Create app.py

app.py

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+import gradio as gr
+import requests
+from PIL import Image
+from io import BytesIO
+import base64
+
+# 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()