app.py

import gradio as gr
from transformers import pipeline
from PIL import Image
import torch
from torchvision import transforms
import re

# MODELES
INGREDIENT_MODEL_ID = "stchakman/Fridge_Items_Model"
RECIPE_MODEL_ID     = "flax-community/t5-recipe-generation"

# PIPELINES
ingredient_classifier = pipeline(
    "image-classification",
    model=INGREDIENT_MODEL_ID,
    device=0 if torch.cuda.is_available() else -1,
    top_k=5
)
recipe_generator = pipeline(
    "text2text-generation",
    model=RECIPE_MODEL_ID,
    device=0 if torch.cuda.is_available() else -1,
)

# AUGMENTATION
augment = transforms.Compose([
    transforms.RandomHorizontalFlip(p=0.5),
    transforms.RandomRotation(10),
    transforms.ColorJitter(brightness=0.2, contrast=0.2),
])

def parse_recipe_text(raw: str):
    """
    Parse le texte brut retourne par le modele en JSON { ingredients: [...], steps: [...] }.
    """
    raw = raw.replace("ingredients: ingredients:", "ingredients:")
    # On cherche la section ingredients et directions
    ing_match = re.search(r"ingredients:\s*(.*?)\s*directions:", raw, re.IGNORECASE | re.DOTALL)
    dir_match = re.search(r"directions:\s*(.+)$", raw, re.IGNORECASE | re.DOTALL)

    # ingredients
    ingredients = []
    if ing_match:
        ing_str = ing_match.group(1).strip()
        parts = re.findall(r"(\d+\s+[^\d,\.]+(?: [^\d,\.]+)*)", ing_str)
        if parts:
            ingredients = [p.strip() for p in parts]
        else:
            ingredients = [i.strip() for i in ing_str.split(",") if i.strip()]

    # etapes
    steps = []
    if dir_match:
        steps_str = dir_match.group(1).strip()
        for s in re.split(r"\.\s*", steps_str):
            s = s.strip()
            if s:
                steps.append(s.endswith(".") and s or s + ".")

    return {"ingredients": ingredients, "steps": steps}

def generate_recipe_json(image: Image.Image):
    # Aug
    image_aug = augment(image)
    results = ingredient_classifier(image_aug)
    ingredients = [res["label"] for res in results]
    ingredient_str = ", ".join(ingredients)
    prompt = f"Ingredients: {ingredient_str}. Recipe:"
    raw = recipe_generator(prompt, max_new_tokens=256, do_sample=True)[0]["generated_text"]

    parsed = parse_recipe_text(raw)
    return {
        "detected_ingredients": ingredients,
        "recipe_text": raw,
        "ingredients_parsed": parsed["ingredients"],
        "steps": parsed["steps"]
    }

interface = gr.Interface(
    fn=generate_recipe_json,
    inputs=gr.Image(type="pil", label="📷 Image de vos ingredients"),
    outputs="json",
    title="🧑‍🍳 Generateur de Recettes (JSON) 🧑‍🍳",
    description="Depose une image d'ingredients, recupere directement un JSON structure avec ingredients et etapes.",
    allow_flagging="never"
)

if __name__ == "__main__":
    interface.launch(share=True)