curl -X POST http://localhost:5000/api/recognize \
  -H "Content-Type: multipart/form-data" \
  -F "image=@photo.jpg" \
  -F "threshold=0.8"

# Response
{
  "status": "success",
  "faces_detected": 2,
  "results": [
    {
      "identity": "john_doe",
      "confidence": 0.92,
      "bbox": [150, 100, 200, 180],
      "landmarks": {...}
    }
  ]
}

import tensorflow as tf
from transformers import AutoTokenizer, AutoModel

class NeuromedModel:
    def __init__(self):
        # Load pre-trained medical BERT
        self.tokenizer = AutoTokenizer.from_pretrained(
            'emilyalsentzer/Bio_ClinicalBERT'
        )
        self.bert_model = AutoModel.from_pretrained(
            'emilyalsentzer/Bio_ClinicalBERT'
        )
        
        # Custom classification layers
        self.classifier = tf.keras.Sequential([
            tf.keras.layers.Dense(512, activation='relu'),
            tf.keras.layers.Dropout(0.3),
            tf.keras.layers.Dense(256, activation='relu'),
            tf.keras.layers.Dense(100, activation='softmax')  # 100 conditions
        ])
    
    def predict_condition(self, symptoms_text):
        # Tokenize and encode
        inputs = self.tokenizer(symptoms_text, return_tensors='tf')
        embeddings = self.bert_model(**inputs).last_hidden_state
        
        # Classify
        predictions = self.classifier(embeddings.mean(axis=1))
        return predictions

import shap
import lime
from sklearn.inspection import permutation_importance

class ModelExplainer:
    def __init__(self, model, X_train, feature_names):
        self.model = model
        self.X_train = X_train
        self.feature_names = feature_names
        
        # Initialize explainers
        self.shap_explainer = shap.TreeExplainer(model)
        self.lime_explainer = lime.tabular.LimeTabularExplainer(
            X_train.values,
            feature_names=feature_names,
            mode='classification'
        )
    
    def explain_prediction(self, instance):
        """Generate multiple explanations for a single prediction"""
        
        # SHAP explanation
        shap_values = self.shap_explainer.shap_values(instance)
        
        # LIME explanation
        lime_explanation = self.lime_explainer.explain_instance(
            instance.flatten(), 
            self.model.predict_proba
        )
        
        # Permutation importance
        perm_importance = permutation_importance(
            self.model, self.X_train, y_train
        )
        
        return {
            'shap': shap_values,
            'lime': lime_explanation,
            'permutation': perm_importance
        }