Update app.py

app.py

@@ -24,21 +24,24 @@ model_tf = tf.saved_model.load(extract_dir)
 
 # Función helper para inferencia TensorFlow
 def predict_tf(img: Image.Image):
-    # Preprocesar imagen para TF: convertir a tensor float32, normalizar, añadir batch
-    img_resized = img.resize((224,224))  # ajusta según modelo
-    img_np = np.array(img_resized) / 255.0
-    if img_np.shape[-1] == 4:  # eliminar canal alfa si existe
-        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)  # batch dimension
-
-    # Ejecutar modelo (suponiendo firma default)
-    infer = model_tf.signatures["serving_default"]
-    output = infer(img_tf)
-    # Extraemos el primer tensor de salida (puede cambiar según modelo)
-    pred = list(output.values())[0].numpy()[0]
-    probs = tf.nn.softmax(pred).numpy()
-    return probs
+    try:
+        # Preprocesar imagen para TF: convertir a tensor float32, normalizar, añadir batch
+        img_resized = img.resize((224,224))  # ajusta según modelo
+        img_np = np.array(img_resized) / 255.0
+        if img_np.shape[-1] == 4:  # eliminar canal alfa si existe
+            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)  # batch dimension
+
+        # Ejecutar modelo (suponiendo firma default)
+        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))
 
 # 🔹 Cargar modelo ViT desde Hugging Face
 MODEL_NAME = "ahishamm/vit-base-HAM-10000-sharpened-patch-32"
@@ -67,34 +70,51 @@ RISK_LEVELS = {
 }
 
 def analizar_lesion_combined(img):
-    # Convertir imagen para Fastai
-    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 models
-    pred_fast_malignant, _, probs_fast_mal = model_malignancy.predict(img_fastai)
-    prob_malignant = float(probs_fast_mal[1])  # 1 = maligno
-
-    pred_fast_type, _, probs_fast_type = model_norm2000.predict(img_fastai)
-
-    # TensorFlow model prediction
-    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 (índice {pred_idx_tf})"
-
+    try:
+        # Convertir imagen para Fastai
+        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]
+    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:
+        # Fast.ai models
+        pred_fast_malignant, _, probs_fast_mal = model_malignancy.predict(img_fastai)
+        prob_malignant = float(probs_fast_mal[1])  # 1 = maligno
+    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:
+        # TensorFlow model prediction
+        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 (índice {pred_idx_tf})"
+    except Exception as e:
+        print(f"Error en TensorFlow prediction: {e}")
+        pred_class_tf = "Error"
+        confidence_tf = 0.0
 
     # Gráfico ViT
     colors_bars = [RISK_LEVELS[i]['color'] for i in range(7)]