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import os
import pickle
import tensorflow as tf
import logging
from typing import Optional
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class DermFoundationModel:
"""Wrapper for Google's Derm Foundation model"""
def __init__(self, model_path: str):
self.model_path = model_path
self.model = None
def load(self) -> bool:
"""Load the Derm Foundation SavedModel"""
try:
saved_model_pb = os.path.join(self.model_path, "saved_model.pb")
if not os.path.exists(saved_model_pb):
logger.error(f"Model file not found at {saved_model_pb}")
return False
self.model = tf.saved_model.load(self.model_path)
logger.info(f"Derm Foundation model loaded from {self.model_path}")
return True
except Exception as e:
logger.error(f"Error loading Derm Foundation model: {e}")
return False
def get_inference_function(self):
"""Get the model's inference signature"""
if self.model is None:
raise RuntimeError("Model not loaded")
return self.model.signatures["serving_default"]
class EASIModel:
"""Wrapper for EASI severity prediction model"""
def __init__(self, model_path: str):
self.model_path = model_path
self.model = None
self.mlb = None
self.embedding_scaler = None
self.confidence_scaler = None
self.weighted_scaler = None
def load(self) -> bool:
"""Load the EASI model and preprocessors"""
try:
if not os.path.exists(self.model_path):
logger.error(f"EASI model not found at {self.model_path}")
return False
logger.info(f"Loading pickle from {self.model_path}")
with open(self.model_path, 'rb') as f:
model_data = pickle.load(f)
logger.info("Pickle loaded successfully")
# Load preprocessing components
self.mlb = model_data['mlb']
self.embedding_scaler = model_data['embedding_scaler']
self.confidence_scaler = model_data['confidence_scaler']
self.weighted_scaler = model_data['weighted_scaler']
logger.info("Preprocessors loaded")
# Load Keras model
keras_model_path = model_data['keras_model_path']
logger.info(f"Keras model path: {keras_model_path}")
if not os.path.exists(keras_model_path):
logger.error(f"Keras model not found at {keras_model_path}")
logger.error(f"Current working directory: {os.getcwd()}")
logger.error(f"Files in models/trained_model/: {os.listdir('./models/trained_model/')}")
return False
logger.info(f"Loading Keras model from {keras_model_path}")
self.model = tf.keras.models.load_model(keras_model_path)
logger.info(f"EASI model loaded successfully from {self.model_path}")
return True
except Exception as e:
logger.error(f"Error loading EASI model: {e}", exc_info=True)
return False
def predict(self, embedding):
"""Make predictions on a single embedding"""
if self.model is None:
raise RuntimeError("Model not loaded")
import numpy as np
if len(embedding.shape) == 1:
embedding = embedding.reshape(1, -1)
# Scale embedding
embedding_scaled = self.embedding_scaler.transform(embedding)
# Make predictions
predictions = self.model.predict(embedding_scaled, verbose=0)
# Process outputs
condition_probs = predictions['conditions'][0]
individual_confidences = predictions['individual_confidences'][0]
individual_weights = predictions['individual_weights'][0]
# Threshold for predictions
condition_threshold = 0.3
predicted_indices = np.where(condition_probs > condition_threshold)[0]
# Build results
predicted_conditions = []
predicted_confidences = []
predicted_weights_dict = {}
for idx in predicted_indices:
condition_name = self.mlb.classes_[idx]
condition_prob = float(condition_probs[idx])
# Inverse transform individual outputs
if individual_confidences[idx] > 0:
confidence_orig = self.confidence_scaler.inverse_transform(
[[individual_confidences[idx]]]
)[0, 0]
else:
confidence_orig = 0.0
if individual_weights[idx] > 0:
weight_orig = self.weighted_scaler.inverse_transform(
[[individual_weights[idx]]]
)[0, 0]
else:
weight_orig = 0.0
predicted_conditions.append(condition_name)
predicted_confidences.append(max(0, confidence_orig))
predicted_weights_dict[condition_name] = max(0, weight_orig)
# All condition probabilities
all_condition_probs = {}
all_confidences = {}
all_weights = {}
for i, class_name in enumerate(self.mlb.classes_):
all_condition_probs[class_name] = float(condition_probs[i])
if individual_confidences[i] > 0:
conf_orig = self.confidence_scaler.inverse_transform(
[[individual_confidences[i]]]
)[0, 0]
all_confidences[class_name] = max(0, conf_orig)
else:
all_confidences[class_name] = 0.0
if individual_weights[i] > 0:
weight_orig = self.weighted_scaler.inverse_transform(
[[individual_weights[i]]]
)[0, 0]
all_weights[class_name] = max(0, weight_orig)
else:
all_weights[class_name] = 0.0
return {
'dermatologist_skin_condition_on_label_name': predicted_conditions,
'dermatologist_skin_condition_confidence': predicted_confidences,
'weighted_skin_condition_label': predicted_weights_dict,
'all_condition_probabilities': all_condition_probs,
'all_individual_confidences': all_confidences,
'all_individual_weights': all_weights,
'condition_threshold': condition_threshold
}
class ModelManager:
"""Singleton manager for all models"""
_instance = None
def __new__(cls):
if cls._instance is None:
cls._instance = super(ModelManager, cls).__new__(cls)
cls._instance.derm_model = None
cls._instance.easi_model = None
return cls._instance
def load_models(self, derm_path: str, easi_path: str) -> bool:
"""Load both models"""
logger.info("Loading models...")
# Load Derm Foundation model
self.derm_model = DermFoundationModel(derm_path)
if not self.derm_model.load():
return False
# Load EASI model
self.easi_model = EASIModel(easi_path)
if not self.easi_model.load():
return False
logger.info("All models loaded successfully")
return True
def is_ready(self) -> bool:
"""Check if both models are loaded"""
return (self.derm_model is not None and
self.derm_model.model is not None and
self.easi_model is not None and
self.easi_model.model is not None) |