app.py

import gradio as gr
import requests
from PIL import Image
from io import BytesIO
import base64
import os

# Hugging Face ControlNet API (Canny version)
HF_API = "https://api-inference.huggingface.co/models/lllyasviel/controlnet-sdxl-1.0-canny"
API_KEY = os.getenv("HF_API_KEY")  # Secure: fetch from secret

headers = {
    "Authorization": f"Bearer {API_KEY}"
}

def generate_image(prompt, image):
    buffered = BytesIO()
    image.save(buffered, format="JPEG")
    img_bytes = buffered.getvalue()

    payload = {
        "inputs": {
            "prompt": prompt,
            "image": base64.b64encode(img_bytes).decode("utf-8"),
            "negative_prompt": "blurry, deformed, cropped"
        },
        "options": {"wait_for_model": True}
    }

    response = requests.post(HF_API, headers=headers, json=payload)

    if response.status_code == 200:
        img_out = Image.open(BytesIO(response.content))
        return img_out
    else:
        return f"Error: {response.status_code} - {response.text}"

# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("# 🧠 NewCrux AI Demo: Product → Lifestyle Image")
    with gr.Row():
        input_image = gr.Image(type="pil", label="Upload Product Image")
        prompt_text = gr.Textbox(label="Enter Prompt", placeholder="e.g., A runner on a beach wearing this shoe")
    output_image = gr.Image(label="Generated Lifestyle Image")
    generate_btn = gr.Button("Generate Image")

    generate_btn.click(fn=generate_image, inputs=[prompt_text, input_image], outputs=output_image)

demo.launch()