add json reponse

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
+TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh
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+emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc=
+-----END CERTIFICATE-----

app.py

@@ -3,10 +3,11 @@ 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"
+RECIPE_MODEL_ID     = "flax-community/t5-recipe-generation"
 
 # PIPELINES
 ingredient_classifier = pipeline(
@@ -15,11 +16,10 @@ ingredient_classifier = pipeline(
     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
+    device=0 if torch.cuda.is_available() else -1,
 )
 
 # AUGMENTATION
@@ -29,37 +29,61 @@ augment = transforms.Compose([
     transforms.ColorJitter(brightness=0.2, contrast=0.2),
 ])
 
-# FONCTION PRINCIPALE
-def generate_recipe(image: Image.Image):
-    # Affichage "Processing..." temporaire
-    yield "🔄 Traitement de l'image... Veuillez patienter."
+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)
 
-    # Augmentation
-    image_aug = augment(image)
+    # 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 + ".")
 
-    # Classification
+    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)
-
-    yield f"🥕 Ingrédients détectés : {ingredient_str}\n\n🍳 Génération de la recette..."
-
-    # Génération recette
     prompt = f"Ingredients: {ingredient_str}. Recipe:"
-    recipe = recipe_generator(prompt, max_new_tokens=256, do_sample=True)[0]["generated_text"]
+    raw = recipe_generator(prompt, max_new_tokens=256, do_sample=True)[0]["generated_text"]
 
-    # Affichage final
-    yield f"### 🥕 Ingrédients détectés :\n{ingredient_str}\n\n### 🍽️ Recette générée :\n{recipe}"
+    parsed = parse_recipe_text(raw)
+    return {
+        "detected_ingredients": ingredients,
+        "recipe_text": raw,
+        "ingredients_parsed": parsed["ingredients"],
+        "steps": parsed["steps"]
+    }
 
-# INTERFACE
 interface = gr.Interface(
-    fn=generate_recipe,
-    inputs=gr.Image(type="pil", label="📷 Image de vos ingrédients"),
-    outputs=gr.Markdown(),
-    title="🥕 Générateur de Recettes 🧑‍🍳",
-    description="Dépose une image d'ingrédients pour obtenir une recette automatiquement générée à partir d'un modèle IA.",
+    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)  # Mets share=True pour générer le lien Gradio public
+    interface.launch(share=True)

requirements.txt

@@ -3,3 +3,4 @@ transformers
 torch
 torchvision
 pillow
+re