import gradio as gr
import torch
import os
import random
import numpy as np
from diffusers import DiffusionPipeline
from safetensors.torch import load_file
from spaces import GPU  # Remove if not in HF Space

# 1. Model and LoRA Loading (Before Gradio)
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
token = os.getenv("HF_TOKEN")
model_repo_id = "stabilityai/stable-diffusion-3.5-large"

try:
    pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype, use_auth_token=token) # No need to check for token existence, diffusers handles this
    pipe = pipe.to(device)

    lora_filename = "lora_trained_model.safetensors"  # EXACT filename of your LoRA
    lora_path = os.path.join("./", lora_filename)

    if os.path.exists(lora_path):
        lora_weights = load_file(lora_path)
        text_encoder = pipe.text_encoder
        text_encoder.load_state_dict(lora_weights, strict=False)
        print(f"LoRA loaded successfully from: {lora_path}")
    else:
        print(f"Error: LoRA file not found at: {lora_path}")
        exit()  # Stop if LoRA is not found

    print("Stable Diffusion model and LoRA loaded successfully!")

except Exception as e:
    print(f"Error loading model or LoRA: {e}")
    exit()


MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

@GPU(duration=65)  # Only if in HF Space
def infer(
    prompt,
    negative_prompt="",
    seed=42,
    randomize_seed=False,
    width=1024,
    height=1024,
    guidance_scale=4.5,
    num_inference_steps=40,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)

    try:
        image = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            width=width,
            height=height,
            generator=generator,
        ).images[0]
        return image, seed
    except Exception as e:
        print(f"Error during image generation: {e}") # Print error for debugging
        return f"Error: {e}", seed # Return error to Gradio interface

# ... (rest of your Gradio code - examples, CSS, etc. - same as before)

# 4. Image generation function (now decorated)
@GPU(duration=65)  # Only if in HF Space
def generate_image(prompt):
    global pipeline
    if pipeline is None:
        print("Error: Pipeline is None (model not loaded)")  # Log this specifically
        return "Error: Model not loaded!"

    try:
        print("Starting image generation...")  # Log before the image generation
        image = pipeline(prompt).images[0]
        print("Image generated successfully!")
        return image
    except Exception as e:
        error_message = f"Error during image generation: {type(e).__name__}: {e}"  # Include exception type
        print(f"Full Error Details:\n{error_message}")  # Print full details
        return error_message  # Return error message to Gradio
    except RuntimeError as re:
        error_message = f"Runtime Error during image generation: {type(re).__name__}: {re}"  # Include exception type
        print(f"Full Runtime Error Details:\n{error_message}")  # Print full details
        return error_message  # Return error message to Gradio

# 5. Gradio interface
with gr.Blocks() as demo:
    prompt_input = gr.Textbox(label="Prompt")
    image_output = gr.Image(label="Generated Image")
    generate_button = gr.Button("Generate")

    generate_button.click(
        fn=generate_image,
        inputs=prompt_input,
        outputs=image_output,
    )

demo.launch()