#Importing required libraries
import os
import random
import numpy as np

import torch
import spaces
import gradio as gr

from diffusers import FluxTransformer2DModel, FluxInpaintPipeline
import google.generativeai as genai



MARKDOWN = """
# Prompt Canvas🎨
Thanks to [Black Forest Labs](https://huggingface.co/black-forest-labs) team for creating this amazing model,
and a big thanks to [Gothos](https://github.com/Gothos) for taking it to the next level by enabling inpainting with the FLUX.
"""

#Gemini Setup
genai.configure(api_key = os.environ['Gemini_API'])
gemini_flash = genai.GenerativeModel(model_name='gemini-1.5-flash-002')

def gemini_predict(prompt):
    system_message = f"""You are the best text analyser.
                         You have to analyse a user query and identify what the user wants to change, from a given user query.
        
                         Examples:
                             Query: Change Lipstick colour to blue
                             Response: Lips
        
                             Query: Add a nose stud
                             Response: Nose
        
                             Query: Add a wallpaper to the right wall
                             Response: Right wall
        
                             Query: Change the Sofa's colour to Purple
                             Response: Sofa
        
                        Your response should be in 1 or 2-3 words
                        Query : {prompt}
                        """
    response = gemini_flash.generate_content(system_message)
    return(str(response.text)[:-2])


MAX_SEED = np.iinfo(np.int32).max
DEVICE = "cuda" #if torch.cuda.is_available() else "cpu"

#Setting up Flux (Schnell) Inpainting
#inpaint_pipe = FluxInpaintPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE)

transformer_SchnellReal = FluxTransformer2DModel.from_single_file("https://huggingface.co/SG161222/RealFlux_1.0b_Schnell/blob/main/4%20-%20Schnell%20Transformer%20Version/RealFlux_1.0b_Schnell_Transformer.safetensors", torch_dtype=torch.bfloat16)
inpaint_pipe = FluxInpaintPipeline.from_pretrained(bfl_repo, transformer=transformer_SchnellReal, torch_dtype=dtype).to(DEVICE)

#Uncomment the following 4 lines, if you want LoRA Realism weights added to the pipeline
# inpaint_pipe.load_lora_weights('hugovntr/flux-schnell-realism', weight_name='schnell-realism_v2.3.safetensors', adapter_name="better")
# inpaint_pipe.set_adapters(["better"], adapter_weights=[2.6])
# inpaint_pipe.fuse_lora(adapter_name=["better"], lora_scale=1.0)
# inpaint_pipe.unload_lora_weights()

#torch.cuda.empty_cache()

@spaces.GPU()
def process(input_image_editor, mask_image, input_text, strength, seed, randomize_seed, num_inference_steps, guidance_scale=3.5, progress=gr.Progress(track_tqdm=True)):
    if not input_text:
        raise gr.Error("Please enter a text prompt.")
    item = gemini_predict(input_text)
    #print(item)
    
    image = input_image_editor['background']
    if not image:
        raise gr.Error("Please upload an image.")
    width, height = image.size

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator(device=DEVICE).manual_seed(seed)

    result = inpaint_pipe(prompt=input_text, image=image, mask_image=mask_image, width=width, height=height,
                          strength=strength, num_inference_steps=num_inference_steps, generator=generator,
                          guidance_scale=guidance_scale).images[0]

    
    return result, mask_image, seed, item

with gr.Blocks(theme=gr.themes.Ocean()) as demo:
    gr.Markdown(MARKDOWN)
    with gr.Row():
        with gr.Column(scale=1):
            input_image_component = gr.ImageEditor(
                label='Image',
                type='pil',
                sources=["upload", "webcam"],
                image_mode='RGB',
                layers=False,
                brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"))
            input_text_component = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            with gr.Accordion("Advanced Settings", open=False):
                strength_slider = gr.Slider(
                    minimum=0.0,
                    maximum=1.0,
                    value=0.7,
                    step=0.01,
                    label="Strength"
                )
                num_inference_steps = gr.Slider(
                    minimum=1,
                    maximum=100,
                    value=30,
                    step=1,
                    label="Number of inference steps"
                )
                guidance_scale = gr.Slider(
                    label="Guidance Scale",
                    minimum=1,
                    maximum=15,
                    step=0.1,
                    value=3.5,
                )
                seed_number = gr.Number(
                    label="Seed", 
                    value=42,
                    precision=0
                )
                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            with gr.Accordion("Upload a mask", open=False):
                uploaded_mask_component = gr.Image(label="Already made mask (black pixels will be preserved, white pixels will be redrawn)", sources=["upload"], type="pil")
            submit_button_component = gr.Button(value='Inpaint', variant='primary')
        with gr.Column(scale=1):
            output_image_component = gr.Image(type='pil', image_mode='RGB', label='Generated Image')
            output_mask_component = gr.Image(type='pil', image_mode='RGB', label='Generated Mask')
            with gr.Accordion("Debug Info", open=False):
                output_seed = gr.Number(label="Used Seed")
                identified_item = gr.Textbox(label="Gemini predicted item")

    submit_button_component.click(
        fn=process,
        inputs=[input_image_component, uploaded_mask_component, input_text_component, strength_slider, seed_number, randomize_seed, num_inference_steps, guidance_scale],
        outputs=[output_image_component, output_mask_component, output_seed, identified_item]
    )

demo.launch(debug=False, show_error=True)