import numpy as np
import gradio as gr
import ast
import requests

from theme_dropdown import create_theme_dropdown  # noqa: F401

dropdown, js = create_theme_dropdown()

models = [
    {"name": "Stable Diffusion 2", "url": "stabilityai/stable-diffusion-2-1"},
    {"name": "stability AI", "url": "stabilityai/stable-diffusion-2-1-base"},
    {"name": "Compressed-S-D", "url": "nota-ai/bk-sdm-small"},
    {"name": "Future Diffusion", "url": "nitrosocke/Future-Diffusion"},
    {"name": "JWST Deep Space Diffusion", "url": "dallinmackay/JWST-Deep-Space-diffusion"},
    {"name": "Robo Diffusion 3 Base", "url": "nousr/robo-diffusion-2-base"},
    {"name": "Robo Diffusion", "url": "nousr/robo-diffusion"},
    {"name": "Tron Legacy Diffusion", "url": "dallinmackay/Tron-Legacy-diffusion"},
]   

text_gen = gr.Interface.load("spaces/daspartho/prompt-extend")

current_model = models[0]

models2 = []
for model in models:
    model_url = f"models/{model['url']}"
    loaded_model = gr.Interface.load(model_url, live=True, preprocess=True)
    models2.append(loaded_model)

def text_it(inputs, text_gen=text_gen):
    return text_gen(inputs)

def flip_text(x):
    return x[::-1]

def send_it(inputs, model_choice):
    proc = models2[model_choice]
    return proc(inputs)


def flip_image(x):
    return np.fliplr(x)


def set_model(current_model_index):
    global current_model
    current_model = models[current_model_index]
    return gr.update(value=f"{current_model['name']}")


with gr.Blocks(theme='pikto/theme@>=0.0.1,<0.0.3') as pan:
    gr.Markdown("AI CONTENT TOOLS.")
                    
    with gr.Tab("T-to-I"):
        
    ##model = ("stabilityai/stable-diffusion-2-1")
         model_name1 = gr.Dropdown(
                label="Choose Model",
                choices=[m["name"] for m in models],
                type="index",
                value=current_model["name"],
                interactive=True,
         )
         input_text = gr.Textbox(label="Prompt idea",)

        ##  run = gr.Button("Generate Images")
         with gr.Row():
             see_prompts = gr.Button("Generate Prompts")
             run = gr.Button("Generate Images", variant="primary")
        
         with gr.Row():
             magic1 = gr.Textbox(label="Generated Prompt", lines=2)
             output1 = gr.Image(label="")
          
             
         with gr.Row():    
             magic2 = gr.Textbox(label="Generated Prompt", lines=2)
             output2 = gr.Image(label="")

            
         run.click(send_it, inputs=[magic1, model_name1], outputs=[output1])
         run.click(send_it, inputs=[magic2, model_name1], outputs=[output2])
         see_prompts.click(text_it, inputs=[input_text], outputs=[magic1])
         see_prompts.click(text_it, inputs=[input_text], outputs=[magic2])
        
    model_name1.change(set_model, inputs=model_name1, outputs=[output1, output2,])
        
    with gr.Tab("Flip Image"):
         #Using Gradio Demos as API - This is Hot!
         API_URL_INITIAL = "https://ysharma-playground-ai-exploration.hf.space/run/initial_dataframe"
         API_URL_NEXT10 = "https://ysharma-playground-ai-exploration.hf.space/run/next_10_rows"

#define inference function
#First: Get initial images for the grid display 
def get_initial_images():
  response = requests.post(API_URL_INITIAL, json={
            "data": []
            }).json()
  #data = response["data"][0]['data'][0][0][:-1]
  response_dict = response['data'][0]
  return response_dict  #, [resp[0][:-1] for resp in response["data"][0]["data"]]

#Second: Process response dictionary to get imges as hyperlinked image tags
def process_response(response_dict):
  return [resp[0][:-1] for resp in response_dict["data"]]

response_dict = get_initial_images()
initial = process_response(response_dict)
initial_imgs  = '<div style="display: grid; grid-template-columns: repeat(3, 1fr); grid-template-rows: repeat(3, 1fr); grid-gap: 0; background-color: #fff; padding: 20px; box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);">\n' + "\n".join(initial[:-1])

#Third: Load more images for the grid
def get_next10_images(response_dict, row_count):
    row_count = int(row_count)
    #print("(1)",type(response_dict))
    #Convert the string to a dictionary
    if isinstance(response_dict, dict) == False :
        response_dict = ast.literal_eval(response_dict)
    response = requests.post(API_URL_NEXT10, json={
              "data": [response_dict, row_count ] #len(initial)-1
               }).json()
    row_count+=10
    response_dict = response['data'][0]
    #print("(2)",type(response))
    #print("(3)",type(response['data'][0]))
    next_set  = [resp[0][:-1] for resp in response_dict["data"]]
    next_set_images = '<div style="display: grid; grid-template-columns: repeat(3, 1fr); grid-template-rows: repeat(3, 1fr); grid-gap: 0; background-color: #fff; padding: 20px; box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); ">\n' + "\n".join(next_set[:-1])
    return response_dict, row_count, next_set_images  #response['data'][0]

#get_next10_images(response_dict=response_dict, row_count=9)
#position: fixed; top: 0; left: 0; width: 100%; background-color: #fff; padding: 20px; box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);

#Defining the Blocks layout
with gr.Blocks(css = """#img_search img {width: 100%; height: 100%; object-fit: cover;}""") as demo:
  gr.HTML(value="top of page", elem_id="top",visible=False)
  gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
        <div
        style="
            display: inline-flex;
            align-items: center;
            gap: 0.8rem;
            font-size: 1.75rem;
        "
        >
        <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
            Using Gradio Demos as API - 2 </h1><br></div>
        <div><h4 style="font-weight: 500; margin-bottom: 7px; margin-top: 5px;">
            Stream <a href="https://github.com/playgroundai/liked_images" target="_blank">PlaygroundAI Images</a> ina beautiful grid</h4><br>
        </div>""")
  with gr.Accordion(label="Details about the working:", open=False, elem_id='accordion'):
    gr.HTML("""
        <p style="margin-bottom: 10px; font-size: 90%"><br>
        ▶️Do you see the "view api" link located in the footer of this application? 
        By clicking on this link, a page will open which provides documentation on the REST API that developers can use to query the Interface function / Block events.<br>
        ▶️In this demo, I am making such an API request to the <a href="https://huggingface.co/spaces/ysharma/Playground_AI_Exploration" target="_blank">Playground_AI_Exploration</a> Space.<br>
        ▶️I am exposing an API endpoint of this Gradio app as well. This can easily be done by one line of code, just set the api_name parameter of the event listener.
        </p></div>""")

  with gr.Column(): #(elem_id = "col-container"):
    b1 = gr.Button("Load More Images").style(full_width=False)
    df = gr.Textbox(visible=False,elem_id='dataframe', value=response_dict)
    row_count = gr.Number(visible=False, value=19 )
    img_search = gr.HTML(label = 'Images from PlaygroundAI dataset', elem_id="img_search", 
                         value=initial_imgs ) #initial[:-1] )
      
  gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/Stream_PlaygroundAI_Images?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a></center> 
        </p></div>''')
  b1.click(get_next10_images, [df, row_count], [df, row_count, img_search], api_name = "load_playgroundai_images" ) 
            

    with gr.Tab("Diffuser"):
        with gr.Row():
            text_input = gr.Textbox()                      ##   Diffuser
            image_output = gr.Image()
        image_button = gr.Button("Flip")



   # text_button.click(flip_text, inputs=text_input, outputs=text_output)
   # image_button.click(flip_image, inputs=image_input, outputs=image_output)
pan.queue(concurrency_count=200)
pan.launch(inline=True, show_api=True, max_threads=400)