app.py

import gradio as gr
import requests
from PIL import Image
from io import BytesIO
import os

# Hugging Face Inference API for image generation
HF_TOKEN = os.getenv('HF_TOKEN')
API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2"

def generate_image(prompt):
    """Generate image from text using Hugging Face API"""
    if not prompt or prompt.strip() == "":
        return None, "Please enter a prompt!"
    
    try:
        headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
        payload = {"inputs": prompt}
        response = requests.post(API_URL, headers=headers, json=payload)
        
        if response.status_code == 200:
            image = Image.open(BytesIO(response.content))
            return image, "✅ Image generated successfully!"
        else:
            return None, f"❌ API Error"
    except Exception as e:
        return None, f"❌ Error: {str(e)}"

# Create interface
with gr.Blocks(title="AI Image Generator") as demo:
    gr.Markdown("# 🎨 AI Image Generator")
    gr.Markdown("Generate images from text descriptions")
    
    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(
                label="Enter prompt",
                placeholder="A beautiful sunset over mountains",
                lines=3
            )
            btn = gr.Button("Generate", variant="primary", size="lg")
            status = gr.Textbox(label="Status", interactive=False)
        
        with gr.Column():
            output = gr.Image(label="Generated Image", type="pil")
    
    btn.click(fn=generate_image, inputs=prompt, outputs=[output, status])

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