Spaces:
Sleeping
Sleeping
File size: 2,030 Bytes
cdc152a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
import torch
from transformers import ViTImageProcessor, ViTForImageClassification, pipeline
from fastai.learner import load_learner
from fastai.vision.core import PILImage
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import gradio as gr
import io
import base64
# 馃敼 Cargar modelo ViT desde Hugging Face (HAM10000)
MODEL_NAME = "ahishamm/vit-base-HAM-10000-sharpened-patch-32"
feature_extractor = ViTImageProcessor.from_pretrained(MODEL_NAME)
model_vit = ViTForImageClassification.from_pretrained(MODEL_NAME)
model_vit.eval()
# 馃敼 Cargar modelos Fast.ai
model_malignancy = load_learner("ada_learn_malben.pkl")
model_norm2000 = load_learner("ada_learn_skin_norm2000.pkl")
# 馃敼 Cargar modelo ISIC 7 clases
classifier_isic7 = pipeline("image-classification", model="Anwarkh1/Skin_Cancer-Image_Classification")
# 馃敼 Clases ViT y niveles de riesgo
CLASSES = [
"Queratosis act铆nica / Bowen", "Carcinoma c茅lulas basales",
"Lesi贸n querat贸sica benigna", "Dermatofibroma",
"Melanoma maligno", "Nevus melanoc铆tico", "Lesi贸n vascular"
]
RISK_LEVELS = {
0: {'level': 'Moderado', 'color': '#ffaa00', 'weight': 0.6},
1: {'level': 'Alto', 'color': '#ff4444', 'weight': 0.8},
2: {'level': 'Bajo', 'color': '#44ff44', 'weight': 0.1},
3: {'level': 'Bajo', 'color': '#44ff44', 'weight': 0.1},
4: {'level': 'Cr铆tico', 'color': '#cc0000', 'weight': 1.0},
5: {'level': 'Bajo', 'color': '#44ff44', 'weight': 0.1},
6: {'level': 'Bajo', 'color': '#44ff44', 'weight': 0.1}
}
def analizar_lesion_combined(img):
img_fastai = PILImage.create(img)
# 馃敼 ViT transformer (HAM10000)
inputs = feature_extractor(img, return_tensors="pt")
with torch.no_grad():
outputs = model_vit(**inputs)
probs_vit = outputs.logits.softmax(dim=-1).cpu().numpy()[0]
pred_idx_vit = int(np.argmax(probs_vit))
pred_class_vit = CLASSES[pred_idx_vit]
confidence_vit = probs_vit[pred_idx_vit]
# 馃敼 Fast.ai modelos
pred_fast_malignant, _, pr_
|