app.py

import os
import io
from PIL import Image
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
hf_api_key = os.environ['HF_API_KEY']

# Helper function
import requests, json

# API_URL = "https://api-inference.huggingface.co/models/sayakpaul/text-to-image-pokemons-gpt4"
# API_URL = "https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5"
# API_URL = "https://api-inference.huggingface.co/models/cloudqi/cqi_text_to_image_pt_v0"
# API_URL = "https://api-inference.huggingface.co/models/playgroundai/playground-v2-1024px-aesthetic"
# API_URL = "https://api-inference.huggingface.co/models/SimianLuo/LCM_Dreamshaper_v7"
# API_URL = "https://api-inference.huggingface.co/models/tensor-diffusion/majicMIX-realistic-v7"
API_URL = "https://api-inference.huggingface.co/models/digiplay/majicMIX_realistic_v1"



#Text-to-image endpoint
def get_completion(inputs, parameters=None, ENDPOINT_URL=API_URL):
    headers = {
      "Authorization": f"Bearer {hf_api_key}",
      "Content-Type": "application/json"
    }   
    data = { "inputs": inputs }
    if parameters is not None:
        data.update({"parameters": parameters})
    response = requests.request("POST",ENDPOINT_URL,headers=headers,data=json.dumps(data))
    return response.content

import gradio as gr 

def generate(prompt):
    output = get_completion(prompt)
    result_image = Image.open(io.BytesIO(output))
    return result_image

import gradio as gr 

def generate(prompt, negative_prompt, steps, guidance, width, height):
    params = {
        "negative_prompt": negative_prompt,
        "num_inference_steps": steps,
        "guidance_scale": guidance,
        "width": width,
        "height": height
    }
    
    output = get_completion(prompt, params)
    pil_image = Image.open(io.BytesIO(output))
    return pil_image

def loadGUI():
    with gr.Blocks() as demo:
        gr.Markdown("# Image Generation with Stable Diffusion - Magic Mix V1")
        with gr.Row():
            with gr.Column(scale=4):
                prompt = gr.Textbox(label="Your prompt") #Give prompt some real estate
            with gr.Column(scale=1, min_width=50):
                btn = gr.Button("Submit") #Submit button side by side!
        with gr.Accordion("Advanced options", open=False): #Let's hide the advanced options!
                negative_prompt = gr.Textbox(label="Negative prompt")
                with gr.Row():
                    with gr.Column():
                        steps = gr.Slider(label="Inference Steps", minimum=1, maximum=100, step=1, value=25,
                        info="In many steps will the denoiser denoise the image?")
                        guidance = gr.Slider(label="Guidance Scale", minimum=1, maximum=20, step=1, value=7,
                        info="Controls how much the text prompt influences the result")
                    with gr.Column():
                        width = gr.Slider(label="Width", minimum=64, maximum=1024, step=32, value=512)
                        height = gr.Slider(label="Height", minimum=64, maximum=1024, step=32, value=512)
        output = gr.Image(label="Result") #Move the output up too
                
        btn.click(fn=generate, inputs=[prompt,negative_prompt,steps,guidance,width,height], outputs=[output])

        gr.close_all()
        demo.launch(share=True)

def main():
     loadGUI()
     
     
if __name__ == "__main__":
     main()