|
|
| import tensorflow as tf
|
| import numpy as np
|
| from PIL import Image
|
| import streamlit as st
|
| import os
|
| import logging
|
|
|
|
|
| logger = logging.getLogger('bloodcell_app')
|
|
|
| @st.cache_resource
|
| def load_tf_model(model_path):
|
| """Loads a TensorFlow/Keras model, handling potential errors."""
|
| if not os.path.exists(model_path):
|
| logger.error(f"Model file not found at path: {model_path}")
|
| st.error(f"Model file not found: {os.path.basename(model_path)}. Please ensure it's in the correct directory.")
|
| return None
|
| try:
|
| logger.info(f"Loading TensorFlow model from {model_path}...")
|
| model = tf.keras.models.load_model(model_path)
|
| logger.info(f"Model {os.path.basename(model_path)} loaded successfully.")
|
| return model
|
| except Exception as e:
|
| logger.error(f"Error loading TensorFlow model from {model_path}: {e}")
|
| st.error(f"Error loading model {os.path.basename(model_path)}: {e}")
|
| return None
|
|
|
| def preprocess_and_predict(image: Image.Image, model: tf.keras.Model, class_names: list, target_size: tuple):
|
| """Preprocess image and predict class using the provided model."""
|
| if model is None:
|
| st.error("Prediction model is not loaded.")
|
| return None, None
|
|
|
| try:
|
|
|
| img_resized = image.resize(target_size)
|
| if img_resized.mode != 'RGB':
|
| img_resized = img_resized.convert('RGB')
|
|
|
|
|
| img_array = np.array(img_resized) / 255.0
|
| img_array = np.expand_dims(img_array, axis=0)
|
|
|
|
|
| predictions = model.predict(img_array)
|
|
|
|
|
| pred_index = np.argmax(predictions, axis=1)[0]
|
| if pred_index < len(class_names):
|
| predicted_class = class_names[pred_index]
|
| confidence = np.max(predictions) * 100
|
| logger.info(f"Prediction: {predicted_class} with {confidence:.2f}% confidence.")
|
| return predicted_class, confidence
|
| else:
|
| logger.error(f"Prediction index {pred_index} is out of bounds for class_names list (length {len(class_names)}).")
|
| st.error("Model prediction resulted in an invalid class index. Check model compatibility.")
|
| return None, None
|
|
|
| except Exception as e:
|
| logger.error(f"Error during image preprocessing or prediction: {e}")
|
| st.error(f"Image processing/prediction error: {e}")
|
| return None, None |
