src/streamlit_app.py

import streamlit as st
import os
import torch
import random
from PIL import Image
import torchvision.transforms as transforms
from transformers import (
    ViTForImageClassification,
    AutoTokenizer,
    T5ForConditionalGeneration
)

# Set page config
st.set_page_config(
    page_title="🍽️ Food Nutrition Estimator",
    page_icon="🥗",
    layout="centered"
)

def main():
    st.title("🍽️ Food Nutrition Estimator")
    st.markdown("""
    Upload a food image to classify it and receive a paraphrased nutritional description.

    ⚠️ This demo is trained on **10 food categories** only:
    pizza, hamburger, sushi, caesar_salad, spaghetti_bolognese,
    ice_cream, fried_rice, tacos, steak, chocolate_cake.
    """)

    hf_token = os.getenv("HF_TOKEN", None)
    cache_dir = "/tmp/cache"
    os.makedirs(cache_dir, exist_ok=True)
    os.environ["HUGGINGFACE_HUB_CACHE"] = cache_dir

    nutritional_info = {
        "pizza": {"serving": "100 g (1 slice)", "calories": "270 kcal", "protein": "12 g", "carbs": "34 g", "fat": "10 g", "ingredients": "dough, tomato sauce, mozzarella cheese", "method": "baked", "substitute": "cauliflower crust"},
        "hamburger": {"serving": "150 g", "calories": "300 kcal", "protein": "20 g", "carbs": "30 g", "fat": "12 g", "ingredients": "ground beef patty, bun, lettuce, tomato", "method": "grilled or pan-fried", "substitute": "chicken patty"},
        "sushi": {"serving": "150 g (6 pieces)", "calories": "200 kcal", "protein": "7 g", "carbs": "30 g", "fat": "5 g", "ingredients": "sushi rice, nori, crab (or imitation), avocado, cucumber", "method": "assembled raw", "substitute": "brown rice"},
        "salad": {"serving": "200 g", "calories": "50 kcal", "protein": "2 g", "carbs": "10 g", "fat": "0.5 g", "ingredients": "mixed greens, tomato, cucumber, carrots", "method": "raw", "substitute": "vinaigrette instead of ranch"},
        "pasta": {"serving": "200 g (1 cup)", "calories": "220 kcal", "protein": "7 g", "carbs": "43 g", "fat": "2 g", "ingredients": "wheat pasta, marinara sauce, olive oil", "method": "boiled and simmered", "substitute": "whole-grain pasta"},
        "ice_cream": {"serving": "100 g (½ cup)", "calories": "200 kcal", "protein": "4 g", "carbs": "20 g", "fat": "12 g", "ingredients": "cream, sugar, milk, vanilla", "method": "churned and frozen", "substitute": "frozen yogurt"},
        "fried_rice": {"serving": "200 g (1 cup)", "calories": "250 kcal", "protein": "8 g", "carbs": "35 g", "fat": "9 g", "ingredients": "rice, egg, peas, carrots, soy sauce, oil", "method": "stir-fried", "substitute": "brown rice"},
        "tacos": {"serving": "100 g (1 taco)", "calories": "200 kcal", "protein": "10 g", "carbs": "15 g", "fat": "10 g", "ingredients": "ground beef, corn tortilla, lettuce, cheese, salsa", "method": "beef pan-fried, tortilla warmed", "substitute": "fish filling"},
        "steak": {"serving": "113 g (4 oz)", "calories": "250 kcal", "protein": "25 g", "carbs": "0 g", "fat": "15 g", "ingredients": "beef sirloin, salt, pepper", "method": "grilled or pan-seared", "substitute": "leaner cut (filet mignon)"},
        "chocolate_cake": {"serving": "100 g (1 slice)", "calories": "350 kcal", "protein": "5 g", "carbs": "50 g", "fat": "15 g", "ingredients": "flour, sugar, cocoa, butter, eggs", "method": "baked", "substitute": "gluten-free flour"}
    }

    label_mapping = {
        "caesar_salad": "salad",
        "spaghetti_bolognese": "pasta"
    }

    st.sidebar.header("Models Used")
    st.sidebar.markdown("""
    - 🖼️ **Image Classifier**: shingguy1/fine_tuned_vit
    - 💬 **Paraphraser**: google/flan-t5-small (sampling mode)
    """)

    transform = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.Lambda(lambda img: img.convert("RGB")),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])
    ])

    @st.cache_resource
    def load_models():
        device = torch.device("cpu")
        vit = ViTForImageClassification.from_pretrained(
            "shingguy1/fine_tuned_vit",
            cache_dir=cache_dir,
            use_auth_token=hf_token
        ).to(device)
        tok = AutoTokenizer.from_pretrained(
            "google/flan-t5-small",
            cache_dir=cache_dir,
            use_auth_token=hf_token
        )
        t5 = T5ForConditionalGeneration.from_pretrained(
            "google/flan-t5-small",
            cache_dir=cache_dir,
            use_auth_token=hf_token
        ).to(device)
        return vit, tok, t5, device

    model_vit, tokenizer_t5, model_t5, device = load_models()

    uploaded = st.file_uploader("📷 Upload a food image...", type=["jpg", "png", "jpeg"])
    if uploaded:
        img = Image.open(uploaded)
        st.image(img, caption="Your Food", use_column_width=True)

        inp = transform(img).unsqueeze(0).to(device)
        with torch.no_grad():
            out = model_vit(pixel_values=inp)
        label = model_vit.config.id2label[out.logits.argmax(-1).item()]
        st.success(f"🍽️ Detected: **{label}**")

        true_label = label_mapping.get(label.lower(), label.lower())
        data = nutritional_info.get(true_label)

        if data:
            base_description = (
                f"A typical {true_label} serving ({data['serving']}) contains about {data['calories']}, "
                f"with {data['protein']} protein, {data['carbs']} carbs, and {data['fat']} fat. "
                f"Made from {data['ingredients']} and usually {data['method']}. "
                f"Try {data['substitute']} as a healthier swap."
            )
            prompt = (
                f"Paraphrase the following nutritional facts in a friendly, conversational tone. "
                f"Use varied sentence structures and synonyms, and feel free to generalize numeric details "
                f"(e.g., ‘around 250 kcal’). Don’t add any new facts.\n\n"
                f"{base_description}"
            )
        else:
            prompt = (
                f"Provide an approximate nutrition summary for {label}, including calories, "
                f"macronutrients, and a brief description."
            )

        inputs = tokenizer_t5(prompt, return_tensors="pt", truncation=True).to(device)
        output_ids = model_t5.generate(
            inputs["input_ids"],
            max_new_tokens=100,
            do_sample=True,
            top_p=0.9,
            temperature=0.7,
            early_stopping=True
        )
        response = tokenizer_t5.decode(output_ids[0], skip_special_tokens=True)

        # Fallback if the output seems too short or misses key phrases
        if "calories" not in response.lower() or len(response.split()) < 10:
            response = base_description

        st.subheader("🧾 Nutrition Overview")
        st.info(response)

if __name__ == "__main__":
    main()