import gradio as gr
import requests
from PIL import Image
from io import BytesIO
import base64

api_url = "https://5cb20b40-572c-426f-9466-995256f9b6eb.id.repl.co/generate_image"

def generate_image(model="Deliberate", prompt="", seed=0, negative_prompt="", sampler="k_dpmpp_2s_a", steps=50):
    data = "?model=" + model + "&prompt=" + prompt + "&seed=" + str(seed) + "&negative_prompt=" + negative_prompt + "&sampler=" + sampler + "&steps=" + str(steps)
    response = requests.post(api_url + data, timeout=400)
    if response.status_code == 200:
        img_base64 = response.json()["url"]
        img_bytes = base64.b64decode(img_base64)
        img = Image.open(BytesIO(img_bytes))
        return img
    else:
        return None

inputs = [
     gr.inputs.Dropdown(['Abyss OrangeMix', 'Anything Diffusion', 'ChilloutMix', 'Deliberate', 'Dreamshaper', 'Dreamlike Diffusion', 'Dreamlike Photoreal',  'Experience',  'Midjourney Diffusion', 'ProtoGen',  'Realistic Vision', 'Rev Animated'], label="Model", default="Deliberate"),
    gr.inputs.Textbox(label="Prompt"),
    gr.inputs.Number(label="Seed", default=0),
    gr.inputs.Textbox(label="Negative Prompt", default=""),
    gr.inputs.Dropdown(["k_lms", "k_heun", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "DDIM", "k_dpm_fast", "k_dpm_adaptive", "k_dpmpp_2m", "k_dpmpp_2s_a", "k_dpmpp_sde"], label="Sampler", default="k_dpmpp_2s_a"),
    gr.inputs.Number(label="Steps", default=50)
]

outputs = gr.outputs.Image(label="Generated Image", type="pil")

interface = gr.Interface(generate_image, inputs, outputs, title="Best Diffusion Models", 
                         description="<center>Live access to the Most Popular Diffusion models of all time</center>", 
                         examples=[])

interface.launch()