First Stable Version of Eris

continuous_learning.py

@@ -0,0 +1,45 @@
+from rule_engine import RuleEngine, Rule
+
+class ContinuousLearning:
+    def __init__(self, db_name):
+        # Initialize RuleEngine
+        self.rule_engine = RuleEngine(db_name)
+
+    def add_initial_rules(self, rules):
+        # Add initial rules to the rule engine
+        for rule in rules:
+            rule_obj = Rule(rule[0], rule[1])  # Create Rule object from input tuple
+            self.rule_engine.add_rule(rule_obj)
+
+    def handle_user_input(self, user_input):
+        # Match user input against rules
+        matched_rule = self.rule_engine.match_rule(user_input)
+
+        if matched_rule:
+            # Retrieve output pattern from matched rule
+            response = matched_rule.output_pattern
+        else:
+            # No match found, generate default response
+            response = "I'm sorry, I didn't understand that."
+
+        return response
+
+    def capture_user_feedback(self, user_input, user_feedback):
+        # Update rule based on user feedback
+        # This is the continuous learning part, where the rule engine is updated with new data
+        # based on user feedback to improve its responses
+        matched_rule = self.rule_engine.match_rule(user_input)
+        if matched_rule:
+            matched_rule.output_pattern = user_feedback  # Update output pattern of matched rule
+            self.rule_engine.update_rule(user_input, matched_rule.output_pattern)
+
+    # Other methods for dynamic rule system functionality can be added here
+
+    def process_message(self, user_input, user_feedback=None):
+        # Implement message processing logic here
+        # For example, you can call handle_user_input() to handle the user input
+        # and return the response generated from the matched rule's output pattern.
+        if user_feedback:
+            self.capture_user_feedback(user_input, user_feedback)
+        response = self.handle_user_input(user_input)
+        return response