import os
from flask import Flask, request
import requests
from gradio_client import Client
import base64

from PIL import Image
from io import BytesIO
import base64
import os

from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
from diffusers.utils import load_image
import torch

import gradio as gr

controlnet = ControlNetModel.from_pretrained("rgres/sd-controlnet-aerialdreams", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet, torch_dtype=torch.float16
)

pipe = pipe.to("cuda")

# CPU offloading for faster inference times
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()

app = Flask(__name__, static_url_path='/static')


@app.route('/')
def index():
    return app.send_static_file('index.html')


def save_base64_image(base64Image):
    image_data = base64.b64decode(base64Image)
    path = "input_image.jpg"
    with open(path, 'wb') as f:
        f.write(image_data)
    return path


def encode_image_to_base64(filepath):
    with open(filepath, "rb") as image_file:
        encoded_image = base64.b64encode(image_file.read()).decode("utf-8")
    return encoded_image


def generate_map(image, prompt, steps, seed):
    #image = Image.open(BytesIO(base64.b64decode(image_base64)))
    generator = torch.manual_seed(seed)

    image = pipe(
        prompt=prompt,
        num_inference_steps=steps,
        image=image
    ).images[0]

    return image


@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    
    base64Image = data['data'][0]
    prompt = data['data'][1]
    steps = data['data'][2]
    seed = data['data'][3]

    b64meta, b64_data = base64Image.split(',')
    image = Image.open(BytesIO(base64.b64decode(b64_data)))
    
    return generate_map(image, prompt, steps, seed)


if __name__ == '__main__':
    app.run(host='0.0.0.0',  port=int(
        os.environ.get('D2M_PORT', 8000)), debug=True)