app.py

import gradio as gr
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
from PIL import Image
import torch
import pandas as pd

# Cargar modelo de reconocimiento de alimentos
model_name = "nateraw/food101"  # modelo preentrenado Food-101
model = AutoModelForImageClassification.from_pretrained(model_name)
extractor = AutoFeatureExtractor.from_pretrained(model_name)

# Cargar tabla de nutrición de ejemplo
# Debes subir nutrition.csv después con columnas: food, calories, protein, fat, carbs
nutrition_df = pd.read_csv("nutrition.csv")  

def analizar_comida(imagen: Image):
    # Preprocesar imagen
    inputs = extractor(images=imagen, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    
    predicted_class = model.config.id2label[outputs.logits.]()_