🧠 Self-Learning AI

import numpy as np
import tensorflow as tf
from sklearn.metrics import accuracy_score

class SelfLearningGaugeReader:
    def __init__(self):
        self.base_model = self.load_pretrained_model()
        self.adaptation_rate = 0.01
        self.confidence_threshold = 0.95
        self.feedback_buffer = []
        
    def predict_with_confidence(self, image):
        """Make prediction with confidence score"""
        prediction = self.base_model.predict(image)
        confidence = np.max(tf.nn.softmax(prediction))
        
        return prediction, confidence
    
    def adapt_to_feedback(self, image, true_value, predicted_value):
        """Learn from user corrections"""
        if predicted_value != true_value:
            # Add to feedback buffer
            self.feedback_buffer.append({
                'image': image,
                'true_value': true_value,
                'predicted_value': predicted_value,
                'timestamp': time.time()
            })
            
            # Trigger learning if buffer is full
            if len(self.feedback_buffer) >= 10:
                self.incremental_learning()
    
    def incremental_learning(self):
        """Update model with accumulated feedback"""
        if not self.feedback_buffer:
            return
            
        # Prepare training data from feedback
        X_feedback = np.array([item['image'] for item in self.feedback_buffer])
        y_feedback = np.array([item['true_value'] for item in self.feedback_buffer])
        
        # Fine-tune model with small learning rate
        self.base_model.compile(
            optimizer=tf.keras.optimizers.Adam(lr=self.adaptation_rate),
            loss='sparse_categorical_crossentropy',
            metrics=['accuracy']
        )
        
        # Train for few epochs to avoid overfitting
        self.base_model.fit(
            X_feedback, y_feedback,
            epochs=3,
            batch_size=8,
            verbose=0
        )
        
        # Clear buffer
        self.feedback_buffer = []
        
        print(f"Model updated with {len(X_feedback)} feedback samples")
    
    def meta_learning_adaptation(self, new_gauge_type_data):
        """Quick adaptation to new gauge types using meta-learning"""
        # Few-shot learning for new gauge types
        support_set = new_gauge_type_data[:5]  # Use only 5 examples
        query_set = new_gauge_type_data[5:]
        
        # Meta-learning update
        with tf.GradientTape() as tape:
            support_loss = self.compute_loss(support_set)
            
        gradients = tape.gradient(support_loss, self.base_model.trainable_variables)
        
        # Apply gradients with meta-learning rate
        meta_optimizer = tf.keras.optimizers.Adam(lr=0.001)
        meta_optimizer.apply_gradients(zip(gradients, self.base_model.trainable_variables))
        
        # Test on query set
        query_accuracy = self.evaluate(query_set)
        print(f"Quick adaptation accuracy: {query_accuracy:.3f}")
        
        return query_accuracy
    
    def environmental_adaptation(self, current_conditions):
        """Adapt to environmental changes"""
        temperature = current_conditions.get('temperature', 25)
        humidity = current_conditions.get('humidity', 50)
        light_level = current_conditions.get('light_level', 100)
        
        # Adjust preprocessing based on conditions
        if light_level < 50:  # Low light
            self.preprocessing_params['contrast'] *= 1.2
            self.preprocessing_params['brightness'] += 10
        elif light_level > 200:  # Bright light
            self.preprocessing_params['contrast'] *= 0.9
            self.preprocessing_params['brightness'] -= 5
            
        # Temperature compensation
        if temperature > 40:  # Hot environment
            self.confidence_threshold *= 0.98  # Slightly lower threshold
        elif temperature < 10:  # Cold environment
            self.confidence_threshold *= 1.02  # Slightly higher threshold
    
    def continuous_learning_loop(self):
        """Main loop for continuous learning"""
        while True:
            # Get new image
            image = self.get_latest_image()
            prediction, confidence = self.predict_with_confidence(image)
            
            # Check if we need human verification
            if confidence < self.confidence_threshold:
                true_value = self.request_human_verification(image, prediction)
                self.adapt_to_feedback(image, true_value, prediction)
            
            # Adapt to environmental changes
            current_conditions = self.get_environmental_conditions()
            self.environmental_adaptation(current_conditions)
            
            time.sleep(1)  # Process every second

# Usage
reader = SelfLearningGaugeReader()
reader.continuous_learning_loop()