app.py

import streamlit as st
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image
import torch

# Load the model and processor
@st.cache_resource
def load_model():
    st.text("Loading model...")
    processor = AutoProcessor.from_pretrained("vikhyatk/moondream2", trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained(
        "vikhyatk/moondream2",
        revision="2025-03-27",
        trust_remote_code=True,
        # device_map="auto"  # Enable if running on GPU
    )
    st.text("Model loaded successfully!")
    return model, processor

model, processor = load_model()

# File uploader
uploaded_file = st.file_uploader("Upload an image of a dish", type=["jpg", "jpeg", "png"])

if uploaded_file:
    image = Image.open(uploaded_file).convert("RGB")
    st.image(image, caption="Uploaded Image", use_column_width=True)

    # Auto-ask the food question
    question = "What food is in this image?"  # or try: "What is the name of the dish?"

    if st.button("Classify Dish"):
        inputs = processor(image, question, return_tensors="pt").to(model.device)
        with torch.no_grad():
            output = model.generate(**inputs, max_new_tokens=64)
        answer = processor.batch_decode(output, skip_special_tokens=True)[0].strip()

        st.success(f"🍽️ Predicted Dish: **{answer}**")