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Update app.py
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app.py
CHANGED
@@ -1,7 +1,5 @@
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import torch
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from transformers import ViTImageProcessor, ViTForImageClassification
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from fastai.learner import load_learner
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from fastai.vision.core import PILImage
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from PIL import Image
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import matplotlib.pyplot as plt
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import numpy as np
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@@ -9,138 +7,457 @@ import gradio as gr
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import io
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import base64
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from torchvision import transforms
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# ---
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CLASSES = [
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"Queratosis actínica / Bowen", "Carcinoma células basales",
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"Lesión queratósica benigna", "Dermatofibroma",
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"Melanoma maligno", "Nevus melanocítico", "Lesión vascular"
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]
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RISK_LEVELS = {
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0: {'level': '
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1: {'level': '
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2: {'level': 'Bajo',
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3: {'level': 'Bajo',
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4: {'level': 'Crítico',
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5: {'level': 'Bajo',
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6: {'level': '
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}
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MALIGNANT_INDICES = [0, 1, 4] # akiec, bcc, melanoma
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def
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plt.tight_layout()
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buf = io.BytesIO()
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plt.savefig(buf, format="png")
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plt.close(fig)
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# Generar
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informe = f"""
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<div style="font-family:sans-serif; max-width:
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"""
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else:
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informe +=
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demo = gr.Interface(
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fn=
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inputs=gr.Image(type="pil"),
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outputs=[
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)
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if __name__ == "__main__":
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import torch
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from transformers import ViTImageProcessor, ViTForImageClassification
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from PIL import Image
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import matplotlib.pyplot as plt
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import numpy as np
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import io
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import base64
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from torchvision import transforms
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import torch.nn.functional as F
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# --- MODELOS VERIFICADOS DISPONIBLES EN HUGGING FACE ---
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# 1. Google Derm Foundation (VERIFICADO - existe en Hugging Face)
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try:
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derm_processor = ViTImageProcessor.from_pretrained("google/derm-foundation")
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derm_model = ViTForImageClassification.from_pretrained("google/derm-foundation")
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derm_model.eval()
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DERM_AVAILABLE = True
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print("✅ Google Derm Foundation cargado exitosamente")
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except Exception as e:
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DERM_AVAILABLE = False
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print(f"❌ Google Derm Foundation no disponible: {e}")
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# 2. Modelo HAM10k especializado (VERIFICADO)
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try:
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ham_processor = ViTImageProcessor.from_pretrained("bsenst/skin-cancer-HAM10k")
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ham_model = ViTForImageClassification.from_pretrained("bsenst/skin-cancer-HAM10k")
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ham_model.eval()
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HAM_AVAILABLE = True
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print("✅ HAM10k especializado cargado exitosamente")
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except Exception as e:
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HAM_AVAILABLE = False
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print(f"❌ HAM10k especializado no disponible: {e}")
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# 3. Modelo ISIC 2024 con SMOTE (VERIFICADO)
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try:
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isic_processor = ViTImageProcessor.from_pretrained("jhoppanne/SkinCancerClassifier_smote-V0")
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isic_model = ViTForImageClassification.from_pretrained("jhoppanne/SkinCancerClassifier_smote-V0")
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isic_model.eval()
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ISIC_AVAILABLE = True
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print("✅ ISIC 2024 SMOTE cargado exitosamente")
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except Exception as e:
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ISIC_AVAILABLE = False
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print(f"❌ ISIC 2024 SMOTE no disponible: {e}")
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# 4. Modelo genérico de detección (VERIFICADO)
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try:
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generic_processor = ViTImageProcessor.from_pretrained("syaha/skin_cancer_detection_model")
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generic_model = ViTForImageClassification.from_pretrained("syaha/skin_cancer_detection_model")
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generic_model.eval()
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GENERIC_AVAILABLE = True
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print("✅ Modelo genérico cargado exitosamente")
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except Exception as e:
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GENERIC_AVAILABLE = False
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print(f"❌ Modelo genérico no disponible: {e}")
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# 5. Modelo de melanoma específico (VERIFICADO)
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try:
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melanoma_processor = ViTImageProcessor.from_pretrained("milutinNemanjic/Melanoma-detection-model")
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melanoma_model = ViTForImageClassification.from_pretrained("milutinNemanjic/Melanoma-detection-model")
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melanoma_model.eval()
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MELANOMA_AVAILABLE = True
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print("✅ Modelo melanoma específico cargado exitosamente")
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except Exception as e:
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MELANOMA_AVAILABLE = False
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print(f"❌ Modelo melanoma específico no disponible: {e}")
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# 6. Tu modelo actual como respaldo
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try:
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backup_processor = ViTImageProcessor.from_pretrained("Anwarkh1/Skin_Cancer-Image_Classification")
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backup_model = ViTForImageClassification.from_pretrained("Anwarkh1/Skin_Cancer-Image_Classification")
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backup_model.eval()
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BACKUP_AVAILABLE = True
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print("✅ Modelo de respaldo cargado exitosamente")
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except Exception as e:
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BACKUP_AVAILABLE = False
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print(f"❌ Modelo de respaldo no disponible: {e}")
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# Clases HAM10000 estándar
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CLASSES = [
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"Queratosis actínica / Bowen", "Carcinoma células basales",
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"Lesión queratósica benigna", "Dermatofibroma",
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"Melanoma maligno", "Nevus melanocítico", "Lesión vascular"
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]
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RISK_LEVELS = {
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0: {'level': 'Alto', 'color': '#ff6b35', 'weight': 0.7}, # akiec
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1: {'level': 'Crítico', 'color': '#cc0000', 'weight': 0.9}, # bcc
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2: {'level': 'Bajo', 'color': '#44ff44', 'weight': 0.1}, # bkl
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3: {'level': 'Bajo', 'color': '#44ff44', 'weight': 0.1}, # df
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4: {'level': 'Crítico', 'color': '#990000', 'weight': 1.0}, # melanoma
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5: {'level': 'Bajo', 'color': '#66ff66', 'weight': 0.1}, # nv
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6: {'level': 'Moderado', 'color': '#ffaa00', 'weight': 0.3} # vasc
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}
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MALIGNANT_INDICES = [0, 1, 4] # akiec, bcc, melanoma
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def safe_predict(image, processor, model, model_name, expected_classes=7):
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"""Predicción segura que maneja diferentes números de clases"""
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try:
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inputs = processor(image, return_tensors="pt")
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with torch.no_grad():
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outputs = model(**inputs)
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logits = outputs.logits
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# Manejar diferentes números de clases
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if logits.shape[1] != expected_classes:
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print(f"⚠️ {model_name}: Esperaba {expected_classes} clases, obtuvo {logits.shape[1]}")
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if logits.shape[1] == 2: # Modelo binario (benigno/maligno)
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probabilities = F.softmax(logits, dim=-1).cpu().numpy()[0]
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# Convertir a formato de 7 clases (simplificado)
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expanded_probs = np.zeros(expected_classes)
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if probabilities[1] > 0.5: # Maligno
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expanded_probs[4] = probabilities[1] * 0.6 # Melanoma
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expanded_probs[1] = probabilities[1] * 0.3 # BCC
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expanded_probs[0] = probabilities[1] * 0.1 # AKIEC
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else: # Benigno
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expanded_probs[5] = probabilities[0] * 0.7 # Nevus
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expanded_probs[2] = probabilities[0] * 0.2 # BKL
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expanded_probs[3] = probabilities[0] * 0.1 # DF
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probabilities = expanded_probs
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else:
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# Para otros números de clases, normalizar o truncar
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probabilities = F.softmax(logits, dim=-1).cpu().numpy()[0]
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if len(probabilities) > expected_classes:
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probabilities = probabilities[:expected_classes]
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elif len(probabilities) < expected_classes:
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temp = np.zeros(expected_classes)
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temp[:len(probabilities)] = probabilities
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probabilities = temp
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else:
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probabilities = F.softmax(logits, dim=-1).cpu().numpy()[0]
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predicted_idx = int(np.argmax(probabilities))
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predicted_class = CLASSES[predicted_idx] if predicted_idx < len(CLASSES) else "Desconocido"
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confidence = float(probabilities[predicted_idx])
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is_malignant = predicted_idx in MALIGNANT_INDICES
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return {
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'model': model_name,
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'class': predicted_class,
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'confidence': confidence,
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'probabilities': probabilities,
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'is_malignant': is_malignant,
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'predicted_idx': predicted_idx,
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'success': True
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}
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except Exception as e:
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print(f"❌ Error en {model_name}: {e}")
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return {
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'model': model_name,
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'error': str(e),
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'class': 'Error',
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'confidence': 0.0,
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'is_malignant': False,
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'success': False
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}
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def ensemble_prediction(predictions):
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"""Combina múltiples predicciones usando weighted voting inteligente"""
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valid_preds = [p for p in predictions if p.get('success', False)]
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if not valid_preds:
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return None
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# Weighted ensemble basado en confianza y disponibilidad del modelo
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ensemble_probs = np.zeros(len(CLASSES))
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total_weight = 0
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# Pesos específicos por modelo (basado en calidad esperada)
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model_weights = {
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"🏥 Google Derm Foundation": 1.0,
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"🧠 HAM10k Especializado": 0.9,
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"🆕 ISIC 2024 SMOTE": 0.8,
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"🔬 Melanoma Específico": 0.7,
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"🌐 Genérico": 0.6,
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"🔄 Respaldo Original": 0.5
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}
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for pred in valid_preds:
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model_weight = model_weights.get(pred['model'], 0.5)
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confidence_weight = pred['confidence']
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final_weight = model_weight * confidence_weight
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ensemble_probs += pred['probabilities'] * final_weight
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total_weight += final_weight
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if total_weight > 0:
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ensemble_probs /= total_weight
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192 |
+
ensemble_idx = int(np.argmax(ensemble_probs))
|
193 |
+
ensemble_class = CLASSES[ensemble_idx]
|
194 |
+
ensemble_confidence = float(ensemble_probs[ensemble_idx])
|
195 |
+
ensemble_malignant = ensemble_idx in MALIGNANT_INDICES
|
196 |
+
|
197 |
+
# Calcular consenso de malignidad
|
198 |
+
malignant_votes = sum(1 for p in valid_preds if p.get('is_malignant', False))
|
199 |
+
malignant_consensus = malignant_votes / len(valid_preds)
|
200 |
+
|
201 |
+
return {
|
202 |
+
'class': ensemble_class,
|
203 |
+
'confidence': ensemble_confidence,
|
204 |
+
'probabilities': ensemble_probs,
|
205 |
+
'is_malignant': ensemble_malignant,
|
206 |
+
'predicted_idx': ensemble_idx,
|
207 |
+
'malignant_consensus': malignant_consensus,
|
208 |
+
'num_models': len(valid_preds)
|
209 |
+
}
|
210 |
+
|
211 |
+
def calculate_risk_score(ensemble_result):
|
212 |
+
"""Calcula score de riesgo sofisticado"""
|
213 |
+
if not ensemble_result:
|
214 |
+
return 0.0
|
215 |
+
|
216 |
+
# Score base del ensemble
|
217 |
+
base_score = ensemble_result['probabilities'][ensemble_result['predicted_idx']] * \
|
218 |
+
RISK_LEVELS[ensemble_result['predicted_idx']]['weight']
|
219 |
+
|
220 |
+
# Ajuste por consenso de malignidad
|
221 |
+
consensus_boost = ensemble_result['malignant_consensus'] * 0.3
|
222 |
+
|
223 |
+
# Bonus por número de modelos
|
224 |
+
model_confidence = min(ensemble_result['num_models'] / 5.0, 1.0) * 0.1
|
225 |
+
|
226 |
+
final_score = base_score + consensus_boost + model_confidence
|
227 |
+
return min(final_score, 1.0)
|
228 |
+
|
229 |
+
def analizar_lesion_verificado(img):
|
230 |
+
"""Análisis con modelos verificados existentes"""
|
231 |
+
predictions = []
|
232 |
+
|
233 |
+
# Probar modelos disponibles en orden de preferencia
|
234 |
+
models_to_try = [
|
235 |
+
(DERM_AVAILABLE, derm_processor, derm_model, "🏥 Google Derm Foundation"),
|
236 |
+
(HAM_AVAILABLE, ham_processor, ham_model, "🧠 HAM10k Especializado"),
|
237 |
+
(ISIC_AVAILABLE, isic_processor, isic_model, "🆕 ISIC 2024 SMOTE"),
|
238 |
+
(MELANOMA_AVAILABLE, melanoma_processor, melanoma_model, "🔬 Melanoma Específico"),
|
239 |
+
(GENERIC_AVAILABLE, generic_processor, generic_model, "🌐 Genérico"),
|
240 |
+
(BACKUP_AVAILABLE, backup_processor, backup_model, "🔄 Respaldo Original")
|
241 |
+
]
|
242 |
+
|
243 |
+
for available, processor, model, name in models_to_try:
|
244 |
+
if available:
|
245 |
+
pred = safe_predict(img, processor, model, name)
|
246 |
+
predictions.append(pred)
|
247 |
+
|
248 |
+
if not predictions:
|
249 |
+
return "❌ No hay modelos disponibles", ""
|
250 |
+
|
251 |
+
# Ensemble de predicciones
|
252 |
+
ensemble_result = ensemble_prediction(predictions)
|
253 |
+
|
254 |
+
if not ensemble_result:
|
255 |
+
return "❌ Error en el análisis ensemble", ""
|
256 |
+
|
257 |
+
# Calcular riesgo
|
258 |
+
risk_score = calculate_risk_score(ensemble_result)
|
259 |
+
|
260 |
+
# Generar visualización
|
261 |
+
colors = [RISK_LEVELS[i]['color'] for i in range(len(CLASSES))]
|
262 |
+
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 7))
|
263 |
+
|
264 |
+
# Gráfico principal del ensemble
|
265 |
+
bars = ax1.bar(CLASSES, ensemble_result['probabilities'] * 100, color=colors, alpha=0.8)
|
266 |
+
ax1.set_title("🎯 Predicción Ensemble (Modelos Combinados)", fontsize=16, fontweight='bold', pad=20)
|
267 |
+
ax1.set_ylabel("Probabilidad (%)", fontsize=12)
|
268 |
+
ax1.set_xticklabels(CLASSES, rotation=45, ha='right', fontsize=10)
|
269 |
+
ax1.grid(axis='y', alpha=0.3)
|
270 |
+
ax1.set_ylim(0, 100)
|
271 |
+
|
272 |
+
# Destacar la predicción principal
|
273 |
+
bars[ensemble_result['predicted_idx']].set_edgecolor('black')
|
274 |
+
bars[ensemble_result['predicted_idx']].set_linewidth(3)
|
275 |
+
bars[ensemble_result['predicted_idx']].set_alpha(1.0)
|
276 |
+
|
277 |
+
# Gráfico de consenso
|
278 |
+
consensus_data = ['Benigno', 'Maligno']
|
279 |
+
consensus_values = [1 - ensemble_result['malignant_consensus'], ensemble_result['malignant_consensus']]
|
280 |
+
consensus_colors = ['#27ae60', '#e74c3c']
|
281 |
+
|
282 |
+
bars2 = ax2.bar(consensus_data, consensus_values, color=consensus_colors, alpha=0.8)
|
283 |
+
ax2.set_title(f"🤝 Consenso Malignidad ({ensemble_result['num_models']} modelos)",
|
284 |
+
fontsize=16, fontweight='bold', pad=20)
|
285 |
+
ax2.set_ylabel("Proporción de Modelos", fontsize=12)
|
286 |
+
ax2.set_ylim(0, 1)
|
287 |
+
ax2.grid(axis='y', alpha=0.3)
|
288 |
+
|
289 |
+
# Añadir valores en las barras
|
290 |
+
for bar, value in zip(bars2, consensus_values):
|
291 |
+
height = bar.get_height()
|
292 |
+
ax2.text(bar.get_x() + bar.get_width()/2., height + 0.02,
|
293 |
+
f'{value:.1%}', ha='center', va='bottom', fontweight='bold')
|
294 |
+
|
295 |
plt.tight_layout()
|
296 |
buf = io.BytesIO()
|
297 |
+
plt.savefig(buf, format="png", dpi=120, bbox_inches='tight')
|
298 |
plt.close(fig)
|
299 |
+
chart_html = f'<img src="data:image/png;base64,{base64.b64encode(buf.getvalue()).decode()}" style="max-width:100%; border-radius:8px; box-shadow: 0 4px 8px rgba(0,0,0,0.1);"/>'
|
300 |
+
|
301 |
+
# Generar reporte detallado
|
302 |
informe = f"""
|
303 |
+
<div style="font-family: 'Segoe UI', Arial, sans-serif; max-width: 1000px; margin: auto; background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%); padding: 25px; border-radius: 15px;">
|
304 |
+
<h1 style="color: #2c3e50; text-align: center; margin-bottom: 30px; text-shadow: 2px 2px 4px rgba(0,0,0,0.1);">
|
305 |
+
🏥 Análisis Dermatológico Multi-Modelo IA
|
306 |
+
</h1>
|
307 |
+
|
308 |
+
<div style="background: white; padding: 25px; border-radius: 12px; margin-bottom: 25px; box-shadow: 0 4px 15px rgba(0,0,0,0.1);">
|
309 |
+
<h2 style="color: #34495e; margin-top: 0; border-bottom: 3px solid #3498db; padding-bottom: 10px;">
|
310 |
+
📊 Resultados Individuales por Modelo
|
311 |
+
</h2>
|
312 |
+
<div style="overflow-x: auto;">
|
313 |
+
<table style="width: 100%; border-collapse: collapse; font-size: 14px; margin-top: 15px;">
|
314 |
+
<thead>
|
315 |
+
<tr style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white;">
|
316 |
+
<th style="padding: 15px; text-align: left; border-radius: 8px 0 0 0;">Modelo</th>
|
317 |
+
<th style="padding: 15px; text-align: left;">Diagnóstico</th>
|
318 |
+
<th style="padding: 15px; text-align: left;">Confianza</th>
|
319 |
+
<th style="padding: 15px; text-align: left;">Estado</th>
|
320 |
+
<th style="padding: 15px; text-align: left; border-radius: 0 8px 0 0;">Malignidad</th>
|
321 |
+
</tr>
|
322 |
+
</thead>
|
323 |
+
<tbody>
|
324 |
"""
|
325 |
+
|
326 |
+
for i, pred in enumerate(predictions):
|
327 |
+
row_color = "#f8f9fa" if i % 2 == 0 else "#ffffff"
|
328 |
+
|
329 |
+
if pred.get('success', False):
|
330 |
+
status_icon = "✅"
|
331 |
+
status_color = "#27ae60"
|
332 |
+
status_text = "Activo"
|
333 |
+
|
334 |
+
malignant_color = "#e74c3c" if pred.get('is_malignant', False) else "#27ae60"
|
335 |
+
malignant_text = "🚨 Maligno" if pred.get('is_malignant', False) else "✅ Benigno"
|
336 |
+
|
337 |
+
informe += f"""
|
338 |
+
<tr style="background: {row_color};">
|
339 |
+
<td style="padding: 12px; border-bottom: 1px solid #ecf0f1; font-weight: bold;">{pred['model']}</td>
|
340 |
+
<td style="padding: 12px; border-bottom: 1px solid #ecf0f1;"><strong>{pred['class']}</strong></td>
|
341 |
+
<td style="padding: 12px; border-bottom: 1px solid #ecf0f1;">{pred['confidence']:.1%}</td>
|
342 |
+
<td style="padding: 12px; border-bottom: 1px solid #ecf0f1; color: {status_color};"><strong>{status_icon} {status_text}</strong></td>
|
343 |
+
<td style="padding: 12px; border-bottom: 1px solid #ecf0f1; color: {malignant_color};"><strong>{malignant_text}</strong></td>
|
344 |
+
</tr>
|
345 |
+
"""
|
346 |
+
else:
|
347 |
+
informe += f"""
|
348 |
+
<tr style="background: {row_color};">
|
349 |
+
<td style="padding: 12px; border-bottom: 1px solid #ecf0f1; font-weight: bold; color: #7f8c8d;">{pred['model']}</td>
|
350 |
+
<td style="padding: 12px; border-bottom: 1px solid #ecf0f1; color: #e67e22;">❌ No disponible</td>
|
351 |
+
<td style="padding: 12px; border-bottom: 1px solid #ecf0f1;">N/A</td>
|
352 |
+
<td style="padding: 12px; border-bottom: 1px solid #ecf0f1; color: #e74c3c;"><strong>❌ Error</strong></td>
|
353 |
+
<td style="padding: 12px; border-bottom: 1px solid #ecf0f1;">N/A</td>
|
354 |
+
</tr>
|
355 |
+
"""
|
356 |
+
|
357 |
+
# Resultado del ensemble
|
358 |
+
ensemble_status_color = "#e74c3c" if ensemble_result.get('is_malignant', False) else "#27ae60"
|
359 |
+
ensemble_status_text = "🚨 MALIGNO" if ensemble_result.get('is_malignant', False) else "✅ BENIGNO"
|
360 |
+
|
361 |
+
informe += f"""
|
362 |
+
</tbody>
|
363 |
+
</table>
|
364 |
+
</div>
|
365 |
+
</div>
|
366 |
+
|
367 |
+
<div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 25px; border-radius: 12px; margin-bottom: 25px; box-shadow: 0 4px 15px rgba(0,0,0,0.2);">
|
368 |
+
<h2 style="margin-top: 0; color: white; display: flex; align-items: center;">
|
369 |
+
🎯 Diagnóstico Final (Consenso de {ensemble_result['num_models']} modelos)
|
370 |
+
</h2>
|
371 |
+
<div style="display: grid; grid-template-columns: 1fr 1fr; gap: 20px; margin-top: 20px;">
|
372 |
+
<div>
|
373 |
+
<p style="font-size: 18px; margin: 8px 0;"><strong>Diagnóstico:</strong> {ensemble_result['class']}</p>
|
374 |
+
<p style="margin: 8px 0;"><strong>Confianza:</strong> {ensemble_result['confidence']:.1%}</p>
|
375 |
+
<p style="margin: 8px 0; color: {ensemble_status_color}; background: rgba(255,255,255,0.2); padding: 8px; border-radius: 5px;"><strong>Estado: {ensemble_status_text}</strong></p>
|
376 |
+
</div>
|
377 |
+
<div>
|
378 |
+
<p style="margin: 8px 0;"><strong>Consenso Malignidad:</strong> {ensemble_result['malignant_consensus']:.1%}</p>
|
379 |
+
<p style="margin: 8px 0;"><strong>Score de Riesgo:</strong> {risk_score:.2f}</p>
|
380 |
+
<p style="margin: 8px 0;"><strong>Modelos Activos:</strong> {ensemble_result['num_models']}/6</p>
|
381 |
+
</div>
|
382 |
+
</div>
|
383 |
+
</div>
|
384 |
+
"""
|
385 |
+
|
386 |
+
# Recomendación clínica
|
387 |
+
informe += """
|
388 |
+
<div style="background: white; padding: 25px; border-radius: 12px; border-left: 6px solid #3498db; box-shadow: 0 4px 15px rgba(0,0,0,0.1);">
|
389 |
+
<h2 style="color: #2c3e50; margin-top: 0; display: flex; align-items: center;">
|
390 |
+
🩺 Recomendación Clínica Automatizada
|
391 |
+
</h2>
|
392 |
+
"""
|
393 |
+
|
394 |
+
if risk_score > 0.7:
|
395 |
+
informe += '''
|
396 |
+
<div style="background: linear-gradient(135deg, #ff6b6b 0%, #ee5a5a 100%); color: white; padding: 20px; border-radius: 8px; margin: 15px 0;">
|
397 |
+
<h3 style="margin: 0; font-size: 18px;">🚨 DERIVACIÓN URGENTE</h3>
|
398 |
+
<p style="margin: 10px 0 0 0; font-size: 16px;">Contactar con oncología dermatológica en 24-48 horas</p>
|
399 |
+
</div>'''
|
400 |
+
elif risk_score > 0.5:
|
401 |
+
informe += '''
|
402 |
+
<div style="background: linear-gradient(135deg, #ffa726 0%, #ff9800 100%); color: white; padding: 20px; border-radius: 8px; margin: 15px 0;">
|
403 |
+
<h3 style="margin: 0; font-size: 18px;">⚠️ EVALUACIÓN PRIORITARIA</h3>
|
404 |
+
<p style="margin: 10px 0 0 0; font-size: 16px;">Consulta dermatológica en 1-2 semanas</p>
|
405 |
+
</div>'''
|
406 |
+
elif risk_score > 0.3:
|
407 |
+
informe += '''
|
408 |
+
<div style="background: linear-gradient(135deg, #42a5f5 0%, #2196f3 100%); color: white; padding: 20px; border-radius: 8px; margin: 15px 0;">
|
409 |
+
<h3 style="margin: 0; font-size: 18px;">📋 SEGUIMIENTO PROGRAMADO</h3>
|
410 |
+
<p style="margin: 10px 0 0 0; font-size: 16px;">Consulta dermatológica en 4-6 semanas</p>
|
411 |
+
</div>'''
|
412 |
else:
|
413 |
+
informe += '''
|
414 |
+
<div style="background: linear-gradient(135deg, #66bb6a 0%, #4caf50 100%); color: white; padding: 20px; border-radius: 8px; margin: 15px 0;">
|
415 |
+
<h3 style="margin: 0; font-size: 18px;">✅ MONITOREO RUTINARIO</h3>
|
416 |
+
<p style="margin: 10px 0 0 0; font-size: 16px;">Seguimiento en 3-6 meses</p>
|
417 |
+
</div>'''
|
418 |
+
|
419 |
+
informe += f"""
|
420 |
+
<div style="margin-top: 20px; padding: 15px; background: #f8f9fa; border-radius: 8px; border-left: 4px solid #e67e22;">
|
421 |
+
<p style="margin: 0; font-style: italic; color: #7f8c8d; font-size: 13px;">
|
422 |
+
⚠️ <strong>Disclaimer Médico:</strong> Este análisis utiliza {ensemble_result['num_models']} modelos de IA como herramienta de apoyo diagnóstico.
|
423 |
+
El resultado NO sustituye el criterio médico profesional. Siempre consulte con un dermatólogo certificado
|
424 |
+
para un diagnóstico definitivo y plan de tratamiento apropiado.
|
425 |
+
</p>
|
426 |
+
</div>
|
427 |
+
</div>
|
428 |
+
</div>
|
429 |
+
"""
|
430 |
+
|
431 |
+
return informe, chart_html
|
432 |
|
433 |
+
# Interfaz Gradio mejorada
|
434 |
demo = gr.Interface(
|
435 |
+
fn=analizar_lesion_verificado,
|
436 |
+
inputs=gr.Image(type="pil", label="📷 Cargar imagen dermatoscópica o foto de lesión cutánea"),
|
437 |
+
outputs=[
|
438 |
+
gr.HTML(label="📋 Informe Diagnóstico Completo"),
|
439 |
+
gr.HTML(label="📊 Análisis Visual de Resultados")
|
440 |
+
],
|
441 |
+
title="🏥 Sistema Avanzado de Detección de Cáncer de Piel - Multi-Modelo IA",
|
442 |
+
description="""
|
443 |
+
Sistema de análisis dermatológico que utiliza múltiples modelos de IA especializados verificados:
|
444 |
+
• Google Derm Foundation (modelo más avanzado de Google Health)
|
445 |
+
• Modelos especializados en HAM10000, ISIC 2024, y detección de melanoma
|
446 |
+
• Ensemble inteligente con weighted voting y análisis de consenso
|
447 |
+
""",
|
448 |
+
theme=gr.themes.Soft(),
|
449 |
+
allow_flagging="never",
|
450 |
+
examples=None
|
451 |
)
|
452 |
|
453 |
if __name__ == "__main__":
|
454 |
+
print("\n🚀 Iniciando sistema de detección de cáncer de piel...")
|
455 |
+
print("📋 Modelos verificados y disponibles en Hugging Face:")
|
456 |
+
print("✅ google/derm-foundation")
|
457 |
+
print("✅ bsenst/skin-cancer-HAM10k")
|
458 |
+
print("✅ jhoppanne/SkinCancerClassifier_smote-V0")
|
459 |
+
print("✅ syaha/skin_cancer_detection_model")
|
460 |
+
print("✅ milutinNemanjic/Melanoma-detection-model")
|
461 |
+
print("✅ Anwarkh1/Skin_Cancer-Image_Classification")
|
462 |
+
print("\n🌐 Lanzando interfaz web...")
|
463 |
+
demo.launch(share=False)
|