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app.py

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+from flask import Flask, request, jsonify
+from transformers import FlaxAutoModelForSeq2SeqLM, AutoTokenizer
+from transformers import AutoModel
+import torch
+import numpy as np
+import random
+from flask_cors import CORS
+
+app = Flask(__name__)
+CORS(app)
+
+# Load RecipeBERT model (for semantic ingredient combination)
+bert_model_name = "alexdseo/RecipeBERT"
+bert_tokenizer = AutoTokenizer.from_pretrained(bert_model_name)
+bert_model = AutoModel.from_pretrained(bert_model_name)
+bert_model.eval()
+
+# Load T5 recipe generation model
+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 for T5 model output processing
+special_tokens = t5_tokenizer.all_special_tokens
+tokens_map = {
+    "<sep>": "--",
+    "<section>": "\n"
+}
+
+
+def get_embedding(text):
+    """Computes embedding for a text with Mean Pooling over all tokens"""
+    inputs = bert_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = bert_model(**inputs)
+
+    # Mean Pooling - take average of all token embeddings
+    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):
+    """Computes the average of a list of embeddings"""
+    tensors = torch.stack([emb for _, emb in embedding_list])
+    return tensors.mean(dim=0)
+
+
+def get_cosine_similarity(vec1, vec2):
+    """Computes the cosine similarity between two vectors"""
+    if torch.is_tensor(vec1):
+        vec1 = vec1.detach().numpy()
+    if torch.is_tensor(vec2):
+        vec2 = vec2.detach().numpy()
+
+    # Make sure vectors have the right shape (flatten if necessary)
+    vec1 = vec1.flatten()
+    vec2 = vec2.flatten()
+
+    dot_product = np.dot(vec1, vec2)
+    norm_a = np.linalg.norm(vec1)
+    norm_b = np.linalg.norm(vec2)
+
+    # Avoid division by zero
+    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):
+    """Computes combined score considering both similarity to average and individual ingredients"""
+    results = []
+
+    for name, emb in embedding_list:
+        # Similarity to average vector
+        avg_similarity = get_cosine_similarity(query_vector, emb)
+
+        # Average similarity to individual ingredients
+        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 (weighted average)
+        combined_score = avg_weight * avg_similarity + (1 - avg_weight) * avg_individual_similarity
+
+        results.append((name, emb, combined_score))
+
+    # Sort by combined score (descending)
+    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):
+    """
+    Finds the best ingredients based on RecipeBERT embeddings.
+
+    Args:
+        required_ingredients (list): Required ingredients that must be used
+        available_ingredients (list): Available ingredients to choose from
+        max_ingredients (int): Maximum number of ingredients for the recipe
+        avg_weight (float): Weight for average vector
+
+    Returns:
+        list: The optimal combination of ingredients
+    """
+    # Ensure no duplicates in lists
+    required_ingredients = list(set(required_ingredients))
+    available_ingredients = list(set([i for i in available_ingredients if i not in required_ingredients]))
+
+    # Special case: If no required ingredients, randomly select one from available ingredients
+    if not required_ingredients and available_ingredients:
+        # Randomly select 1 ingredient as starting point
+        random_ingredient = random.choice(available_ingredients)
+        required_ingredients = [random_ingredient]
+        available_ingredients = [i for i in available_ingredients if i != random_ingredient]
+        print(f"No required ingredients provided. Randomly selected: {random_ingredient}")
+
+    # If still no ingredients or already at max capacity
+    if not required_ingredients or len(required_ingredients) >= max_ingredients:
+        return required_ingredients[:max_ingredients]
+
+    # If no additional ingredients available
+    if not available_ingredients:
+        return required_ingredients
+
+    # Calculate embeddings for all ingredients
+    embed_required = [(e, get_embedding(e)) for e in required_ingredients]
+    embed_available = [(e, get_embedding(e)) for e in available_ingredients]
+
+    # Number of ingredients to add
+    num_to_add = min(max_ingredients - len(required_ingredients), len(available_ingredients))
+
+    # Copy required ingredients to final list
+    final_ingredients = embed_required.copy()
+
+    # Add best ingredients
+    for _ in range(num_to_add):
+        # Calculate average vector of current combination
+        avg = average_embedding(final_ingredients)
+
+        # Calculate combined scores for all candidates
+        candidates = get_combined_scores(avg, embed_available, final_ingredients, avg_weight)
+
+        # If no candidates left, break
+        if not candidates:
+            break
+
+        # Choose best ingredient
+        best_name, best_embedding, _ = candidates[0]
+
+        # Add best ingredient to final list
+        final_ingredients.append((best_name, best_embedding))
+
+        # Remove ingredient from available ingredients
+        embed_available = [item for item in embed_available if item[0] != best_name]
+
+    # Extract only ingredient names
+    return [name for name, _ in final_ingredients]
+
+
+def skip_special_tokens(text, special_tokens):
+    """Removes special tokens from text"""
+    for token in special_tokens:
+        text = text.replace(token, "")
+    return text
+
+
+def target_postprocessing(texts, special_tokens):
+    """Post-processes generated text"""
+    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):
+    """
+    Validates if the recipe contains approximately the expected ingredients.
+
+    Args:
+        recipe_ingredients (list): Ingredients from generated recipe
+        expected_ingredients (list): Expected ingredients
+        tolerance (int): Allowed difference in ingredient count
+
+    Returns:
+        bool: True if recipe is valid, False otherwise
+    """
+    # Count non-empty ingredients
+    recipe_count = len([ing for ing in recipe_ingredients if ing and ing.strip()])
+    expected_count = len(expected_ingredients)
+
+    # Check if ingredient count is within tolerance
+    return abs(recipe_count - expected_count) == tolerance
+
+
+def generate_recipe_with_t5(ingredients_list, max_retries=5):
+    """
+    Generates a recipe using the T5 recipe generation model with validation.
+
+    Args:
+        ingredients_list (list): List of ingredients
+        max_retries (int): Maximum number of retry attempts
+
+    Returns:
+        dict: A dictionary with title, ingredients, and directions
+    """
+    original_ingredients = ingredients_list.copy()
+
+    for attempt in range(max_retries):
+        try:
+            # For retries after the first attempt, shuffle the ingredients
+            if attempt > 0:
+                current_ingredients = original_ingredients.copy()
+                random.shuffle(current_ingredients)
+                print(f"Retry {attempt}: Shuffling ingredients order")
+            else:
+                current_ingredients = ingredients_list
+
+            # Format ingredients as a comma-separated string
+            ingredients_string = ", ".join(current_ingredients)
+            prefix = "items: "
+
+            # Generation settings
+            generation_kwargs = {
+                "max_length": 512,
+                "min_length": 64,
+                "do_sample": True,
+                "top_k": 60,
+                "top_p": 0.95
+            }
+            print(f"Attempt {attempt + 1}: {prefix + ingredients_string}")
+
+            # Tokenize input
+            inputs = t5_tokenizer(
+                prefix + ingredients_string,
+                max_length=256,
+                padding="max_length",
+                truncation=True,
+                return_tensors="jax"
+            )
+
+            # Generate text
+            output_ids = t5_model.generate(
+                input_ids=inputs.input_ids,
+                attention_mask=inputs.attention_mask,
+                **generation_kwargs
+            )
+
+            # Decode and post-process
+            generated = output_ids.sequences
+            generated_text = target_postprocessing(
+                t5_tokenizer.batch_decode(generated, skip_special_tokens=False),
+                special_tokens
+            )[0]
+
+            # Parse sections
+            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 is missing, create one
+            if "title" not in recipe:
+                recipe["title"] = f"Recipe with {', '.join(current_ingredients[:3])}"
+
+            # Ensure all sections exist
+            if "ingredients" not in recipe:
+                recipe["ingredients"] = current_ingredients
+            if "directions" not in recipe:
+                recipe["directions"] = ["No directions generated"]
+
+            # Validate the recipe
+            if validate_recipe_ingredients(recipe["ingredients"], original_ingredients):
+                print(f"Success on attempt {attempt + 1}: Recipe has correct number of ingredients")
+                return recipe
+            else:
+                print(
+                    f"Attempt {attempt + 1} failed: Expected {len(original_ingredients)} ingredients, got {len(recipe['ingredients'])}")
+                if attempt == max_retries - 1:
+                    print("Max retries reached, returning last generated recipe")
+                    return recipe
+
+        except Exception as e:
+            print(f"Error in recipe generation attempt {attempt + 1}: {str(e)}")
+            if attempt == max_retries - 1:
+                return {
+                    "title": f"Recipe with {original_ingredients[0] if original_ingredients else 'ingredients'}",
+                    "ingredients": original_ingredients,
+                    "directions": ["Error generating recipe instructions"]
+                }
+
+    # Fallback (should not be reached)
+    return {
+        "title": f"Recipe with {original_ingredients[0] if original_ingredients else 'ingredients'}",
+        "ingredients": original_ingredients,
+        "directions": ["Error generating recipe instructions"]
+    }
+
+
+@app.route('/generate_recipe', methods=['POST'])
+def handle_recipe_request():
+    """
+    Processes a recipe generation request with a given list of ingredients.
+    Uses the intelligent ingredient combination feature.
+    """
+    if not request.is_json:
+        return jsonify({"error": "Request must be JSON"}), 415
+
+    data = request.get_json()
+
+    # Extract required and available ingredients from request
+    required_ingredients = data.get('required_ingredients', [])
+    available_ingredients = data.get('available_ingredients', [])
+
+    # For backward compatibility: If only 'ingredients' is specified, treat as required ingredients
+    if data.get('ingredients') and not required_ingredients:
+        required_ingredients = data.get('ingredients', [])
+
+    # Maximum number of ingredients (for better recipes)
+    max_ingredients = data.get('max_ingredients', 7)
+
+    # Maximum retries for recipe generation
+    max_retries = data.get('max_retries', 5)
+
+    # If no ingredients specified
+    if not required_ingredients and not available_ingredients:
+        return jsonify({"error": "No ingredients provided"}), 400
+
+    try:
+        # Always find best ingredient combination with RecipeBERT
+        optimized_ingredients = find_best_ingredients(
+            required_ingredients,
+            available_ingredients,
+            max_ingredients
+        )
+
+        # Generate recipe with optimized ingredients using T5 model with validation
+        recipe = generate_recipe_with_t5(optimized_ingredients, max_retries)
+
+        # Format for Flutter app consumption - structured format
+        return jsonify({
+            'title': recipe['title'],
+            'ingredients': recipe['ingredients'],
+            'directions': recipe['directions'],
+            'used_ingredients': optimized_ingredients
+        })
+
+    except Exception as e:
+        return jsonify({"error": f"Error in recipe generation: {str(e)}"}), 500
+
+
+@app.route('/generate_recipe_smart', methods=['POST'])
+def handle_smart_recipe_request():
+    """
+    Processes an intelligent recipe generation request.
+    This endpoint remains for backward compatibility.
+    """
+    # Delegate to handle_recipe_request
+    return handle_recipe_request()
+
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=8000, debug=True)

requirements.txt

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+Flask
+Flask-Cors
+transformers
+torch
+numpy
+jax # If you use JAX backend for FlaxAutoModelForSeq2SeqLM
+jaxlib # If you use JAX backend for FlaxAutoModelForSeq2SeqLM