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| 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.]()_ | |