app.py

import gradio as gr
import torch
import numpy as np
import matplotlib.pyplot as plt
import base64
import io
from fastai.vision.all import *
import tensorflow as tf
import zipfile
import os
import traceback
from transformers import AutoImageProcessor, AutoModelForImageClassification

# Descomprimir el modelo si no se ha descomprimido aún
if not os.path.exists("saved_model"):
    with zipfile.ZipFile("saved_model.zip", "r") as zip_ref:
        zip_ref.extractall("saved_model")

# Cargar modelo ISIC con TensorFlow desde el directorio correcto
try:
    model_isic = tf.saved_model.load("saved_model")
except Exception as e:
    print("\U0001F534 Error al cargar el modelo ISIC:", e)
    raise

# Cargar modelos fastai
model_malignancy = load_learner("ada_learn_malben.pkl")
model_norm2000 = load_learner("ada_learn_skin_norm2000.pkl")

# Cargar modelo ViT
feature_extractor = AutoImageProcessor.from_pretrained("nateraw/vit-skin-cancer")
model_vit = AutoModelForImageClassification.from_pretrained("nateraw/vit-skin-cancer")

# Clases y colores
CLASSES = ['akiec', 'bcc', 'bkl', 'df', 'mel', 'nv', 'vasc']
RISK_LEVELS = {
    0: {"label": "akiec", "color": "#FF6F61", "weight": 0.9},
    1: {"label": "bcc", "color": "#FF8C42", "weight": 0.7},
    2: {"label": "bkl", "color": "#FFD166", "weight": 0.3},
    3: {"label": "df", "color": "#06D6A0", "weight": 0.1},
    4: {"label": "mel", "color": "#EF476F", "weight": 1.0},
    5: {"label": "nv", "color": "#118AB2", "weight": 0.2},
    6: {"label": "vasc", "color": "#073B4C", "weight": 0.4},
}

def preprocess_image_isic(pil_image):
    image = pil_image.resize((224, 224))
    array = np.array(image) / 255.0
    return np.expand_dims(array, axis=0)

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]

        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)

        x_isic = preprocess_image_isic(img)
        isic_func = model_isic.signatures["serving_default"]
        preds_isic_tensor = isic_func(tf.constant(x_isic))
        key = list(preds_isic_tensor.keys())[0]
        preds_isic = preds_isic_tensor[key].numpy()[0]
        pred_idx_isic = int(np.argmax(preds_isic))
        pred_class_isic = CLASSES[pred_idx_isic]
        confidence_isic = preds_isic[pred_idx_isic]

        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 = 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>"

        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

    except Exception as e:
        print("\U0001F534 ERROR en analizar_lesion_combined:")
        print(str(e))
        traceback.print_exc()
        return f"<b>Error interno:</b> {str(e)}", ""

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()