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| import os | |
| import io | |
| import tempfile | |
| import gradio as gr | |
| import requests | |
| from PIL import Image | |
| import fal_client | |
| APP_NAME = os.getenv("APP_NAME", "Scenora") | |
| MODEL_ID = os.getenv("MODEL_ID", "fal-ai/flux-pro/kontext") | |
| APP_PASSWORD = os.getenv("APP_PASSWORD") | |
| PROMPT_SUFFIX = ( | |
| "commercial product photography, natural light, sharp focus on the product, " | |
| "realistic shadows and reflections, 24mm lens" | |
| ) | |
| PRESETS = { | |
| "Studio neutre": "on a seamless light grey studio backdrop, soft diffused lighting", | |
| "Table en bois": "placed on a warm oak table, blurred kitchen in the background, morning light", | |
| "Beton mineral": "on a raw concrete surface, minimalist industrial setting, side light", | |
| "Exterieur nature": "on a mossy stone outdoors, soft green foliage behind, golden hour", | |
| "Marbre luxe": "on a white marble slab, elegant premium setting, soft reflections", | |
| } | |
| NEGATIVE_DEFAULT = "text, watermark, logo, people, clutter, distorted proportions" | |
| def _image_to_url(image: Image.Image) -> str: | |
| """Envoie l'image sur le stockage temporaire de fal et renvoie son URL.""" | |
| with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp: | |
| path = tmp.name | |
| image.convert("RGB").save(path, format="PNG") | |
| try: | |
| return fal_client.upload_file(path) | |
| finally: | |
| try: | |
| os.unlink(path) | |
| except OSError: | |
| pass | |
| def _download(url: str) -> Image.Image: | |
| resp = requests.get(url, timeout=120) | |
| resp.raise_for_status() | |
| return Image.open(io.BytesIO(resp.content)).convert("RGB") | |
| def generate(image, preset, prompt, negative, variations, progress=gr.Progress()): | |
| if not os.getenv("FAL_KEY"): | |
| raise gr.Error( | |
| "Cle API absente. Ajoutez FAL_KEY dans les Secrets du Space, " | |
| "puis redemarrez-le." | |
| ) | |
| if image is None: | |
| raise gr.Error("Chargez d'abord une photo produit.") | |
| scene = prompt.strip() or PRESETS.get(preset, "") | |
| if not scene: | |
| raise gr.Error("Decrivez la scene souhaitee ou choisissez un preset.") | |
| instruction = ( | |
| f"Replace the background only. Keep the product exactly as it is: " | |
| f"same shape, same colors, same proportions, same details. " | |
| f"New background: {scene}. Style: {PROMPT_SUFFIX}." | |
| ) | |
| if negative.strip(): | |
| instruction += f" Avoid: {negative.strip()}." | |
| progress(0, desc="Envoi de l'image...") | |
| image_url = _image_to_url(image) | |
| results = [] | |
| n = int(variations) | |
| for i in range(n): | |
| progress((i + 0.2) / n, desc=f"Generation {i + 1}/{n}...") | |
| try: | |
| out = fal_client.subscribe( | |
| MODEL_ID, | |
| arguments={"prompt": instruction, "image_url": image_url}, | |
| ) | |
| except Exception as exc: | |
| raise gr.Error(f"Erreur API ({MODEL_ID}) : {exc}") | |
| images = out.get("images") or [] | |
| if not images: | |
| raise gr.Error(f"Reponse vide du modele : {out}") | |
| progress((i + 0.8) / n, desc=f"Recuperation {i + 1}/{n}...") | |
| results.append(_download(images[0]["url"])) | |
| return results, instruction | |
| CSS = """ | |
| html, body, .gradio-container { | |
| width: 100% !important; | |
| max-width: 100% !important; | |
| } | |
| .gradio-container { | |
| margin: 0 !important; | |
| padding: 1rem 1.5rem 2rem !important; | |
| } | |
| .main, .wrap, .contain, .app { | |
| max-width: 100% !important; | |
| width: 100% !important; | |
| } | |
| footer {display: none !important;} | |
| """ | |
| with gr.Blocks(css=CSS, title=APP_NAME, fill_width=True, fill_height=True) as demo: | |
| gr.Markdown(f"# {APP_NAME}") | |
| gr.Markdown( | |
| "Chargez une photo produit, choisissez ou decrivez le decor, generez. " | |
| "Le produit est preserve, seul l'arriere-plan change." | |
| ) | |
| with gr.Row(equal_height=True): | |
| with gr.Column(scale=1, min_width=320): | |
| image_in = gr.Image(label="Photo produit", type="pil", height=420) | |
| preset = gr.Radio( | |
| label="Preset de decor", | |
| choices=list(PRESETS.keys()), | |
| value="Studio neutre", | |
| ) | |
| prompt = gr.Textbox( | |
| label="Decor sur mesure (prioritaire sur le preset)", | |
| placeholder="sur un plan de travail en marbre, cuisine lumineuse en arriere-plan", | |
| lines=2, | |
| ) | |
| negative = gr.Textbox( | |
| label="A eviter", | |
| value=NEGATIVE_DEFAULT, | |
| lines=2, | |
| ) | |
| variations = gr.Slider( | |
| label="Nombre de variantes", | |
| minimum=1, maximum=4, step=1, value=2, | |
| ) | |
| run = gr.Button("Generer", variant="primary") | |
| with gr.Column(scale=1, min_width=320): | |
| gallery = gr.Gallery( | |
| label="Resultats", columns=2, height=520, | |
| object_fit="contain", show_download_button=True, | |
| ) | |
| debug = gr.Textbox(label="Instruction envoyee au modele", lines=4) | |
| run.click( | |
| fn=generate, | |
| inputs=[image_in, preset, prompt, negative, variations], | |
| outputs=[gallery, debug], | |
| ) | |
| gr.Markdown(f"Modele : `{MODEL_ID}` — facture a l'image par le fournisseur.") | |
| if __name__ == "__main__": | |
| auth = ("admin", APP_PASSWORD) if APP_PASSWORD else None | |
| demo.queue(max_size=8).launch( | |
| server_name="0.0.0.0", | |
| server_port=int(os.getenv("PORT", 7860)), | |
| auth=auth, | |
| show_api=False, | |
| ) | |