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ada_learn_malben.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:441760abd9a3a6b143917c68cf1709a1d96323fa334b4952009f50a34ddb57c2
+size 87548073

ada_learn_skin_norm2000.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:820252747a54e3e7494fdc86c998d1d67996cb31bde244ddd4260307e80dd819
+size 87739881

app.py

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+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
+
+# 🔹 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()
+
+# 🔹 Modelos Fast.ai desde archivo local
+model_malignancy = load_learner("ada_learn_malben.pkl")
+model_norm2000 = load_learner("ada_learn_skin_norm2000.pkl")
+
+# 🔹 Modelo binario ISIC preentrenado (alta fiabilidad)
+classifier_isic = pipeline("image-classification", model="VRJBro/skin-cancer-detection")
+
+# 🔹 Clases 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 prediction
+    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 predictions
+    pred_fast_malignant, _, probs_fast_mal = model_malignancy.predict(img_fastai)
+    prob_malignant = float(probs_fast_mal[1])
+    pred_fast_type, _, probs_fast_type = model_norm2000.predict(img_fastai)
+
+    # 🔹 ISIC binary classification (modelo 4)
+    result_isic = classifier_isic(img)
+    pred_isic = result_isic[0]['label']
+    confidence_isic = result_isic[0]['score']
+
+    # 🔹 Gráfico ViT
+    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'<img src="data:image/png;base64,{img_b64}" style="max-width:100%"/>'
+
+    # 🔹 Informe HTML
+    informe = f"""
+    <div style="font-family:sans-serif; max-width:800px; margin:auto">
+    <h2>🧪 Diagnóstico por 4 modelos de IA</h2>
+    <table style="border-collapse: collapse; width:100%; font-size:16px">
+        <tr><th style="text-align:left">🔍 Modelo</th><th>Resultado</th><th>Confianza</th></tr>
+        <tr><td>🧠 ViT (transformer)</td><td><b>{pred_class_vit}</b></td><td>{confidence_vit:.1%}</td></tr>
+        <tr><td>🧬 Fast.ai (clasificación)</td><td><b>{pred_fast_type}</b></td><td>N/A</td></tr>
+        <tr><td>⚠️ Fast.ai (malignidad)</td><td><b>{"Maligno" if prob_malignant > 0.5 else "Benigno"}</b></td><td>{prob_malignant:.1%}</td></tr>
+        <tr><td>🔬 ISIC binario</td><td><b>{pred_isic.capitalize()}</b></td><td>{confidence_isic:.1%}</td></tr>
+    </table>
+    <br>
+    <b>🩺 Recomendación automática:</b><br>
+    """
+
+    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 or (pred_isic == "cancerous" and confidence_isic > 0.9):
+        informe += "🚨 <b>CRÍTICO</b> – Derivación urgente a oncología dermatológica"
+    elif prob_malignant > 0.4 or cancer_risk_score > 0.4 or (pred_isic == "cancerous"):
+        informe += "⚠️ <b>ALTO RIESGO</b> – Consulta con dermatólogo en 7 días"
+    elif cancer_risk_score > 0.2:
+        informe += "📋 <b>RIESGO MODERADO</b> – Evaluación programada (2-4 semanas)"
+    else:
+        informe += "✅ <b>BAJO RIESGO</b> – Seguimiento de rutina (3-6 meses)"
+
+    informe += "</div>"
+
+    return informe, html_chart
+
+# 🔹 Interfaz Gradio actualizada
+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 + ISIC)",
+    description="Comparación entre ViT transformer (HAM10000), dos modelos Fast.ai y un clasificador binario ISIC con alta precisión.",
+    flagging_mode="never"
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+torch==2.0.1
+torchvision==0.15.2
+fastai==2.7.12
+transformers==4.41.2
+numpy<2
+gradio==5.38.1
+matplotlib