app.py

import gradio as gr
import torch
from diffusers import StableDiffusionPipeline

# Initialize with basic model
model_id = "CompVis/stable-diffusion-v1-4"
device = "cuda" if torch.cuda.is_available() else "cpu"

try:
    pipe = StableDiffusionPipeline.from_pretrained(
        model_id,
        torch_dtype=torch.float16 if device == "cuda" else torch.float32,
        use_auth_token=False
    ).to(device)
    print("✓ Model loaded successfully")
except Exception as e:
    raise RuntimeError(f"Model loading failed: {str(e)}")

def generate(prompt):
    try:
        if len(prompt) < 3:
            return None, "Prompt too short"
        image = pipe(prompt).images[0]
        return image, "Success"
    except Exception as e:
        return None, f"Error: {str(e)}"

# Minimal UI
with gr.Blocks() as demo:
    gr.Markdown("## 🖼️ Stable Image Generator")
    with gr.Row():
        prompt = gr.Textbox(label="Enter prompt", placeholder="A cute cat astronaut...")
        generate_btn = gr.Button("Generate", variant="primary")
    output = gr.Image(label="Result")
    status = gr.Textbox(label="Status", interactive=False)
    
    generate_btn.click(
        generate,
        inputs=prompt,
        outputs=[output, status]
    )

demo.launch(server_name="0.0.0.0")