Create autonomy_engine.py

models/ai_models/autonomy_engine.py

@@ -0,0 +1,150 @@
+# Import necessary libraries
+import os
+import time
+import threading
+import numpy as np
+import tensorflow as tf
+from ai_models import NeuralNetwork
+from ai_models import ComputerVision
+from ai_models import NaturalLanguageProcessing
+from device_drivers import DeviceDriver
+
+# Define a class for the autonomy engine
+class AutonomyEngine:
+    def __init__(self, config_path):
+        # Load configuration from file
+        with open(config_path) as f:
+            config = json.load(f)
+        self.config = config
+
+        # Initialize AI models
+        self.neural_network = NeuralNetwork(input_shape=config['neural_network']['input_shape'],
+                                            output_shape=config['neural_network']['output_shape'],
+                                            hidden_layers=config['neural_network']['hidden_layers'])
+        self.computer_vision = ComputerVision(model_type=config['computer_vision']['model_type'])
+        self.natural_language_processing = NaturalLanguageProcessing(model_type=config['natural_language_processing']['model_type'])
+
+        # Initialize device drivers
+        self.device_drivers = []
+        for device_config in config['device_drivers']:
+            device_driver = DeviceDriver(device_config)
+            self.device_drivers.append(device_driver)
+
+        # Initialize threads
+        self.threads = []
+
+    def start(self):
+        # Start threads for AI models and device drivers
+        self.threads.append(threading.Thread(target=self._run_neural_network))
+        self.threads.append(threading.Thread(target=self._run_computer_vision))
+        self.threads.append(threading.Thread(target=self._run_natural_language_processing))
+        for device_driver in self.device_drivers:
+            self.threads.append(threading.Thread(target=device_driver.run))
+        for thread in self.threads:
+            thread.start()
+
+    def stop(self):
+        # Stop threads for AI models and device drivers
+        for device_driver in self.device_drivers:
+            device_driver.stop()
+        for thread in self.threads:
+            thread.join()
+
+    def _run_neural_network(self):
+        # Train and predict using the neural network model
+        while True:
+            if self.config['neural_network']['train']:
+                # Load data from file
+                X_train = np.load(self.config['neural_network']['train_data_path'])
+                y_train = np.load(self.config['neural_network']['train_labels_path'])
+                X_test = np.load(self.config['neural_network']['test_data_path'])
+                y_test = np.load(self.config['neural_network']['test_labels_path'])
+
+                # Train the neural network model
+                self.neural_network.train(X_train, y_train, X_test, y_test)
+
+                # Save the trained model to file
+                self.neural_network.model.save(self.config['neural_network']['model_path'])
+
+                # Update the configuration
+                self.config['neural_network']['train'] = False
+
+            if self.config['neural_network']['predict']:
+                # Load data from file
+                X = np.load(self.config['neural_network']['predict_data_path'])
+
+                # Make predictions using the trained model
+                y_pred = self.neural_network.predict(X)
+
+                # Save the predictions to file
+                np.save(self.config['neural_network']['predictions_path'], y_pred)
+
+                # Update the configuration
+                self.config['neural_network']['predict'] = False
+
+            time.sleep(1)
+
+    def _run_computer_vision(self):
+        # Detect faces and objects using the computer vision model
+        while True:
+            if self.config['computer_vision']['detect_faces']:
+                # Load image from file
+                image = cv2.imread(self.config['computer_vision']['image_path'])
+
+                # Detect faces in the image
+                faces = self.computer_vision.detect_faces(image)
+
+                # Save the faces to file
+                cv2.imwrite(self.config['computer_vision']['faces_path'], image)
+
+                # Update the configuration
+                self.config['computer_vision']['detect_faces'] = False
+
+            if self.config['computer_vision']['detect_objects']:
+                # Load image from file
+                image = cv2.imread(self.config['computer_vision']['image_path'])
+
+                # Detect objects in the image
+                objects = self.computer_vision.detect_objects(image)
+
+                # Save the objects to file
+                np.save(self.config['computer_vision']['objects_path'], objects)
+
+                # Update the configuration
+                self.config['computer_vision']['detect_objects'] = False
+
+            time.sleep(1)
+
+    def _run_natural_language_processing(self):
+        # Recognize entities and analyze sentiment using the natural language processing model
+        while True:
+            if self.config['natural_language_processing']['recognize_entities']:
+                # Load text from file
+                text = open(self.config['natural_language_processing']['text_path']).read()
+
+                # Recognize named entities in the text
+                entities = self.natural_language_processing.recognize_entities(text)
+
+                # Save the entities to file
+                with open(self.config['natural_language_processing']['entities_path'], 'w') as f:
+                    for entity in entities:
+                        f.write(f'{entity[0]} {entity[1]} {entity[2]}\n')
+
+                # Update the configuration
+                self.config['natural_language_processing']['recognize_entities'] = False
+
+            if self.config['natural_language_processing']['analyze_sentiment']:
+                # Load text from file
+                text = open(self.config['natural_language_processing']['text_path']).read()
+
+                # Analyze the sentiment of the text
+                sentiment = self.natural_language_processing.analyze_sentiment(text)
+
+                # Save the sentiment to file
+                with open(self.config['natural_language_processing']['sentiment_path'], 'w') as f:
+                    f.write(f'{sentiment}\n')
+
+                # Update the configuration
+                self.config['natural_language_processing']['analyze_sentiment'] = False
+
+            time.sleep(1)