image.py

import gradio as gr
import torch
from diffusers import AutoPipelineForText2Image
import time
import numpy as np # The only library we need for this fix

# --- 1. Load the SD-Turbo Model (Optimized for CPU) ---
# (No changes here)
print("Loading the SD-Turbo model for CPU...")
pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sd-turbo")
device = "cpu"
pipe = pipe.to(device)
print("Model loaded successfully on CPU!")

# --- 2. Pre-warm the Model ---
# (No changes here)
print("Pre-warming the pipeline...")
_ = pipe(prompt="A photo of a cat", width=512, height=512, num_inference_steps=1).images[0]
print("Pipeline is warmed up and ready!")


# --- 3. The NumPy Array Solution ---
def generate_and_return_numpy(prompt, seed, width, height):
    """
    Generates an image and returns it as a raw NumPy array. This is the most
    stable method to avoid Gradio/Windows bugs, though a UI delay will exist.
    """
    start_time = time.time()
    try:
        width = int(width)
        height = int(height)
        generator = torch.Generator(device=pipe.device).manual_seed(int(seed))

        # The model generates the PIL Image
        pil_image = pipe(
            prompt=prompt,
            width=width,
            height=height,
            num_inference_steps=4,
            guidance_scale=0.0,
            generator=generator,
        ).images[0]
        
        # --- THE FIX: Convert to NumPy Array ---
        numpy_array = np.array(pil_image)
        # --- END OF FIX ---

        end_time = time.time()
        # This time will be the FAST backend time. The UI will take longer.
        generation_time = f"Backend generation time: {end_time - start_time:.2f} seconds"
        
        # We return the raw array. Gradio will handle the slow encoding now.
        return numpy_array, generation_time, None

    except Exception as e:
        print(f"An error occurred: {e}")
        return None, "Generation failed", str(e)


# --- 4. Create the Gradio Interface ---
# The UI code is identical. gr.Image can handle NumPy arrays.
with gr.Blocks(theme=gr.themes.Soft()) as app:
    gr.Markdown(
        """
        # 💯 Stable CPU Generator (NumPy Version) 💯
        ### This is the most robust version to prevent crashes on Windows.
        """
    )
    with gr.Row():
        with gr.Column(scale=3):
            prompt_input = gr.Textbox(
                label="Prompt", placeholder="A wizard casting a spell", lines=3,
            )
            with gr.Row():
                width_slider = gr.Slider(
                    label="Width", minimum=256, maximum=768, value=512, step=64,
                )
                height_slider = gr.Slider(
                    label="Height", minimum=256, maximum=768, value=512, step=64,
                )
            seed_input = gr.Number(label="Seed", value=100)
            generate_button = gr.Button("Generate", variant="primary")
        with gr.Column(scale=2):
            image_output = gr.Image(label="Generated Image", show_label=False)
            info_output = gr.Textbox(label="Status", show_label=False, interactive=False)
            error_output = gr.Textbox(label="Error", visible=False)

    generate_button.click(
        fn=generate_and_return_numpy,
        inputs=[prompt_input, seed_input, width_slider, height_slider],
        outputs=[image_output, info_output, error_output],
    )

# --- 5. Launch the App ---
app.launch(share=True)