app.py

import gradio as gr
import torch
from diffusers import StableDiffusionPipeline
import os

# Set the model ID
MODEL_ID = "runwayml/stable-diffusion-v1-5"

# Detect hardware
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# Use float16 for faster, lighter inference if on GPU
TORCH_DTYPE = torch.float16 if DEVICE == "cuda" else torch.float32

# Load the pipeline
print(f"Loading model on {DEVICE}...")
pipe = StableDiffusionPipeline.from_pretrained(
    MODEL_ID, 
    torch_dtype=TORCH_DTYPE,
    use_safetensors=True
)
pipe = pipe.to(DEVICE)

def generate_image(prompt, negative_prompt):
    if not prompt:
        return None
    
    try:
        # Generate the image
        image = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            num_inference_steps=30,
            guidance_scale=7.5
        ).images[0]
        return image
    except Exception as e:
        print(f"Error: {e}")
        return None

# Build the UI
with gr.Blocks(theme=gr.themes.Base()) as demo:
    gr.Markdown("# ✨ AI Image Generator")
    gr.Markdown("Enter a prompt and click generate to create an image.")
    
    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(label="Prompt", placeholder="A majestic lion in the jungle...")
            neg_prompt = gr.Textbox(label="Negative Prompt", placeholder="blurry, low quality, distorted")
            btn = gr.Button("Generate", variant="primary")
        
        with gr.Column():
            output = gr.Image(label="Result")

    btn.click(fn=generate_image, inputs=[prompt, neg_prompt], outputs=output)

if __name__ == "__main__":
    demo.launch()