app.py

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

# 🔹 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()

# 🔹 Cargar modelos Fast.ai
model_malignancy = load_learner("ada_learn_malben.pkl")
model_norm2000 = load_learner("ada_learn_skin_norm2000.pkl")

# 🔹 Cargar modelo ISIC 7 clases
classifier_isic7 = pipeline("image-classification", model="Anwarkh1/Skin_Cancer-Image_Classification")

# 🔹 Clases ViT y niveles de riesgo
CLASSES = [
    "Queratosis actínica / Bowen", "Carcinoma células basales",
    "Lesión queratósica benigna", "Dermatofibroma",
    "Melanoma maligno", "Nevus melanocítico", "Lesión 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': 'Crítico', '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)

    # 🔹 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]

    # 🔹 Fast.ai modelos
    pred_fast_malignant, _, pr_