Inital commit

app.py

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+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+import torch
+from torchvision import transforms
+
+INGREDIENT_MODEL_ID = "stchakman/Fridge_Items_Model"
+ingredient_classifier = pipeline(
+    "image-classification",
+    model=INGREDIENT_MODEL_ID,
+    device=0 if torch.cuda.is_available() else -1,
+    top_k=5 
+)
+
+RECIPE_MODEL_ID = "flax-community/t5-recipe-generation"
+recipe_generator = pipeline("text2text-generation", model=RECIPE_MODEL_ID)
+
+augment = transforms.Compose([
+    transforms.RandomHorizontalFlip(p=0.5),
+    transforms.RandomRotation(10),
+    transforms.ColorJitter(brightness=0.2, contrast=0.2),
+])
+
+def generate_recipe(image: Image.Image):
+    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:"
+    recipe = recipe_generator(prompt, max_length=300, do_sample=True)[0]["generated_text"]
+    
+    return f"### Ingredients detectes :\n{ingredient_str}\n\n### Recette generee :\n{recipe}"
+
+interface = gr.Interface(
+    fn=generate_recipe,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Markdown(),
+    title="🥕 Generateur de recettes 🧑‍🍳",
+    description="Envoyer une image d'ingredients pour recevoir une recette",
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

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+gradio
+transformers
+torch
+torchvision
+pillow