Update app.py

app.py

@@ -1,5 +1,5 @@
 import torch
-from transformers import ViTImageProcessor, ViTForImageClassification, pipeline
+from transformers import ViTImageProcessor, ViTForImageClassification
 from fastai.learner import load_learner
 from fastai.vision.core import PILImage
 from PIL import Image
@@ -8,21 +8,29 @@ import numpy as np
 import gradio as gr
 import io
 import base64
+import tensorflow as tf
+import zipfile
+import os
 
-# 🔹 Cargar modelo ViT desde Hugging Face (HAM10000)
+# 🔹 Cargar modelo ViT desde Hugging Face
 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
+# 🔹 Cargar modelos Fast.ai desde archivos locales
 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")
+# 🔹 Preparar y cargar modelo TensorFlow ISIC
+zip_path = "saved_model.zip"
+extract_dir = "saved_model"
+if not os.path.exists(extract_dir):
+    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+        zip_ref.extractall(extract_dir)
+model_isic = tf.keras.models.load_model(extract_dir)
 
-# 🔹 Clases ViT y niveles de riesgo
+# 🔹 Clases y niveles de riesgo
 CLASSES = [
     "Queratosis actínica / Bowen", "Carcinoma células basales",
     "Lesión queratósica benigna", "Dermatofibroma",
@@ -38,10 +46,20 @@ RISK_LEVELS = {
     6: {'level': 'Bajo', 'color': '#44ff44', 'weight': 0.1}
 }
 
+def preprocess_image_isic(image: Image.Image):
+    # Ajustar tamaño y normalización que espera el modelo ISIC
+    image = image.resize((224, 224))
+    img_array = np.array(image) / 255.0
+    if img_array.shape[-1] == 4:  # eliminar canal alpha si existe
+        img_array = img_array[..., :3]
+    img_array = np.expand_dims(img_array, axis=0)  # batch dimension
+    return img_array
+
 def analizar_lesion_combined(img):
+    # Convertir imagen para Fastai
     img_fastai = PILImage.create(img)
 
-    # 🔹 ViT transformer (HAM10000)
+    # ViT prediction
     inputs = feature_extractor(img, return_tensors="pt")
     with torch.no_grad():
         outputs = model_vit(**inputs)
@@ -50,5 +68,75 @@ def analizar_lesion_combined(img):
     pred_class_vit = CLASSES[pred_idx_vit]
     confidence_vit = probs_vit[pred_idx_vit]
 
-    # 🔹 Fast.ai modelos
-    pred_fast_malignant, _, pr_
+    # Fast.ai models
+    pred_fast_malignant, _, probs_fast_mal = model_malignancy.predict(img_fastai)
+    prob_malignant = float(probs_fast_mal[1])  # índice 1 = maligno
+    pred_fast_type, _, probs_fast_type = model_norm2000.predict(img_fastai)
+
+    # Modelo TensorFlow ISIC
+    x_isic = preprocess_image_isic(img)
+    preds_isic = model_isic.predict(x_isic)[0]  # vector probabilidades
+    pred_idx_isic = int(np.argmax(preds_isic))
+    pred_class_isic = CLASSES[pred_idx_isic]
+    confidence_isic = preds_isic[pred_idx_isic]
+
+    # 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 con los 4 modelos
+    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 TensorFlow</td><td><b>{pred_class_isic}</b></td><td>{confidence_isic:.1%}</td></tr>
+    </table>
+    <br>
+    <b>🩺 Recomendación automática:</b><br>
+    """
+
+    # Recomendación basada en ViT + malignidad
+    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 += "🚨 <b>CRÍTICO</b> – Derivación urgente a oncología dermatológica"
+    elif prob_malignant > 0.4 or cancer_risk_score > 0.4:
+        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
+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 TensorFlow)",
+    description="Comparación entre ViT transformer (HAM10000), dos modelos Fast.ai y el modelo ISIC TensorFlow.",
+    flagging_mode="never"
+)
+
+if __name__ == "__main__":
+    demo.launch()
+