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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip 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

README.txt

@@ -0,0 +1,7 @@
+🍳 Recipe Genius: Your AI Kitchen Assistant
+
+Ever wonder what to cook with the ingredients you already have? Recipe Genius solves that problem.
+
+How it works: Simply list your ingredients (e.g., "chicken, rice, tomatoes").
+
+What you get: The app uses vector search to find the 3 most similar recipes from a database of over 50,000. It also uses a generative AI to invent a completely new recipe title for you on the spot.

app.py

@@ -1,64 +1,125 @@
+# app.py
+
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+import faiss
+import numpy as np
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer, util
+from transformers import pipeline
+import time
+
+# --- 1. LOAD MODELS AND DATA (GLOBAL SCOPE) ---
+# This section runs only once when the app starts.
+
+print("Loading dataset and embedding model...")
+start_time = time.time()
+
+# Load the dataset
+dataset = load_dataset("corbt/all-recipes", split="train[:20000]")
+
+# Extract title and ingredients for embedding
+def extract_title_and_ingredients(sample):
+    """
+    Extract the title and ingredients of a recipe from a sample.
+    """
+    extraction = sample['input'][:sample['input'].find("Directions")]
+    return {
+        "text_for_embedding": extraction
+    }
+
+dataset = dataset.map(extract_title_and_ingredients)
+
+
+# Extract all features
+def extract_each_feature(sample):
+    """
+    Extract each feature of a recipe from a sample.
+    """
+    title = sample['input'][:sample['input'].find("\n")]
+    ingredients = sample['input'][sample['input'].find("Ingredients")+len("Ingredients:\n"):sample['input'].find("Directions")].strip()
+    directions = sample['input'][sample['input'].find("Directions")+len("Directions:\n"):].strip()
+    return {
+        "title": title,
+        "ingredients": ingredients,
+        "directions": directions,
+    }
+
+dataset = dataset.map(extract_each_feature)
+
+# Load the embedding model
+embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+
+# Load the pre-built FAISS index
+try:
+    index = faiss.read_index("recipe_index.faiss")
+except Exception as e:
+    print(f"Could not load FAISS index. Error: {e}. Please ensure 'recipe_index.faiss' is in the same directory.")
+    # Handle error gracefully, maybe by disabling the search feature
+    index = None
+
+# Load the text generation model
+generator = pipeline('text-generation', model='distilgpt2')
+
+end_time = time.time()
+print(f"Models and data loaded in {end_time - start_time:.2f} seconds.")
+
+
+# --- 2. DEFINE THE CORE LOGIC FUNCTION ---
+def find_and_generate(user_ingredients):
+    """
+    Takes user ingredients, finds similar recipes, and generates a new one.
+    """
+    if not user_ingredients:
+        return "<p>Please enter some ingredients.</p>", "<p></p>"
+        
+    # --- Recommendation Logic ---
+    if index:
+        query_vector = embedding_model.encode([user_ingredients])
+        distances, indices = index.search(np.array(query_vector, dtype=np.float32), 3)
+        
+        recs_html = "<h2>Top 3 Similar Recipes:</h2>"
+        for i, idx in enumerate(indices[0]):
+            # Use .get() for safety in case a key is missing
+            title = dataset[int(idx)].get('title', dataset[int(idx)].get('Name', 'No Title'))
+            ingredients_list = dataset[int(idx)].get('ingredients', dataset[int(idx)].get('RecipeIngredientParts', []))
+            ingredients = "<br>".join(ingredients_list)
+            
+            recs_html += f"<h3>{i+1}. {title}</h3>"
+            recs_html += f"<b>Ingredients:</b><br>{ingredients}<hr>"
+    else:
+        recs_html = "<h2>Recommendation engine not available.</h2> <p>Could not load the FAISS index file.</p>"
+
+
+    # --- Generation Logic ---
+    prompt = f"A creative and delicious recipe that features {user_ingredients}.\n\nRecipe Title:"
+    generated_outputs = generator(prompt, max_new_tokens=100, num_return_sequences=1)
+    generated_text = generated_outputs[0]['generated_text']
+    
+    gen_html = "<h2>AI-Generated Idea:</h2>"
+    gen_html += generated_text.replace("\n", "<br>")
+
+    return recs_html, gen_html
+
+# --- 3. CREATE AND LAUNCH THE GRADIO INTERFACE ---
+
+examples = [
+    ["chicken, potatoes, carrots, onions"],
+    ["beef, soy sauce, ginger, rice"],
+    ["tomatoes, basil, mozzarella, olive oil"],
+]
+
+demo = gr.Interface(
+    fn=find_and_generate,
+    inputs=gr.Textbox(lines=3, label="Enter Your Ingredients (comma-separated)"),
+    outputs=[
+        gr.HTML(label="Similar Recipes"),
+        gr.HTML(label="AI Generated Recipe")
     ],
+    title="🍳 Recipe Genius",
+    description="Your personal AI chef! Enter the ingredients you have, and get 3 real recipe recommendations plus 1 new AI-generated idea.",
+    examples=examples,
+    theme=gr.themes.Soft()
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+# Launch the app!
+demo.launch()

recipe_index.faiss

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

requirements.txt

@@ -1 +1,6 @@
-huggingface_hub==0.25.2
+datasets
+sentence-transformers
+torch
+faiss-cpu
+transformers
+gradio