    webcm_img = gr.Image(source="webcam", streaming=True, shape=(800, 800), interactive=True)
            inp_select_btn = gr.Button("Select")

        with gr.Column():
            with gr.Tabs():
                with gr.TabItem("Upload"):
                    bgm_img = gr.Image(shape=(800, 800), type="pil", interactive=True, label="The Background")
                    bgm_select_btn = gr.Button("Select")

                with gr.TabItem("Generate via Text Prompt"):
                    with gr.Box():
                        with gr.Row().style(mobile_collapse=False, equal_height=True):
                            text = gr.Textbox(lines=7,
                                              placeholder="Enter your prompt to generate a background image... something like - Photorealistic scenery of bookshelf in a room")

                            samples = gr.Slider(label="Number of Images", minimum=1, maximum=5, value=2, step=1)
                            btn = gr.Button("Generate images",variant="primary").style(
                                margin=False,
                                rounded=(False, True, True, False),
                            )

                    gallery = gr.Gallery(label="Generated images", show_label=True).style(grid=(1, 3), height="auto")
                    # image_options = gr.Radio(label="Pick", interactive=True, choices=None, type="value")
                    text.submit(infer, inputs=[text, samples], outputs=gallery)
                    btn.click(infer, inputs=[text, samples], outputs=gallery, show_progress=True, status_tracker=None)


    # Second Row - Backgrounds
    with gr.Row(scale=1):
        with gr.Column():
            final_input_img = gr.Image(shape=(800, 800), type="pil", label="Foreground")

        with gr.Column():
            final_back_img = gr.Image(shape=(800, 800), type="pil", label="Background", interactive=True)

        bgm_select_btn.click(fn=lambda x: x, inputs=bgm_img, outputs=final_back_img)

    inp_select_btn.click(select_input, [input_img, webcm_img], final_input_img)

    with gr.Row(scale=1):
        with gr.Box():
            gr.Markdown(running)

    with gr.Row(scale=1):

        with gr.Column(scale=1):
            supimp_btn = gr.Button("SuperImpose")
            overlay_img = gr.Image(shape=(800, 800), label="Overlay", type="pil")


        with gr.Column(scale=1):
            style_btn = gr.Button("Composition-Transfer",variant="primary")
            style_img = gr.Image(shape=(800, 800),label="Style-Transfer Image",type="pil")

        with gr.Column(scale=1):
            submit_btn = gr.Button("Smoothen",variant="primary")
            output_img = gr.Image(shape=(800, 800),label="FinalSmoothened Image",type="pil")

        supimp_btn.click(fn=st.superimpose, inputs=[final_input_img, final_back_img], outputs=[overlay_img])
        style_btn.click(fn=st.style_transfer, inputs=[overlay_img,final_input_img], outputs=[style_img])
        submit_btn.click(fn=st.smoother, inputs=[style_img,overlay_img], outputs=[output_img])

demo.queue()
demo.launch()