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from fastai.vision.all import *
from fastai.vision.core import PILImage
import torch
import gradio as gr
import warnings
warnings.filterwarnings("ignore")
learn = load_learner('modelo.pkl')
classes = ['abdomen', 'antebrazo', 'cadera', 'cervical', 'clavicula', 'codo', 'col. torax', 'craneo', 'dedos', 'hombro',
'lumbar', 'mano', 'muslo', 'mu帽eca', 'otros', 'pelvis', 'pierna', 'pies', 'rodilla', 'senos nasales', 'tobillo', 'torax']
# Funci贸n de predicci贸n
def classify_image(image, model=learn, classes=classes):
# Cargar imagen y realizar predicci贸n
img = PILImage.create(image)
pred, pred_idx, probs = model.predict(img)
# Filtrar probabilidades
probs = torch.where(probs > 1e-2, probs, torch.tensor(0).to(probs.device))
# Obtener top 5 resultados
top5_probs, top5_idxs = torch.topk(probs, 5)
top5_classes = [classes[idx] for idx in top5_idxs]
# Crear lista de predicciones
predictions = []
for i in range(5):
if top5_probs[i] > 1e-2:
prob = round(float(top5_probs[i].numpy()), 3)
predictions.append(f"{top5_classes[i]}: {prob}")
return predictions
inputs = gr.inputs.Image()
outputs = gr.outputs.Textbox()
gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs, title='Clasificaci贸n de Im谩genes M茅dicas',
description='Cargue una radiograf铆a').launch()
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