import torch from transformers import ViTImageProcessor, ViTForImageClassification 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 import os import zipfile import tensorflow as tf # --- Extraer y cargar modelo TensorFlow desde zip --- zip_path = "saved_model.zip" extract_dir = "saved_model" if not os.path.exists(extract_dir): os.makedirs(extract_dir) with zipfile.ZipFile(zip_path, 'r') as zip_ref: zip_ref.extractall(extract_dir) model_tf = tf.saved_model.load(extract_dir) # --- Inspección de firma del modelo TensorFlow --- print("\n\n🔍 FIRMA DEL MODELO TENSORFLOW:") for key, func in model_tf.signatures.items(): print(f"Firma: {key}") print("Entradas:", func.structured_input_signature) print("Salidas:", func.structured_outputs) # Función helper para inferencia TensorFlow def predict_tf(img: Image.Image): try: img_resized = img.resize((224,224)) img_np = np.array(img_resized) / 255.0 if img_np.shape[-1] == 4: img_np = img_np[..., :3] img_tf = tf.convert_to_tensor(img_np, dtype=tf.float32) img_tf = tf.expand_dims(img_tf, axis=0) infer = model_tf.signatures["serving_default"] output = infer(img_tf) pred = list(output.values())[0].numpy()[0] probs = tf.nn.softmax(pred).numpy() return probs except Exception as e: print(f"Error en predict_tf: {e}") return np.zeros(len(CLASSES)) 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() model_malignancy = load_learner("ada_learn_malben.pkl") model_norm2000 = load_learner("ada_learn_skin_norm2000.pkl") 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): try: img_fastai = PILImage.create(img) 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] except Exception as e: print(f"Error en ViT prediction: {e}") pred_class_vit = "Error" confidence_vit = 0.0 probs_vit = np.zeros(len(CLASSES)) try: pred_fast_malignant, _, probs_fast_mal = model_malignancy.predict(img_fastai) prob_malignant = float(probs_fast_mal[1]) except Exception as e: print(f"Error en Fast.ai malignancy: {e}") prob_malignant = 0.0 try: pred_fast_type, _, probs_fast_type = model_norm2000.predict(img_fastai) except Exception as e: print(f"Error en Fast.ai tipo: {e}") pred_fast_type = "Error" try: probs_tf = predict_tf(img) pred_idx_tf = int(np.argmax(probs_tf)) confidence_tf = probs_tf[pred_idx_tf] if pred_idx_tf < len(CLASSES): pred_class_tf = CLASSES[pred_idx_tf] else: pred_class_tf = f"Clase desconocida (\u00edndice {pred_idx_tf})" except Exception as e: print(f"Error en TensorFlow prediction: {e}") pred_class_tf = "Error" confidence_tf = 0.0 colors_bars = [RISK_LEVELS[i]['color'] for i in range(7)] fig, ax = plt.subplots(figsize=(8, 3)) ax.bar(CLASSES, probs_vit*100, color=colors_bars) ax.set_title("Probabilidad ViT por tipo de lesión") ax.set_ylabel("Probabilidad (%)") ax.set_xticks(np.arange(len(CLASSES))) ax.set_xticklabels(CLASSES, rotation=45, ha='right') ax.grid(axis='y', alpha=0.2) plt.tight_layout() buf = io.BytesIO() plt.savefig(buf, format="png") plt.close(fig) img_bytes = buf.getvalue() img_b64 = base64.b64encode(img_bytes).decode("utf-8") html_chart = f'' informe = f"""

🧪 Diagnóstico por 4 modelos de IA

🔍 ModeloResultadoConfianza
🧠 ViT (transformer){pred_class_vit}{confidence_vit:.1%}
🧬 Fast.ai (clasificación){pred_fast_type}N/A
⚠️ Fast.ai (malignidad){"Maligno" if prob_malignant > 0.5 else "Benigno"}{prob_malignant:.1%}
🔬 TensorFlow (saved_model){pred_class_tf}{confidence_tf:.1%}

🧪 Recomendación automática:
""" cancer_risk_score = sum(probs_vit[i] * RISK_LEVELS[i]['weight'] for i in range(7)) if prob_malignant > 0.7 or cancer_risk_score > 0.6: informe += "🚨 CRÍTICO – Derivación urgente a oncología dermatológica" elif prob_malignant > 0.4 or cancer_risk_score > 0.4: informe += "⚠️ ALTO RIESGO – Consulta con dermatólogo en 7 días" elif cancer_risk_score > 0.2: informe += "📋 RIESGO MODERADO – Evaluación programada (2-4 semanas)" else: informe += "✅ BAJO RIESGO – Seguimiento de rutina (3-6 meses)" informe += "
" return informe, html_chart # Interfaz Gradio demo = gr.Interface( fn=analizar_lesion_combined, inputs=gr.Image(type="pil", label="Sube una imagen de la lesión"), outputs=[gr.HTML(label="Informe combinado"), gr.HTML(label="Gráfico ViT")], title="Detector de Lesiones Cutáneas (ViT + Fast.ai + TensorFlow)", description="Comparación entre ViT transformer (HAM10000), dos modelos Fast.ai y un modelo TensorFlow.", flagging_mode="never" ) if __name__ == "__main__": demo.launch()