import gradio as gr
import requests
from pydantic import BaseModel
from diffusers.utils import load_image
from io import BytesIO



sdxl_inference_endpoint = 'https://vikramsingh178-picpilot-server.hf.space/api/v1/product-diffusion/sdxl_v0_lora_inference'
kandinsky_inpainting_inference = 'https://vikramsingh178-picpilot-server.hf.space/api/v1/product-diffusion/inpainting'

# Define the InpaintingRequest model
class InputRequest(BaseModel):
    prompt: str
    num_inference_steps: int
    guidance_scale: float
    negative_prompt: str
    num_images: int
    mode: str

class InpaintingRequest(BaseModel):
    prompt: str
    negative_prompt: str
    num_inference_steps: int
    strength: float
    guidance_scale: float
    mode: str

async def generate_sdxl_lora_image(prompt, negative_prompt, num_inference_steps, guidance_scale, num_images, mode):
    # Prepare the payload for SDXL LORA API
    payload = InputRequest(
        prompt=prompt,
        negative_prompt=negative_prompt,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        num_images=num_images,
        mode=mode
    ).dict()
    
    response = requests.post(sdxl_inference_endpoint, json=payload)
    response = response.json()
    url = response['url']
    image = load_image(url)
    return image



def generate_outpainting(prompt, negative_prompt, num_inference_steps, strength, guidance_scale, mode, image):
    # Convert the image to bytes
    img_byte_arr = BytesIO()
    image.save(img_byte_arr, format='PNG')
    img_byte_arr = img_byte_arr.getvalue()
    
    # Prepare the payload for multipart/form-data
    files = {
        'image': ('image.png', img_byte_arr, 'image/png'),
        'prompt': (None, prompt),
        'negative_prompt': (None, negative_prompt),
        'num_inference_steps': (None, str(num_inference_steps)),
        'strength': (None, str(strength)),
        'guidance_scale': (None, str(guidance_scale)),
        'mode': (None, mode)
    }
    
    response = requests.post(kandinsky_inpainting_inference, files=files)
    response.raise_for_status()
    response = response.json()
    url = response['url']
    image = load_image(url)
    return image



with gr.Blocks(theme='VikramSingh178/Webui-Theme') as demo:
    with gr.Tab("SdxL-Lora"):
       with gr.Row():
            with gr.Column():
              with gr.Group():
                prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
                negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here")
                num_inference_steps = gr.Slider(minimum=1, maximum=1000, step=1, value=20, label="Inference Steps")
                guidance_scale = gr.Slider(minimum=1.0, maximum=10.0, step=0.1, value=7.5, label="Guidance Scale")
                num_images = gr.Slider(minimum=1, maximum=10, step=1, value=1, label="Number of Images")
                mode = gr.Dropdown(choices=["s3_json", "b64_json"], value="s3_json", label="Mode")
                generate_button = gr.Button("Generate Image",variant='primary')
            
            with gr.Column(scale=1):
              
                image_preview = gr.Image(label="Generated Image",show_download_button=True,show_share_button=True,container=True)
                generate_button.click(generate_sdxl_lora_image, inputs=[prompt, negative_prompt, num_inference_steps, guidance_scale, num_images, mode], outputs=[image_preview])

    with gr.Tab("Generate AI Background"):
        with gr.Row():
            with gr.Column():
              with gr.Group():
                image_input = gr.Image(type="pil", label="Upload Image")
                prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
                negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here")
                num_inference_steps = gr.Slider(minimum=1, maximum=500, step=1, value=20, label="Inference Steps")
                guidance_scale = gr.Slider(minimum=1.0, maximum=10.0, step=0.1, value=7.5, label="Guidance Scale")
                strength = gr.Slider(minimum=0.1, maximum=1, step=0.1, value=1, label="Strength")
                mode = gr.Dropdown(choices=["s3_json", "b64_json"], value="s3_json", label="Mode")
                generate_button = gr.Button("Generate Background", variant='primary')

            with gr.Column(scale=1):
            
                image_preview = gr.Image(label="Image", show_download_button=True, show_share_button=True, container=True)
                generate_button.click(generate_outpainting, inputs=[prompt, negative_prompt, num_inference_steps, strength, guidance_scale, mode, image_input], outputs=[image_preview])
        
demo.launch()