Update app.py

app.py

@@ -1,11 +1,11 @@
 import gradio as gr
-from transformers import FlaxAutoModelForSeq2SeqLM, AutoTokenizer
-from transformers import AutoModel
+from transformers import FlaxAutoModelForSeq2SeqLM, AutoTokenizer, AutoModel
 import torch
 import numpy as np
 import random
+import json
 
-# Load models (same as before)
+# Model loading (same as before)
 bert_model_name = "alexdseo/RecipeBERT"
 bert_tokenizer = AutoTokenizer.from_pretrained(bert_model_name)
 bert_model = AutoModel.from_pretrained(bert_model_name)
@@ -15,34 +15,334 @@ 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.
+# Token mapping for T5 model output processing
+special_tokens = t5_tokenizer.all_special_tokens
+tokens_map = {
+    "<sep>": "--",
+    "<section>": "\n"
+}
 
-def generate_recipe_interface(required_ingredients_text, available_ingredients_text, max_ingredients, max_retries):
-    """Gradio interface function"""
+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.
+    """
+    # 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:
+        random_ingredient = random.choice(available_ingredients)
+        required_ingredients = [random_ingredient]
+        available_ingredients = [i for i in available_ingredients if i != 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."""
+    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):
+    """Generates a recipe using the T5 recipe generation model with validation."""
+    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)
+            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
+            }
+
+            # 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):
+                return recipe
+            else:
+                if attempt == max_retries - 1:
+                    return recipe
+
+        except Exception as 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
+    return {
+        "title": f"Recipe with {original_ingredients[0] if original_ingredients else 'ingredients'}",
+        "ingredients": original_ingredients,
+        "directions": ["Error generating recipe instructions"]
+    }
+
+def flutter_api_generate_recipe(ingredients_data):
+    """
+    Flutter-friendly API function that processes JSON input
+    and returns structured JSON output matching your original Flask API
+    """
     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()]
+        # Parse input - handle both string and dict formats
+        if isinstance(ingredients_data, str):
+            data = json.loads(ingredients_data)
+        else:
+            data = ingredients_data
+            
+        # Extract parameters (same as your Flask API)
+        required_ingredients = data.get('required_ingredients', [])
+        available_ingredients = data.get('available_ingredients', [])
         
-        # Find optimal ingredient combination
+        # Backward compatibility
+        if data.get('ingredients') and not required_ingredients:
+            required_ingredients = data.get('ingredients', [])
+            
+        max_ingredients = data.get('max_ingredients', 7)
+        max_retries = data.get('max_retries', 5)
+        
+        if not required_ingredients and not available_ingredients:
+            return json.dumps({"error": "No ingredients provided"})
+            
+        # Find optimal ingredients
         optimized_ingredients = find_best_ingredients(
             required_ingredients,
-            available_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 in exact same format as your Flask API
+        result = {
+            'title': recipe['title'],
+            'ingredients': recipe['ingredients'], 
+            'directions': recipe['directions'],
+            'used_ingredients': optimized_ingredients
+        }
+        
+        return json.dumps(result)
+        
+    except Exception as e:
+        return json.dumps({"error": f"Error in recipe generation: {str(e)}"})
+
+def gradio_ui_generate_recipe(required_ingredients_text, available_ingredients_text, max_ingredients, max_retries):
+    """Gradio UI function for web interface"""
+    try:
+        # Parse text 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()]
+        
+        # Create data dict in Flutter API format
+        data = {
+            'required_ingredients': required_ingredients,
+            'available_ingredients': available_ingredients,
+            'max_ingredients': max_ingredients,
+            'max_retries': max_retries
+        }
+        
+        # Use the same function as Flutter API
+        result_json = flutter_api_generate_recipe(data)
+        result = json.loads(result_json)
+        
+        if 'error' in result:
+            return result['error'], "", "", ""
+            
+        # Format for Gradio display
+        ingredients_list = '\n'.join([f"• {ing}" for ing in result['ingredients']])
+        directions_list = '\n'.join([f"{i+1}. {dir}" for i, dir in enumerate(result['directions'])])
+        used_ingredients = ', '.join(result['used_ingredients'])
         
         return (
-            recipe['title'],
-            ingredients_list,
+            result['title'],
+            ingredients_list, 
             directions_list,
             used_ingredients
         )
@@ -50,59 +350,65 @@ def generate_recipe_interface(required_ingredients_text, available_ingredients_t
     except Exception as e:
         return f"Error: {str(e)}", "", "", ""
 
-# Create Gradio interface
+# 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.Tab("Web Interface"):
+        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(3, 10, value=7, step=1, label="Maximum Ingredients")
+                max_retries = gr.Slider(1, 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)
         
-        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]
-    )
+        generate_btn.click(
+            fn=gradio_ui_generate_recipe,
+            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]
-    )
+    with gr.Tab("API Testing"):
+        gr.Markdown("### Test the Flutter API")
+        gr.Markdown("This tab uses the same function that Flutter apps will call via API")
+        
+        api_input = gr.Textbox(
+            label="JSON Input (Flutter API Format)",
+            placeholder='{"required_ingredients": ["chicken", "rice"], "available_ingredients": ["onion", "garlic"], "max_ingredients": 6}',
+            lines=4
+        )
+        api_output = gr.Textbox(label="JSON Output", lines=15, interactive=False)
+        api_test_btn = gr.Button("Test API", variant="secondary")
+        
+        api_test_btn.click(
+            fn=flutter_api_generate_recipe,
+            inputs=[api_input],
+            outputs=[api_output]
+        )
+        
+        gr.Examples(
+            examples=[
+                ['{"required_ingredients": ["chicken", "rice"], "available_ingredients": ["onion", "garlic", "tomato"], "max_ingredients": 6}'],
+                ['{"ingredients": ["pasta"], "available_ingredients": ["cheese", "mushrooms", "cream"], "max_ingredients": 5}']
+            ],
+            inputs=[api_input]
+        )
 
 if __name__ == "__main__":
     demo.launch()