Update app.py

app.py

@@ -1,387 +1,108 @@
-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)
+import gradio as gr
+from transformers import FlaxAutoModelForSeq2SeqLM, AutoTokenizer
+from transformers import AutoModel
+import torch
+import numpy as np
+import random
+
+# Load models (same as before)
+bert_model_name = "alexdseo/RecipeBERT"
+bert_tokenizer = AutoTokenizer.from_pretrained(bert_model_name)
+bert_model = AutoModel.from_pretrained(bert_model_name)
+bert_model.eval()
+
+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)
+
+# ... (all your existing functions remain the same) ...
+# get_embedding, average_embedding, get_cosine_similarity, etc.
+
+def generate_recipe_interface(required_ingredients_text, available_ingredients_text, max_ingredients, max_retries):
+    """Gradio interface function"""
+    try:
+        # Parse ingredient inputs
+        required_ingredients = [ing.strip() for ing in required_ingredients_text.split(',') if ing.strip()]
+        available_ingredients = [ing.strip() for ing in available_ingredients_text.split(',') if ing.strip()]
+        
+        # Find optimal ingredient combination
+        optimized_ingredients = find_best_ingredients(
+            required_ingredients,
+            available_ingredients,
+            max_ingredients
+        )
+        
+        # Generate recipe
+        recipe = generate_recipe_with_t5(optimized_ingredients, max_retries)
+        
+        # Format output
+        ingredients_list = '\n'.join([f"• {ing}" for ing in recipe['ingredients']])
+        directions_list = '\n'.join([f"{i+1}. {dir}" for i, dir in enumerate(recipe['directions'])])
+        used_ingredients = ', '.join(optimized_ingredients)
+        
+        return (
+            recipe['title'],
+            ingredients_list,
+            directions_list,
+            used_ingredients
+        )
+        
+    except Exception as e:
+        return f"Error: {str(e)}", "", "", ""
+
+# Create Gradio interface
+with gr.Blocks(title="AI Recipe Generator") as demo:
+    gr.Markdown("# 🍳 AI Recipe Generator")
+    gr.Markdown("Generate recipes using AI with intelligent ingredient combination!")
+    
+    with gr.Row():
+        with gr.Column():
+            required_ing = gr.Textbox(
+                label="Required Ingredients (comma-separated)",
+                placeholder="chicken, rice, onion",
+                lines=2
+            )
+            available_ing = gr.Textbox(
+                label="Available Ingredients (comma-separated)",
+                placeholder="garlic, tomato, pepper, herbs",
+                lines=2
+            )
+            max_ing = gr.Slider(
+                minimum=3,
+                maximum=10,
+                value=7,
+                step=1,
+                label="Maximum Ingredients"
+            )
+            max_retries = gr.Slider(
+                minimum=1,
+                maximum=10,
+                value=5,
+                step=1,
+                label="Max Retries"
+            )
+            generate_btn = gr.Button("Generate Recipe", variant="primary")
+        
+        with gr.Column():
+            title_output = gr.Textbox(label="Recipe Title", interactive=False)
+            ingredients_output = gr.Textbox(label="Ingredients", lines=8, interactive=False)
+            directions_output = gr.Textbox(label="Directions", lines=10, interactive=False)
+            used_ingredients_output = gr.Textbox(label="Used Ingredients", interactive=False)
+    
+    generate_btn.click(
+        fn=generate_recipe_interface,
+        inputs=[required_ing, available_ing, max_ing, max_retries],
+        outputs=[title_output, ingredients_output, directions_output, used_ingredients_output]
+    )
+    
+    # Example
+    gr.Examples(
+        examples=[
+            ["chicken, rice", "onion, garlic, tomato, herbs, pepper", 6, 3],
+            ["pasta", "cheese, mushrooms, cream, spinach, garlic", 5, 3],
+        ],
+        inputs=[required_ing, available_ing, max_ing, max_retries]
+    )
+
+if __name__ == "__main__":
+    demo.launch()