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import joblib
import numpy as np
import tensorflow as tf
from .llm import LLM
from .config import *
from .exception import log_exception, ModelLoadingError, PreprocessingError, PredictionError
from .logger import get_logger
logger = get_logger(__name__)
class DataPreprocessor:
def __init__(self):
try:
self.scaler = joblib.load(f"{MODELS_DIR}/scaler_object.joblib")
logger.info("DataPreprocessor initialized with scaler.")
except Exception as e:
log_exception(e, "Error loading scaler in DataPreprocessor.")
raise ModelLoadingError("Could not load the scaler for data preprocessing.") from e
def preprocess(self, input_data):
try:
scaled_data = self.scaler.transform(np.array(input_data).reshape(1, -1))
logger.info("Data preprocessing completed successfully.")
return scaled_data
except Exception as e:
log_exception(e, "Error in preprocessing data in DataPreprocessor.")
raise PreprocessingError("Preprocessing failed. Ensure input data format is correct.") from e
class ML_Model_Predictor:
def __init__(self):
try:
self.model = joblib.load(f"{MODELS_DIR}/randomforest_best_model.pkl")
logger.info("ML model loaded successfully.")
except Exception as e:
log_exception(e, "Failed to load ML model in ML_Model_Predictor.")
raise ModelLoadingError("Could not load ML model. Please check model path and format.") from e
def predict(self, preprocessed_data):
try:
prediction = self.model.predict(preprocessed_data)
logger.info("ML model prediction completed successfully.")
return prediction[0]
except Exception as e:
log_exception(e, "Error during ML model prediction in ML_Model_Predictor.")
raise PredictionError("Prediction failed. Ensure input data format is correct.") from e
class CNN_Model_Predictor:
def __init__(self):
try:
self.model = tf.keras.models.load_model(f"{MODELS_DIR}/mobilenet_lumpy_skin_model.h5")
logger.info("CNN model loaded successfully.")
except Exception as e:
log_exception(e, "Failed to load CNN model in CNN_Model_Predictor.")
raise ModelLoadingError("Could not load CNN model. Please check model path and format.") from e
def predict(self, image):
try:
image = image.resize((224, 224))
image_array = np.array(image) / 255.0
image_array = np.expand_dims(image_array, axis=0)
image_array = tf.keras.applications.mobilenet_v2.preprocess_input(image_array)
prediction = self.model.predict(image_array)
logger.info("CNN model prediction completed successfully.")
return np.argmax(prediction, axis=1)[0]
except Exception as e:
log_exception(e, "Error during CNN model prediction in CNN_Model_Predictor.")
raise PredictionError("CNN prediction failed. Check image input format.") from e
def prediction(image, longitude, latitude, cloud_cover, evapotranspiration, precipitation, min_temp, mean_temp, max_temp, vapour_pressure, wet_day_freq):
try:
# Initialize classes
preprocessor = DataPreprocessor()
ml_predictor = ML_Model_Predictor()
cnn_predictor = CNN_Model_Predictor()
llm = LLM()
# Prepare structured data input for ML model
structured_data = [longitude, latitude, cloud_cover, evapotranspiration, precipitation, min_temp, mean_temp, max_temp, vapour_pressure, wet_day_freq]
preprocessed_data = preprocessor.preprocess(structured_data)
# Get predictions from ML and CNN models
ml_prediction = ml_predictor.predict(preprocessed_data)
cnn_prediction = cnn_predictor.predict(image)
result = f"""
Lumpy Skin Disease Diagnostic Report:
**ML Model Prediction:** {'Lumpy' if ml_prediction == 1 else 'Not Lumpy'}
**CNN Model Prediction:** {'Lumpy' if cnn_prediction == 1 else 'Not Lumpy'}
**Input Data:**
- Longitude: {longitude}
- Latitude: {latitude}
- Monthly Cloud Cover: {cloud_cover}
- Potential EvapoTranspiration: {evapotranspiration}
- Precipitation: {precipitation}
- Minimum Temperature: {min_temp}
- Mean Temperature: {mean_temp}
- Maximum Temperature: {max_temp}
- Vapour Pressure: {vapour_pressure}
- Wet Day Frequency: {wet_day_freq}
"""
# Generate LLM report
report = llm.inference(image=image, result=result)
logger.info("LLM report generated successfully.")
return report
except Exception as e:
log_exception(e, "Error in prediction function.")
raise PredictionError("Prediction function encountered an error. Check inputs and model paths.") from e
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