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+# Verwende ein offizielles Python-Image als Basis
+FROM python:3.10-slim-bullseye
+
+# Setze das Arbeitsverzeichnis im Container
+WORKDIR /app
+
+# Kopiere die requirements.txt in das Arbeitsverzeichnis
+COPY requirements.txt .
+
+# Installiere die Python-Abhängigkeiten
+# --no-cache-dir: keine Pip-Cache-Dateien schreiben
+# --upgrade: installierte Pakete aktualisieren
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Kopiere den Rest deiner Anwendungsdateien (app.py, etc.) in das Arbeitsverzeichnis
+COPY . .
+
+# Exponiere den Port, auf dem Uvicorn lauschen wird
+# Dies ist der Standard-Port für Hugging Face Spaces
+EXPOSE 7860
+
+# Starte die FastAPI-Anwendung mit Uvicorn
+# 'app:app' bedeutet: Finde die Variable 'app' im Modul 'app.py'
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from transformers import FlaxAutoModelForSeq2SeqLM, AutoTokenizer, AutoModel
+import torch
+import numpy as np
+import random
+import json
+from fastapi import FastAPI
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
+
+# Lade RecipeBERT Modell
+bert_model_name = "alexdseo/RecipeBERT"
+bert_tokenizer = AutoTokenizer.from_pretrained(bert_model_name)
+bert_model = AutoModel.from_pretrained(bert_model_name)
+bert_model.eval()
+
+# Lade T5 Rezeptgenerierungsmodell
+MODEL_NAME_OR_PATH = "flax-community/t5-recipe-generation"
+t5_tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME_OR_PATH, use_fast=True)
+t5_model = FlaxAutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME_OR_PATH)
+
+# Token Mapping
+special_tokens = t5_tokenizer.all_special_tokens
+tokens_map = {
+    "<sep>": "--",
+    "<section>": "\n"
+}
+
+# --- Deine Helper-Funktionen (get_embedding, average_embedding, get_cosine_similarity, etc.) ---
+# Kopiere alle diese Funktionen von deinem aktuellen app.py hierher.
+# Ich kürze sie hier aus Platzgründen, aber sie müssen vollständig eingefügt werden.
+
+def get_embedding(text):
+    inputs = bert_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = bert_model(**inputs)
+    attention_mask = inputs['attention_mask']
+    token_embeddings = outputs.last_hidden_state
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
+    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+    return (sum_embeddings / sum_mask).squeeze(0)
+
+def average_embedding(embedding_list):
+    tensors = torch.stack([emb for _, emb in embedding_list])
+    return tensors.mean(dim=0)
+
+def get_cosine_similarity(vec1, vec2):
+    if torch.is_tensor(vec1): vec1 = vec1.detach().numpy()
+    if torch.is_tensor(vec2): vec2 = vec2.detach().numpy()
+    vec1 = vec1.flatten()
+    vec2 = vec2.flatten()
+    dot_product = np.dot(vec1, vec2)
+    norm_a = np.linalg.norm(vec1)
+    norm_b = np.linalg.norm(vec2)
+    if norm_a == 0 or norm_b == 0: return 0
+    return dot_product / (norm_a * norm_b)
+
+def get_combined_scores(query_vector, embedding_list, all_good_embeddings, avg_weight=0.6):
+    results = []
+    for name, emb in embedding_list:
+        avg_similarity = get_cosine_similarity(query_vector, emb)
+        individual_similarities = [get_cosine_similarity(good_emb, emb) for _, good_emb in all_good_embeddings]
+        avg_individual_similarity = sum(individual_similarities) / len(individual_similarities)
+        combined_score = avg_weight * avg_similarity + (1 - avg_weight) * avg_individual_similarity
+        results.append((name, emb, combined_score))
+    results.sort(key=lambda x: x[2], reverse=True)
+    return results
+
+def find_best_ingredients(required_ingredients, available_ingredients, max_ingredients=6, avg_weight=0.6):
+    required_ingredients = list(set(required_ingredients))
+    available_ingredients = list(set([i for i in available_ingredients if i not in required_ingredients]))
+    if not required_ingredients and available_ingredients:
+        random_ingredient = random.choice(available_ingredients)
+        required_ingredients = [random_ingredient]
+        available_ingredients = [i for i in available_ingredients if i != random_ingredient]
+    if not required_ingredients or len(required_ingredients) >= max_ingredients:
+        return required_ingredients[:max_ingredients]
+    if not available_ingredients:
+        return required_ingredients
+    embed_required = [(e, get_embedding(e)) for e in required_ingredients]
+    embed_available = [(e, get_embedding(e)) for e in available_ingredients]
+    num_to_add = min(max_ingredients - len(required_ingredients), len(available_ingredients))
+    final_ingredients = embed_required.copy()
+    for _ in range(num_to_add):
+        avg = average_embedding(final_ingredients)
+        candidates = get_combined_scores(avg, embed_available, final_ingredients, avg_weight)
+        if not candidates: break
+        best_name, best_embedding, _ = candidates[0]
+        final_ingredients.append((best_name, best_embedding))
+        embed_available = [item for item in embed_available if item[0] != best_name]
+    return [name for name, _ in final_ingredients]
+
+def skip_special_tokens(text, special_tokens):
+    for token in special_tokens: text = text.replace(token, "")
+    return text
+
+def target_postprocessing(texts, special_tokens):
+    if not isinstance(texts, list): texts = [texts]
+    new_texts = []
+    for text in texts:
+        text = skip_special_tokens(text, special_tokens)
+        for k, v in tokens_map.items(): text = text.replace(k, v)
+        new_texts.append(text)
+    return new_texts
+
+def validate_recipe_ingredients(recipe_ingredients, expected_ingredients, tolerance=0):
+    recipe_count = len([ing for ing in recipe_ingredients if ing and ing.strip()])
+    expected_count = len(expected_ingredients)
+    return abs(recipe_count - expected_count) == tolerance
+
+def generate_recipe_with_t5(ingredients_list, max_retries=5):
+    original_ingredients = ingredients_list.copy()
+    for attempt in range(max_retries):
+        try:
+            if attempt > 0:
+                current_ingredients = original_ingredients.copy()
+                random.shuffle(current_ingredients)
+            else:
+                current_ingredients = ingredients_list
+            ingredients_string = ", ".join(current_ingredients)
+            prefix = "items: "
+            generation_kwargs = {
+                "max_length": 512, "min_length": 64, "do_sample": True,
+                "top_k": 60, "top_p": 0.95
+            }
+            inputs = t5_tokenizer(
+                prefix + ingredients_string, max_length=256, padding="max_length",
+                truncation=True, return_tensors="jax"
+            )
+            output_ids = t5_model.generate(
+                input_ids=inputs.input_ids, attention_mask=inputs.attention_mask, **generation_kwargs
+            )
+            generated = output_ids.sequences
+            generated_text = target_postprocessing(t5_tokenizer.batch_decode(generated, skip_special_tokens=False), special_tokens)[0]
+            recipe = {}
+            sections = generated_text.split("\n")
+            for section in sections:
+                section = section.strip()
+                if section.startswith("title:"):
+                    recipe["title"] = section.replace("title:", "").strip().capitalize()
+                elif section.startswith("ingredients:"):
+                    ingredients_text = section.replace("ingredients:", "").strip()
+                    recipe["ingredients"] = [item.strip().capitalize() for item in ingredients_text.split("--") if item.strip()]
+                elif section.startswith("directions:"):
+                    directions_text = section.replace("directions:", "").strip()
+                    recipe["directions"] = [step.strip().capitalize() for step in directions_text.split("--") if step.strip()]
+            if "title" not in recipe:
+                recipe["title"] = f"Rezept mit {', '.join(current_ingredients[:3])}"
+            if "ingredients" not in recipe:
+                recipe["ingredients"] = current_ingredients
+            if "directions" not in recipe:
+                recipe["directions"] = ["Keine Anweisungen generiert"]
+            if validate_recipe_ingredients(recipe["ingredients"], original_ingredients):
+                return recipe
+            else:
+                if attempt == max_retries - 1: return recipe
+        except Exception as e:
+            if attempt == max_retries - 1:
+                return {
+                    "title": f"Rezept mit {original_ingredients[0] if original_ingredients else 'Zutaten'}",
+                    "ingredients": original_ingredients,
+                    "directions": ["Fehler beim Generieren der Rezeptanweisungen"]
+                }
+    return {
+        "title": f"Rezept mit {original_ingredients[0] if original_ingredients else 'Zutaten'}",
+        "ingredients": original_ingredients,
+        "directions": ["Fehler beim Generieren der Rezeptanweisungen"]
+    }
+
+# Kernlogik, die von der FastAPI-Route aufgerufen wird
+def process_recipe_request_logic(required_ingredients, available_ingredients, max_ingredients, max_retries):
+    """
+    Kernlogik zur Verarbeitung einer Rezeptgenerierungsanfrage.
+    """
+    if not required_ingredients and not available_ingredients:
+        return {"error": "Keine Zutaten angegeben"}
+    try:
+        optimized_ingredients = find_best_ingredients(
+            required_ingredients, available_ingredients, max_ingredients
+        )
+        recipe = generate_recipe_with_t5(optimized_ingredients, max_retries)
+        result = {
+            'title': recipe['title'],
+            'ingredients': recipe['ingredients'],
+            'directions': recipe['directions'],
+            'used_ingredients': optimized_ingredients
+        }
+        return result
+    except Exception as e:
+        return {"error": f"Fehler bei der Rezeptgenerierung: {str(e)}"}
+
+# --- FastAPI-Implementierung ---
+app = FastAPI() # Deine FastAPI-Instanz
+
+class RecipeRequest(BaseModel):
+    required_ingredients: list[str] = []
+    available_ingredients: list[str] = []
+    max_ingredients: int = 7
+    max_retries: int = 5
+    # Abwärtskompatibilität: Falls 'ingredients' als Top-Level-Feld gesendet wird
+    ingredients: list[str] = []
+
+@app.post("/generate_recipe") # Der API-Endpunkt für Flutter
+async def generate_recipe_api(request_data: RecipeRequest):
+    """
+    Standard-REST-API-Endpunkt für die Flutter-App.
+    Nimmt direkt JSON-Daten an und gibt direkt JSON zurück.
+    """
+    final_required_ingredients = request_data.required_ingredients
+    if not final_required_ingredients and request_data.ingredients:
+        final_required_ingredients = request_data.ingredients
+
+    result_dict = process_recipe_request_logic(
+        final_required_ingredients,
+        request_data.available_ingredients,
+        request_data.max_ingredients,
+        request_data.max_retries
+    )
+    return JSONResponse(content=result_dict)
+
+print("INFO: FastAPI application script finished execution and defined 'app' variable.")

requirements.txt

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+transformers
+torch
+numpy
+jax
+jaxlib
+flax
+fastapi
+uvicorn
+pydantic