app.py

import warnings
warnings.filterwarnings("ignore")

import gradio as gr
import pandas as pd
import numpy as np
import os
import json
from sentence_transformers import SentenceTransformer
from sklearn.linear_model import LogisticRegression
from huggingface_hub import InferenceClient, hf_hub_download

# ============================================
# 1. Load Dataset from HuggingFace
# ============================================
print("Loading dataset from HuggingFace...")

csv_path = hf_hub_download(
    repo_id="adigabay2003/smartchef-recipes",
    filename="smartchef_dataset.csv",
    repo_type="dataset"
)
df = pd.read_csv(csv_path)
df = df.dropna(subset=["text"])
df = df[df["text"].str.strip() != ""]
df = df.reset_index(drop=True)
print(f"Loaded {len(df)} recipes")

# ============================================
# 2. Load Optimal Model from JSON
# ============================================
with open("optimal_model.json", "r") as f:
    optimal = json.load(f)

MODEL_NAME = optimal["model_name"]
print(f"Using embedding model: {MODEL_NAME}")

# ============================================
# 3. Load Precomputed Embeddings
# ============================================
print("Loading precomputed embeddings...")
X = np.load("best_embeddings.npy")
y = df["vibe"].tolist()
print(f"Embeddings shape: {X.shape}")

# ============================================
# 4. Train Classifier on Embeddings
# ============================================
print("Training classifier...")
embedder = SentenceTransformer(MODEL_NAME)
clf = LogisticRegression(max_iter=1000)
clf.fit(X, y)
print("Classifier ready!")

# ============================================
# 5. Prediction Function
# ============================================
def get_recipe_vibe(title, ingredients):
    text = f"{title}. Ingredients: {ingredients}"
    embedding = embedder.encode([text])
    vibe = clf.predict(embedding)[0]
    return vibe

# ============================================
# 6. Image Generation
# ============================================
def generate_food_image(title, vibe):
    token = os.getenv("HF_TOKEN")
    if not token:
        return None
    client = InferenceClient(
        model="stabilityai/stable-diffusion-xl-base-1.0",
        token=token
    )
    prompt = (
        f"Professional food photography of {title}, "
        f"{vibe} style, 4k, highly detailed, "
        f"appetizing, dramatic lighting, vibrant colors"
    )
    try:
        return client.text_to_image(prompt)
    except:
        return None

# ============================================
# 7. Main App Function
# ============================================
def smart_chef_app(title, ingredients):
    vibe = get_recipe_vibe(title, ingredients)
    messages = {
        "Romantic":     "🌹 Love is in the air!",
        "Quick Lunch":  "⚡ Fast & Delicious!",
        "Comfort Food": "🧸 Warm & Cozy.",
        "Party Snack":  "🎉 Party Time!",
        "Healthy Boost":"🥗 Feel Good Food.",
        "Fancy Dinner": "🍷 Chef's Kiss!"
    }
    msg = messages.get(vibe, "Yum!")
    img = generate_food_image(title, vibe)
    return vibe, msg, img

# ============================================
# 8. CSS & Theme
# ============================================
ultra_css = """
@import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;500;800&display=swap');
.gradio-container {
    background: linear-gradient(-45deg, #0f0c29, #302b63, #24243e, #4a1c1c);
    background-size: 400% 400%;
    animation: gradientBG 15s ease infinite;
    font-family: 'Montserrat', sans-serif !important;
}
@keyframes gradientBG {
    0% {background-position: 0% 50%;}
    50% {background-position: 100% 50%;}
    100% {background-position: 0% 50%;}
}
h1 {
    background: linear-gradient(to right, #ffcc00, #ff6600, #ff3300);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    text-shadow: 0px 0px 15px rgba(255, 102, 0, 0.6);
    font-weight: 800 !important;
    text-align: center;
    font-size: 3.5em !important;
    margin-bottom: 10px !important;
}
h3 { color: #e0e0e0 !important; text-align: center; font-weight: 300; }
.group-container {
    background: rgba(255, 255, 255, 0.03);
    backdrop-filter: blur(20px);
    border-radius: 25px;
    border: 1px solid rgba(255, 255, 255, 0.1);
    box-shadow: 0 8px 32px 0 rgba(0, 0, 0, 0.5);
    padding: 30px !important;
    transition: all 0.4s cubic-bezier(0.25, 0.8, 0.25, 1);
}
.group-container:hover {
    transform: translateY(-10px) scale(1.02);
    border: 1px solid rgba(255, 204, 0, 0.4);
    box-shadow: 0 20px 50px rgba(255, 102, 0, 0.3);
}
button.primary-btn {
    background: linear-gradient(135deg, #ffcc00, #ff6600) !important;
    border: none !important;
    color: #000 !important;
    font-weight: 800 !important;
    font-size: 1.3em !important;
    padding: 15px 30px !important;
    animation: pulse 2s infinite;
}
@keyframes pulse {
    0% {box-shadow: 0 0 0 0 rgba(255, 102, 0, 0.7);}
    70% {box-shadow: 0 0 0 15px rgba(255, 102, 0, 0);}
    100% {box-shadow: 0 0 0 0 rgba(255, 102, 0, 0);}
}
label { color: #ffcc00 !important; font-weight: 600 !important; }
span { color: #e0e0e0 !important; }
textarea, input {
    background-color: rgba(0,0,0,0.4) !important;
    border: 1px solid rgba(255,255,255,0.1) !important;
    color: white !important;
}
.gradio-container > * { animation: fadeUp 0.8s ease-out forwards; opacity: 0; }
@keyframes fadeUp { from { opacity: 0; transform: translateY(30px); } to { opacity: 1; transform: translateY(0); } }
"""

theme = gr.themes.Soft(primary_hue="orange", neutral_hue="slate").set(
    body_background_fill="#000000",
    block_background_fill="#121212",
    block_border_width="0px"
)

# ============================================
# 9. Gradio UI
# ============================================
with gr.Blocks(css=ultra_css, theme=theme) as demo:
    gr.Markdown("# ✨ SMARTCHEF AI ✨")
    gr.Markdown("### Experience the future of culinary magic.")

    with gr.Row():
        with gr.Column(elem_classes="group-container"):
            gr.Markdown("#### 📝 CREATE YOUR DISH")
            t_in = gr.Textbox(
                label="Dish Name",
                placeholder="e.g., Mystical Truffle Risotto"
            )
            i_in = gr.Textbox(
                label="Ingredients",
                placeholder="e.g., Arborio rice, black truffle dust, parmesan...",
                lines=4
            )
            btn = gr.Button("✨ UNLEASH MAGIC ✨", elem_classes="primary-btn")

        with gr.Column(elem_classes="group-container"):
            gr.Markdown("#### 🔮 THE REVELATION")
            v_out = gr.Textbox(
                label="Vibe Detected",
                interactive=False
            )
            m_out = gr.Markdown()
            im_out = gr.Image(
                label="AI Visualization",
                type="pil",
                interactive=False
            )

    gr.Markdown("### ⚡ Instant Inspirations (Click to try):")
    gr.Examples(
        examples=[
            ["Midnight Burger",
             "Black bun, wagyu beef, spicy aioli, caramelized onions"],
            ["Neon Sushi Roll",
             "Tuna, salmon, avocado, glowing tobiko, eel sauce"],
            ["Enchanted Forest Salad",
             "Kale, edible flowers, berries, nuts, fairy dust dressing"]
        ],
        inputs=[t_in, i_in],
        elem_id="examples-table"
    )

    btn.click(smart_chef_app, [t_in, i_in], [v_out, m_out, im_out])

if __name__ == "__main__":
    demo.launch()