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@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 recipe_index.faiss filter=lfs diff=lfs merge=lfs -text
+recipe_index[[:space:]](2).faiss filter=lfs diff=lfs merge=lfs -text

app (4).py

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+import gradio as gr
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer, util
+import faiss
+import numpy as np
+from transformers import pipeline
+import time
+
+# --- 1. DATA LOADING AND PREPROCESSING ---
+print("===== Application Startup =====")
+start_time = time.time()
+
+# Load dataset and limit to the first 20,000 rows
+dataset = load_dataset("corbt/all-recipes", split="train[:20000]")
+
+# Preprocessing functions to extract features from the raw text
+def extract_each_feature(sample):
+    full_text = sample['input']
+    
+    # User's fix: Use "\n" instead of "\\n" to correctly find the title
+    title = full_text[:full_text.find("\n")]
+    
+    ingredients = "Not available"
+    directions = "Not available"
+    
+    ingredients_start_index = full_text.find("Ingredients:")
+    directions_start_index = full_text.find("Directions:")
+    
+    if ingredients_start_index != -1 and directions_start_index != -1:
+        ingredients = full_text[ingredients_start_index + len("Ingredients:"):directions_start_index].strip()
+
+    if directions_start_index != -1:
+        directions_raw = full_text[directions_start_index + len("Directions:"):].strip()
+        
+        next_ing_index = directions_raw.find("Ingredients:")
+        next_dir_index = directions_raw.find("Directions:")
+        
+        cut_off_indices = [idx for idx in [next_ing_index, next_dir_index] if idx != -1]
+        if cut_off_indices:
+            cut_off_point = min(cut_off_indices)
+            directions = directions_raw[:cut_off_point].strip()
+        else:
+            directions = directions_raw
+            
+    return {
+        "title": title,
+        "ingredients": ingredients,
+        "directions": directions,
+    }
+
+# Apply preprocessing
+dataset = dataset.map(extract_each_feature)
+
+# --- 2. EMBEDDING AND RECOMMENDATION ENGINE ---
+print("Loading embedding model...")
+model_name = "all-MiniLM-L6-v2"
+embedding_model = SentenceTransformer(f"sentence-transformers/{model_name}")
+
+index_file = "recipe_index.faiss"
+
+print(f"Loading FAISS index from {index_file}...")
+index = faiss.read_index(index_file)
+print(f"Index is ready. Total vectors in index: {index.ntotal}")
+
+# --- 3. SYNTHETIC GENERATION (IMPROVED) ---
+print("Loading generative model...")
+generator = pipeline('text-generation', model='gpt2')
+
+def get_recommendations_and_generate(query_ingredients, k=3):
+    # 1. Get Recommendations
+    query_vector = embedding_model.encode([query_ingredients])
+    query_vector = np.array(query_vector, dtype=np.float32)
+    distances, indices = index.search(query_vector, k)
+    
+    results = []
+    for idx_numpy in indices[0]:
+        idx = int(idx_numpy) 
+        recipe = {
+            "title": dataset[idx]['title'],
+            "ingredients": dataset[idx]['ingredients'],
+            "directions": dataset[idx]['directions']
+        }
+        results.append(recipe)
+        
+    while len(results) < 3:
+        results.append({"title": "No recipe found", "ingredients": "", "directions": ""})
+        
+    # 2. Generate 10 new recipe ideas with a simpler, more direct prompt
+    prompt = f"Write a complete recipe that includes a title, a list of ingredients, and step-by-step directions. The recipe must use the following ingredients: {query_ingredients}."
+    
+    # Optimized for speed by reducing max_new_tokens
+    generated_outputs = generator(prompt, max_new_tokens=180, num_return_sequences=10, pad_token_id=50256)
+    
+    # 3. Find the best recipe out of the 10 generated
+    generated_texts = [output['generated_text'].replace(prompt, "").strip() for output in generated_outputs]
+    
+    # Embed all 10 generated texts
+    generated_embeddings = embedding_model.encode(generated_texts)
+    
+    # Calculate cosine similarity between the user's query and each generated text
+    similarities = util.cos_sim(query_vector, generated_embeddings)
+    
+    # Find the index of the most similar generated recipe
+    best_recipe_index = np.argmax(similarities)
+    best_generated_recipe = generated_texts[best_recipe_index]
+
+    return results[0], results[1], results[2], best_generated_recipe
+
+# --- 4. GRADIO USER INTERFACE ---
+def format_recipe(recipe):
+    # Formats the recommended recipes with markdown
+    if not recipe or not recipe['title']:
+        return "### No recipe found."
+    return f"### {recipe['title']}\n**Ingredients:**\n{recipe['ingredients']}\n\n**Directions:**\n{recipe['directions']}"
+
+def format_generated_recipe(recipe_text):
+    # Formats the AI-generated recipe as simple text, without extra markdown
+    return recipe_text
+
+def recipe_wizard(ingredients):
+    rec1, rec2, rec3, gen_rec_text = get_recommendations_and_generate(ingredients)
+    return format_recipe(rec1), format_recipe(rec2), format_recipe(rec3), format_generated_recipe(gen_rec_text)
+
+end_time = time.time()
+print(f"Models and data loaded in {end_time - start_time:.2f} seconds.")
+
+# Gradio Interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🍳 RecipeWizard AI")
+    gr.Markdown("Enter the ingredients you have, and get recipe recommendations plus a new AI-generated idea!")
+    
+    with gr.Row():
+        ingredient_input = gr.Textbox(label="Your Ingredients", placeholder="e.g., chicken, rice, tomatoes, garlic")
+        submit_btn = gr.Button("Get Recipes")
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            gr.Markdown("### Recommended Recipes")
+            output_rec1 = gr.Markdown()
+            output_rec2 = gr.Markdown()
+            output_rec3 = gr.Markdown()
+        with gr.Column(scale=1):
+            gr.Markdown("### ✨ AI-Generated Idea")
+            output_gen = gr.Textbox(label="AI Generated Recipe", lines=15) # Changed to Textbox for plain text
+
+    submit_btn.click(
+        fn=recipe_wizard,
+        inputs=ingredient_input,
+        outputs=[output_rec1, output_rec2, output_rec3, output_gen]
+    )
+    
+    gr.Examples(
+        examples=[
+            ["chicken, broccoli, cheese"],
+            ["ground beef, potatoes, onions"],
+            ["flour, sugar, eggs, butter"]
+        ],
+        inputs=ingredient_input
+    )
+
+demo.launch()

recipe_index (2).faiss

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:90b9a5c8797e28a0fe4130d9af7ccdb897d0849110ea43765aee3b7b670b14ef
+size 30720045

requirements (4).txt

@@ -0,0 +1,8 @@
+torch==2.1.0
+faiss-cpu==1.7.4
+gradio==4.8.0
+pyarrow==14.0.1
+datasets==2.15.0
+transformers==4.35.2
+sentence-transformers==2.3.1
+huggingface-hub==0.19.4