#This is an example that uses the websockets api to know when a prompt execution is done
#Once the prompt execution is done it downloads the images using the /history endpoint

import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
import uuid
import json
import urllib.request
import urllib.parse
import random
import io
from fastapi import FastAPI, Response
from pydantic import BaseModel
from PIL import Image

server_address = "127.0.0.1:8188"
client_id = str(uuid.uuid4())

def queue_prompt(prompt):
    p = {"prompt": prompt, "client_id": client_id}
    data = json.dumps(p).encode('utf-8')
    req =  urllib.request.Request("http://{}/prompt".format(server_address), data=data)
    return json.loads(urllib.request.urlopen(req).read())

def get_image(filename, subfolder, folder_type):
    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
    url_values = urllib.parse.urlencode(data)
    with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response:
        return response.read()

def get_history(prompt_id):
    with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response:
        return json.loads(response.read())

def get_images(ws, prompt):
    prompt_id = queue_prompt(prompt)['prompt_id']
    output_images = {}
    while True:
        out = ws.recv()
        if isinstance(out, str):
            message = json.loads(out)
            if message['type'] == 'executing':
                data = message['data']
                if data['node'] is None and data['prompt_id'] == prompt_id:
                    break #Execution is done
        else:
            # If you want to be able to decode the binary stream for latent previews, here is how you can do it:
            # bytesIO = BytesIO(out[8:])
            # preview_image = Image.open(bytesIO) # This is your preview in PIL image format, store it in a global
            continue #previews are binary data

    history = get_history(prompt_id)[prompt_id]
    for node_id in history['outputs']:
        node_output = history['outputs'][node_id]
        images_output = []
        if 'images' in node_output:
            for image in node_output['images']:
                image_data = get_image(image['filename'], image['subfolder'], image['type'])
                images_output.append(image_data)
        output_images[node_id] = images_output

    return output_images

app = FastAPI()

class PromptRequest(BaseModel):
    prompt: str

@app.post("/generate-image")
async def generate_image(prompt_request: PromptRequest):
    # Load the workflow JSON
    with open("wsj-api-rnd-v2.json", "r", encoding="utf-8") as f:
        workflow_jsondata = f.read()
    
    jsonwf = json.loads(workflow_jsondata)
    
    # Set the text prompt
    jsonwf["6"]["inputs"]["text"] = prompt_request.prompt
    
    # Set a random seed
    seednum = random.randint(0, 10000)
    jsonwf["25"]["inputs"]["noise_seed"] = seednum
    
    # Connect to WebSocket
    ws = websocket.WebSocket()
    ws.connect(f"ws://{server_address}/ws?clientId={client_id}")
    
    # Generate images
    images = get_images(ws, jsonwf)
    ws.close()
    
    # Assuming we want to return the first image from the first node
    first_node = next(iter(images))
    image_data = images[first_node][0]
    
    # Convert image data to PIL Image
    image = Image.open(io.BytesIO(image_data))
    
    # Convert PIL Image to bytes
    img_byte_arr = io.BytesIO()
    image.save(img_byte_arr, format='PNG')
    img_byte_arr = img_byte_arr.getvalue()
    
    return Response(content=img_byte_arr, media_type="image/png")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)