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import gradio as gr
import tensorflow as tf
import numpy as np
import json
from pathlib import Path
from PIL import Image
# =========================
# 1. Load Model
# =========================
MODEL_PATH = "model/best_food_model.keras"
model = tf.keras.models.load_model(MODEL_PATH)
# =========================
# 2. Labels
# =========================
LABELS = [
'apple_pie', 'baby_back_ribs', 'baklava', 'beef_carpaccio', 'beef_tartare',
'beet_salad', 'beignets', 'bibimbap', 'bread_pudding', 'breakfast_burrito',
'bruschetta', 'caesar_salad', 'cannoli', 'caprese_salad', 'carrot_cake',
'ceviche', 'cheese_plate', 'cheesecake', 'chicken_curry',
'chicken_quesadilla', 'chicken_wings', 'chocolate_cake',
'chocolate_mousse', 'churros', 'clam_chowder', 'club_sandwich',
'crab_cakes', 'creme_brulee', 'croque_madame', 'cup_cakes',
'deviled_eggs', 'donuts', 'dumplings', 'edamame', 'eggs_benedict',
'escargots', 'falafel', 'filet_mignon', 'fish_and_chips', 'foie_gras',
'french_fries', 'french_onion_soup', 'french_toast', 'fried_calamari',
'fried_rice', 'frozen_yogurt', 'garlic_bread', 'gnocchi', 'greek_salad',
'grilled_cheese_sandwich', 'grilled_salmon', 'guacamole', 'gyoza',
'hamburger', 'hot_and_sour_soup', 'hot_dog', 'huevos_rancheros',
'hummus', 'ice_cream', 'lasagna', 'lobster_bisque',
'lobster_roll_sandwich', 'macaroni_and_cheese', 'macarons',
'miso_soup', 'mussels', 'nachos', 'omelette', 'onion_rings',
'oysters', 'pad_thai', 'paella', 'pancakes', 'panna_cotta',
'peking_duck', 'pho', 'pizza', 'pork_chop', 'poutine', 'prime_rib',
'pulled_pork_sandwich', 'ramen', 'ravioli', 'red_velvet_cake',
'risotto', 'samosa', 'sashimi', 'scallops', 'seaweed_salad',
'shrimp_and_grits', 'spaghetti_bolognese', 'spaghetti_carbonara',
'spring_rolls', 'steak', 'strawberry_shortcake', 'sushi', 'tacos',
'takoyaki', 'tiramisu', 'tuna_tartare', 'waffles'
]
# =========================
# 3. Load Nutrition JSON
# =========================
NUTRITION_PATH = Path("nutrition_db.json")
if NUTRITION_PATH.exists():
with open(NUTRITION_PATH, "r", encoding="utf-8") as f:
NUTRITION_DB = json.load(f)
else:
NUTRITION_DB = {}
# =========================
# 4. Prediction Function
# =========================
def predict_nutrition(img):
if img is None:
return {}, "Upload an image."
# Ensure PIL RGB
if isinstance(img, np.ndarray):
img = Image.fromarray(img)
img = img.convert("RGB").resize((224, 224))
img_array = tf.keras.preprocessing.image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0) / 255.0
preds = model.predict(img_array, verbose=0)[0]
# Top 3 predictions
top_indices = np.argsort(preds)[-3:][::-1]
confidences = {LABELS[i]: float(preds[i]) for i in top_indices}
# Top 1 nutrition
top_idx = int(np.argmax(preds))
food_name = LABELS[top_idx]
nutri = NUTRITION_DB.get(
food_name,
{"cal": 0, "protein": 0, "carbs": 0, "fat": 0}
)
clean_name = food_name.replace("_", " ").title()
nutrition_md = f"""
### 🥗 Nutrition Facts — {clean_name}
*(Estimated per 100g)*
| Nutrient | Amount |
|---|---|
| Calories | {nutri['cal']} kcal |
| Protein | {nutri['protein']} g |
| Carbs | {nutri['carbs']} g |
| Fat | {nutri['fat']} g |
"""
return confidences, nutrition_md
# =========================
# 5. Gradio UI
# =========================
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🍎 Food-101 Classifier")
gr.Markdown("Upload food → get prediction + macros.")
with gr.Row():
with gr.Column():
input_img = gr.Image(type="numpy", label="Upload Food Image")
submit_btn = gr.Button("Analyze Meal", variant="primary")
with gr.Column():
output_chart = gr.Label(num_top_classes=3)
output_nutri = gr.Markdown()
submit_btn.click(
fn=predict_nutrition,
inputs=input_img,
outputs=[output_chart, output_nutri]
)
gr.Markdown("---")
gr.Markdown("Educational demo. Not medical advice.")
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860)
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