fix dashboard prediction
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@ -48,6 +48,10 @@ class EnvironmentalSimulator:
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results = []
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current_date = datetime.now()
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# Calcola valori giornalieri di pioggia e radiazione
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daily_rainfall = rainfall / 30 # Distribuisce la pioggia mensile sui giorni
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daily_radiation = radiation # Usa il valore di radiazione fornito
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for day in range(days):
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# Calcola la fase corrente
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day_of_year = (current_date + timedelta(days=day)).timetuple().tm_yday
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@ -56,6 +60,12 @@ class EnvironmentalSimulator:
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# Simula temperatura giornaliera
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temp = np.random.uniform(temp_range[0], temp_range[1])
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# Simula variazione giornaliera della pioggia (±20% del valore medio)
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daily_rain = daily_rainfall * np.random.uniform(0.8, 1.2)
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# Simula variazione giornaliera della radiazione (±10% del valore base)
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daily_rad = daily_radiation * np.random.uniform(0.9, 1.1)
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# Calcola stress giornaliero
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stress = self.calculate_stress_index(temp_range, humidity, rainfall, radiation)
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@ -66,6 +76,9 @@ class EnvironmentalSimulator:
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'date': current_date + timedelta(days=day),
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'phase': phase,
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'temperature': temp,
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'rainfall': daily_rain,
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'radiation': daily_rad,
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'humidity': humidity,
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'stress_index': stress,
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'growth_rate': growth_rate
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})
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@ -9,11 +9,6 @@ import json
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from utils.helpers import clean_column_name
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from dashboard.environmental_simulator import *
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from dotenv import load_dotenv
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import sagemaker
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from sagemaker.tensorflow import TensorFlowModel
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from sagemaker.serverless import ServerlessInferenceConfig
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from sagemaker import Session
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import boto3
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CONFIG_FILE = 'olive_config.json'
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@ -23,6 +18,13 @@ os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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# Set global precision policy
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tf.keras.mixed_precision.set_global_policy('float32')
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DEV_MODE = True
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model = None
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scaler_temporal = None
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scaler_static = None
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scaler_y = None
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MODEL_LOADING = False
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def load_config():
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default_config = {
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@ -54,6 +56,10 @@ def load_config():
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'etichettatura': 0.30
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},
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'selling_price': 12.00
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},
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'inference': {
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'debug_mode': True,
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'model_path': './sources/olive_oil_transformer/olive_oil_transformer_model.keras'
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}
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}
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@ -82,150 +88,12 @@ try:
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simulated_data = pd.read_parquet("./sources/olive_training_dataset.parquet")
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weather_data = pd.read_parquet("./sources/weather_data_solarenergy.parquet")
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olive_varieties = pd.read_parquet("./sources/olive_varieties.parquet")
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if not True:
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# Print versions and system information
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print(f"Keras version: {keras.__version__}")
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print(f"TensorFlow version: {tf.__version__}")
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print(f"CUDA available: {tf.test.is_built_with_cuda()}")
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print(f"GPU devices: {tf.config.list_physical_devices('GPU')}")
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# GPU memory configuration
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gpus = tf.config.experimental.list_physical_devices('GPU')
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if gpus:
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try:
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for gpu in gpus:
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tf.config.experimental.set_memory_growth(gpu, True)
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logical_gpus = tf.config.experimental.list_logical_devices('GPU')
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print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
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except RuntimeError as e:
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print(e)
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@keras.saving.register_keras_serializable()
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class DataAugmentation(tf.keras.layers.Layer):
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"""Custom layer per l'augmentation dei dati"""
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def __init__(self, noise_stddev=0.03, **kwargs):
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super().__init__(**kwargs)
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self.noise_stddev = noise_stddev
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def call(self, inputs, training=None):
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if training:
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return inputs + tf.random.normal(
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shape=tf.shape(inputs),
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mean=0.0,
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stddev=self.noise_stddev
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)
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return inputs
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def get_config(self):
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config = super().get_config()
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config.update({"noise_stddev": self.noise_stddev})
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return config
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@keras.saving.register_keras_serializable()
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class PositionalEncoding(tf.keras.layers.Layer):
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"""Custom layer per l'encoding posizionale"""
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def __init__(self, d_model, **kwargs):
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super().__init__(**kwargs)
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self.d_model = d_model
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def build(self, input_shape):
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_, seq_length, _ = input_shape
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# Crea la matrice di encoding posizionale
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position = tf.range(seq_length, dtype=tf.float32)[:, tf.newaxis]
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div_term = tf.exp(
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tf.range(0, self.d_model, 2, dtype=tf.float32) *
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(-tf.math.log(10000.0) / self.d_model)
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)
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# Calcola sin e cos
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pos_encoding = tf.zeros((1, seq_length, self.d_model))
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pos_encoding_even = tf.sin(position * div_term)
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pos_encoding_odd = tf.cos(position * div_term)
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# Assegna i valori alle posizioni pari e dispari
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pos_encoding = tf.concat(
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[tf.expand_dims(pos_encoding_even, -1),
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tf.expand_dims(pos_encoding_odd, -1)],
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axis=-1
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)
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pos_encoding = tf.reshape(pos_encoding, (1, seq_length, -1))
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pos_encoding = pos_encoding[:, :, :self.d_model]
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# Salva l'encoding come peso non trainabile
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self.pos_encoding = self.add_weight(
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shape=(1, seq_length, self.d_model),
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initializer=tf.keras.initializers.Constant(pos_encoding),
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trainable=False,
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name='positional_encoding'
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)
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super().build(input_shape)
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def call(self, inputs):
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# Broadcast l'encoding posizionale sul batch
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batch_size = tf.shape(inputs)[0]
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pos_encoding_tiled = tf.tile(self.pos_encoding, [batch_size, 1, 1])
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return inputs + pos_encoding_tiled
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def get_config(self):
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config = super().get_config()
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config.update({"d_model": self.d_model})
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return config
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@keras.saving.register_keras_serializable()
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class WarmUpLearningRateSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
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"""Custom learning rate schedule with linear warmup and exponential decay."""
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def __init__(self, initial_learning_rate=1e-3, warmup_steps=500, decay_steps=5000):
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super().__init__()
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self.initial_learning_rate = initial_learning_rate
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self.warmup_steps = warmup_steps
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self.decay_steps = decay_steps
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def __call__(self, step):
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warmup_pct = tf.cast(step, tf.float32) / self.warmup_steps
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warmup_lr = self.initial_learning_rate * warmup_pct
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decay_factor = tf.pow(0.1, tf.cast(step, tf.float32) / self.decay_steps)
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decayed_lr = self.initial_learning_rate * decay_factor
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return tf.where(step < self.warmup_steps, warmup_lr, decayed_lr)
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def get_config(self):
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return {
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'initial_learning_rate': self.initial_learning_rate,
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'warmup_steps': self.warmup_steps,
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'decay_steps': self.decay_steps
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}
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@keras.saving.register_keras_serializable()
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def weighted_huber_loss(y_true, y_pred):
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# Pesi per diversi output
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weights = tf.constant([1.0, 0.8, 0.8, 1.0, 0.6], dtype=tf.float32)
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huber = tf.keras.losses.Huber(delta=1.0)
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loss = huber(y_true, y_pred)
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weighted_loss = tf.reduce_mean(loss * weights)
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return weighted_loss
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print("Caricamento modello e scaler...")
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model = tf.keras.models.load_model('./sources/olive_oil_transformer/olive_oil_transformer_model.keras')
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# model.save('./sources/olive_oil_transformer/olive_oil_transformer_model', save_format='tf')
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scaler_temporal = joblib.load('./sources/olive_oil_transformer/olive_oil_transformer_scaler_temporal.joblib')
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scaler_static = joblib.load('./sources/olive_oil_transformer/olive_oil_transformer_scaler_static.joblib')
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scaler_y = joblib.load('./sources/olive_oil_transformer/olive_oil_transformer_scaler_y.joblib')
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else:
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print("Modalità sviluppo attiva - Modelli non caricati")
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scaler_temporal = joblib.load('./sources/olive_oil_transformer/olive_oil_transformer_scaler_temporal.joblib')
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scaler_static = joblib.load('./sources/olive_oil_transformer/olive_oil_transformer_scaler_static.joblib')
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scaler_y = joblib.load('./sources/olive_oil_transformer/olive_oil_transformer_scaler_y.joblib')
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config = load_config()
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DEV_MODE = config.get('inference', {}).get('debug_mode', True)
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except Exception as e:
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print(f"Errore nel caricamento: {str(e)}")
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raise e
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@ -460,7 +328,6 @@ def mock_make_prediction(weather_data, varieties_info, percentages, hectares, si
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def make_prediction(weather_data, varieties_info, percentages, hectares, simulation_data=None):
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DEV_MODE = True
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if DEV_MODE:
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return mock_make_prediction(weather_data, varieties_info, percentages, hectares, simulation_data)
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try:
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@ -564,12 +431,6 @@ def make_prediction(weather_data, varieties_info, percentages, hectares, simulat
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print(f"Shape dei dati temporali: {temporal_data.shape}")
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print(f"Shape dei dati statici: {static_data.shape}")
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# Debug info
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print("Static data:")
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print(static_data)
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print("\nTemporal data:")
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print(temporal_data)
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# Standardizza i dati
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temporal_data = scaler_temporal.transform(temporal_data.reshape(1, -1)).reshape(1, 1, -1)
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static_data = scaler_static.transform(static_data)
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@ -989,10 +850,9 @@ def create_configuration_tab():
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])
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], className="mb-4")
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], md=6),
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# Sezione SageMaker
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dbc.Row([
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dbc.Col([
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create_sagemaker_config_section()
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create_inference_config_section()
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], md=12)
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]),
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# Configurazione Costi
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@ -1013,57 +873,213 @@ def create_configuration_tab():
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@app.callback(
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[Output('sagemaker-status', 'children'),
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Output('sagemaker-endpoint', 'children'),
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Output('sagemaker-latency', 'children'),
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Output('sagemaker-requests', 'children')],
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[Input('sagemaker-switch', 'value')],
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[State('sagemaker-memory', 'value'),
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State('sagemaker-concurrency', 'value'),
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State('sagemaker-model-uri', 'value'),
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State('sagemaker-role', 'value')]
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[Output('inference-status', 'children'),
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Output('inference-mode', 'children'),
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Output('inference-latency', 'children'),
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Output('inference-requests', 'children')],
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[Input('debug-switch', 'value')]
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)
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def toggle_sagemaker(enabled, memory, concurrency, model_uri, role):
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if not enabled:
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return (
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dbc.Alert("Servizio non attivo", color="warning"),
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"N/A",
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"N/A",
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"N/A"
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)
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else:
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def toggle_inference_mode(debug_mode):
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global DEV_MODE
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global model
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try:
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config = load_config()
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# Aggiorna la modalità debug nella configurazione
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config['inference'] = config.get('inference', {}) # Crea la sezione se non esiste
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config['inference']['debug_mode'] = debug_mode
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# Salva la configurazione aggiornata
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try:
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boto3.setup_default_session(profile_name="giuseppenucifora", region_name="eu-west-1")
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session = Session()
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# Inizializza SageMaker
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tf_model = TensorFlowModel(
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model_data=model_uri,
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role=role,
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framework_version="2.14"
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)
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serverless_config = ServerlessInferenceConfig(
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memory_size_in_mb=memory,
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max_concurrency=concurrency,
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)
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# Deploy del modello
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predictor = tf_model.deploy(serverless_inference_config=serverless_config)
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return (
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dbc.Alert("Servizio attivo", color="success"),
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predictor.endpoint_name,
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"< 100ms",
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"0"
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)
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with open(CONFIG_FILE, 'w') as f:
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json.dump(config, f, indent=4)
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except Exception as e:
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print(f"Errore nell'attivazione di SageMaker: {str(e)}")
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print(f"Errore nel salvataggio della configurazione: {e}")
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DEV_MODE = debug_mode
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if debug_mode:
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return (
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dbc.Alert(f"Errore: {str(e)}", color="danger"),
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"Errore",
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"Errore",
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"Errore"
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dbc.Alert("Modalità Debug attiva - Using mock predictions", color="info"),
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"Debug (Mock)",
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"< 1ms",
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"N/A"
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)
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else:
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try:
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print(f"Keras version: {keras.__version__}")
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print(f"TensorFlow version: {tf.__version__}")
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print(f"CUDA available: {tf.test.is_built_with_cuda()}")
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print(f"GPU devices: {tf.config.list_physical_devices('GPU')}")
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# GPU memory configuration
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gpus = tf.config.experimental.list_physical_devices('GPU')
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if gpus:
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try:
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for gpu in gpus:
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tf.config.experimental.set_memory_growth(gpu, True)
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logical_gpus = tf.config.experimental.list_logical_devices('GPU')
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print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
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except RuntimeError as e:
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print(e)
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@keras.saving.register_keras_serializable()
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class DataAugmentation(tf.keras.layers.Layer):
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"""Custom layer per l'augmentation dei dati"""
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def __init__(self, noise_stddev=0.03, **kwargs):
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super().__init__(**kwargs)
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self.noise_stddev = noise_stddev
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def call(self, inputs, training=None):
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if training:
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return inputs + tf.random.normal(
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shape=tf.shape(inputs),
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mean=0.0,
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stddev=self.noise_stddev
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)
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return inputs
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def get_config(self):
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config = super().get_config()
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config.update({"noise_stddev": self.noise_stddev})
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return config
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@keras.saving.register_keras_serializable()
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class PositionalEncoding(tf.keras.layers.Layer):
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"""Custom layer per l'encoding posizionale"""
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def __init__(self, d_model, **kwargs):
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super().__init__(**kwargs)
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self.d_model = d_model
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def build(self, input_shape):
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_, seq_length, _ = input_shape
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# Crea la matrice di encoding posizionale
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position = tf.range(seq_length, dtype=tf.float32)[:, tf.newaxis]
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div_term = tf.exp(
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tf.range(0, self.d_model, 2, dtype=tf.float32) *
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(-tf.math.log(10000.0) / self.d_model)
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)
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# Calcola sin e cos
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pos_encoding = tf.zeros((1, seq_length, self.d_model))
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pos_encoding_even = tf.sin(position * div_term)
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pos_encoding_odd = tf.cos(position * div_term)
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# Assegna i valori alle posizioni pari e dispari
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pos_encoding = tf.concat(
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[tf.expand_dims(pos_encoding_even, -1),
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tf.expand_dims(pos_encoding_odd, -1)],
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axis=-1
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)
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pos_encoding = tf.reshape(pos_encoding, (1, seq_length, -1))
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pos_encoding = pos_encoding[:, :, :self.d_model]
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# Salva l'encoding come peso non trainabile
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self.pos_encoding = self.add_weight(
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shape=(1, seq_length, self.d_model),
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initializer=tf.keras.initializers.Constant(pos_encoding),
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trainable=False,
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name='positional_encoding'
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)
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super().build(input_shape)
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def call(self, inputs):
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# Broadcast l'encoding posizionale sul batch
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batch_size = tf.shape(inputs)[0]
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pos_encoding_tiled = tf.tile(self.pos_encoding, [batch_size, 1, 1])
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return inputs + pos_encoding_tiled
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def get_config(self):
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config = super().get_config()
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config.update({"d_model": self.d_model})
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return config
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@keras.saving.register_keras_serializable()
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class WarmUpLearningRateSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
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"""Custom learning rate schedule with linear warmup and exponential decay."""
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def __init__(self, initial_learning_rate=1e-3, warmup_steps=500, decay_steps=5000):
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super().__init__()
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self.initial_learning_rate = initial_learning_rate
|
||||
self.warmup_steps = warmup_steps
|
||||
self.decay_steps = decay_steps
|
||||
|
||||
def __call__(self, step):
|
||||
warmup_pct = tf.cast(step, tf.float32) / self.warmup_steps
|
||||
warmup_lr = self.initial_learning_rate * warmup_pct
|
||||
decay_factor = tf.pow(0.1, tf.cast(step, tf.float32) / self.decay_steps)
|
||||
decayed_lr = self.initial_learning_rate * decay_factor
|
||||
return tf.where(step < self.warmup_steps, warmup_lr, decayed_lr)
|
||||
|
||||
def get_config(self):
|
||||
return {
|
||||
'initial_learning_rate': self.initial_learning_rate,
|
||||
'warmup_steps': self.warmup_steps,
|
||||
'decay_steps': self.decay_steps
|
||||
}
|
||||
|
||||
@keras.saving.register_keras_serializable()
|
||||
def weighted_huber_loss(y_true, y_pred):
|
||||
# Pesi per diversi output
|
||||
weights = tf.constant([1.0, 0.8, 0.8, 1.0, 0.6], dtype=tf.float32)
|
||||
huber = tf.keras.losses.Huber(delta=1.0)
|
||||
loss = huber(y_true, y_pred)
|
||||
weighted_loss = tf.reduce_mean(loss * weights)
|
||||
return weighted_loss
|
||||
|
||||
print("Caricamento modello e scaler...")
|
||||
|
||||
# Verifica che il modello sia disponibile
|
||||
model_path = './sources/olive_oil_transformer/olive_oil_transformer_model.keras'
|
||||
if not os.path.exists(model_path):
|
||||
raise FileNotFoundError(f"Modello non trovato in: {model_path}")
|
||||
|
||||
# Prova a caricare il modello
|
||||
model = tf.keras.models.load_model(model_path, custom_objects={
|
||||
'DataAugmentation': DataAugmentation,
|
||||
'PositionalEncoding': PositionalEncoding,
|
||||
'WarmUpLearningRateSchedule': WarmUpLearningRateSchedule,
|
||||
'weighted_huber_loss': weighted_huber_loss
|
||||
})
|
||||
|
||||
return (
|
||||
dbc.Alert("Modello caricato correttamente", color="success"),
|
||||
"Produzione (Local Model)",
|
||||
"~ 100ms",
|
||||
"0"
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"Errore nel caricamento del modello: {str(e)}")
|
||||
# Se c'è un errore nel caricamento del modello, torna in modalità debug
|
||||
DEV_MODE = True
|
||||
|
||||
# Aggiorna la configurazione per riflettere il fallback
|
||||
config['inference']['debug_mode'] = True
|
||||
try:
|
||||
with open(CONFIG_FILE, 'w') as f:
|
||||
json.dump(config, f, indent=4)
|
||||
except Exception as save_error:
|
||||
print(f"Errore nel salvataggio della configurazione di fallback: {save_error}")
|
||||
|
||||
return (
|
||||
dbc.Alert(f"Errore nel caricamento del modello: {str(e)}", color="danger"),
|
||||
"Debug (Mock) - Fallback",
|
||||
"N/A",
|
||||
"N/A"
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"Errore nella configurazione inferenza: {str(e)}")
|
||||
return (
|
||||
dbc.Alert(f"Errore: {str(e)}", color="danger"),
|
||||
"Errore",
|
||||
"Errore",
|
||||
"Errore"
|
||||
)
|
||||
|
||||
|
||||
def create_economic_analysis_tab():
|
||||
@ -1829,14 +1845,18 @@ def create_costs_config_section():
|
||||
])
|
||||
|
||||
|
||||
def create_sagemaker_config_section():
|
||||
def create_inference_config_section():
|
||||
|
||||
config = load_config()
|
||||
debug_mode = config.get('inference', {}).get('debug_mode', True)
|
||||
|
||||
return dbc.Card([
|
||||
dbc.CardHeader([
|
||||
html.H4("Configurazione SageMaker", className="text-primary mb-0"),
|
||||
html.H4("Configurazione Inferenza", className="text-primary mb-0"),
|
||||
dbc.Switch(
|
||||
id='sagemaker-switch',
|
||||
label="Abilita SageMaker",
|
||||
value=False,
|
||||
id='debug-switch',
|
||||
label="Modalità Debug",
|
||||
value=debug_mode,
|
||||
className="mt-2"
|
||||
),
|
||||
], className="bg-light"),
|
||||
@ -1846,7 +1866,7 @@ def create_sagemaker_config_section():
|
||||
dbc.Col([
|
||||
html.Div([
|
||||
html.H5("Stato Servizio", className="mb-3"),
|
||||
html.Div(id='sagemaker-status', className="mb-3"),
|
||||
html.Div(id='inference-status', className="mb-3"),
|
||||
])
|
||||
])
|
||||
], className="mb-4"),
|
||||
@ -1858,59 +1878,15 @@ def create_sagemaker_config_section():
|
||||
dbc.Form([
|
||||
dbc.Row([
|
||||
dbc.Col([
|
||||
dbc.Label("Memoria (MB):", className="fw-bold"),
|
||||
dbc.Input(
|
||||
id='sagemaker-memory',
|
||||
type='number',
|
||||
min=512,
|
||||
max=6144,
|
||||
step=512,
|
||||
value=2048,
|
||||
className="mb-2"
|
||||
)
|
||||
], md=6),
|
||||
dbc.Col([
|
||||
dbc.Label("Concorrenza massima:", className="fw-bold"),
|
||||
dbc.Input(
|
||||
id='sagemaker-concurrency',
|
||||
type='number',
|
||||
min=1,
|
||||
max=10,
|
||||
value=5,
|
||||
className="mb-2"
|
||||
)
|
||||
], md=6),
|
||||
]),
|
||||
dbc.Row([
|
||||
dbc.Col([
|
||||
dbc.Label("Model URI:", className="fw-bold"),
|
||||
dbc.Input(
|
||||
id='sagemaker-model-uri',
|
||||
type='text',
|
||||
value="s3://sagemaker-oil-transformer/model/saved_model.pb",
|
||||
className="mb-2",
|
||||
disabled=True,
|
||||
dbc.Label("Modello:", className="fw-bold"),
|
||||
# Usa html.Div invece di dbc.Input per il percorso in sola lettura
|
||||
html.Div(
|
||||
"./sources/olive_oil_transformer/olive_oil_transformer_model.keras",
|
||||
id='model-path',
|
||||
className="mb-2 p-2 bg-light border rounded",
|
||||
style={
|
||||
"background-color": "#f8f9fa",
|
||||
"opacity": "1",
|
||||
"cursor": "not-allowed"
|
||||
}
|
||||
)
|
||||
], md=12),
|
||||
]),
|
||||
dbc.Row([
|
||||
dbc.Col([
|
||||
dbc.Label("IAM Role:", className="fw-bold"),
|
||||
dbc.Input(
|
||||
id='sagemaker-role',
|
||||
type='text',
|
||||
value="arn:aws:iam::906312666576:role/sagemaker-olive-oil",
|
||||
className="mb-2",
|
||||
disabled=True,
|
||||
style={
|
||||
"background-color": "#f8f9fa",
|
||||
"opacity": "1",
|
||||
"cursor": "not-allowed"
|
||||
"font-family": "monospace",
|
||||
"font-size": "0.9rem"
|
||||
}
|
||||
)
|
||||
], md=12),
|
||||
@ -1925,16 +1901,16 @@ def create_sagemaker_config_section():
|
||||
html.H5("Metriche", className="mb-3"),
|
||||
dbc.ListGroup([
|
||||
dbc.ListGroupItem([
|
||||
html.Strong("Endpoint: "),
|
||||
html.Span(id='sagemaker-endpoint')
|
||||
html.Strong("Modalità: "),
|
||||
html.Span(id='inference-mode')
|
||||
]),
|
||||
dbc.ListGroupItem([
|
||||
html.Strong("Latenza media: "),
|
||||
html.Span(id='sagemaker-latency')
|
||||
html.Span(id='inference-latency')
|
||||
]),
|
||||
dbc.ListGroupItem([
|
||||
html.Strong("Richieste totali: "),
|
||||
html.Span(id='sagemaker-requests')
|
||||
html.Span(id='inference-requests')
|
||||
])
|
||||
], flush=True)
|
||||
])
|
||||
@ -2593,8 +2569,7 @@ def update_simulation(n_clicks, temp_range, humidity, rainfall, radiation):
|
||||
varieties_info.append(variety_data.iloc[0])
|
||||
percentages.append(variety_config['percentage'])
|
||||
|
||||
print(config['oliveto']['varieties'])
|
||||
print(olive_varieties)
|
||||
print(sim_data)
|
||||
|
||||
prediction = make_prediction(weather_data, varieties_info, percentages, hectares, sim_data)
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user