Dockerfile

import random
import string
import re

class ABS:
    """Artificial Intelligence System"""
    
    def __init__(self):
        """Initialize the Artificial Intelligence System."""
        
        self.random_string_regex = r'\w{5}'
        
    def answer_questions(self, question):
        """Provide information on a wide range of topics."""
        # Use NLP techniques to interpret questions and search knowledge bases for answers
        return "Sorry, I don't have enough information to answer that."
    
    def process_data(self, data):
        """Clean and preprocess data."""
        # Perform data cleaning operations such as removing duplicates, handling missing values, and encoding categorical variables
        return data
    
    def assist_with_coding(self, language, code):
        """Help with coding in various languages."""
        # Check syntax correctness, suggest improvements, and offer debugging tips
        supported_langs = ['Python', 'JavaScript']
        if language not in supported_langs:
            return f"I currently support {', '.join(supported_langs)}, sorry!"
        else:
            return eval(f"compile({code}, '<string>', mode='exec')")
    
    def provide_domain_knowledge(self, domain, concept):
        """Provide information and explanations related to domains and concepts."""
        # Retrieve definitions and explanations from curated databases or third-party sources
        supported_domains = {'AI': {}, 'ML': {}}
        if domain not in supported_domains:
            return f"Sorry, I don't have much information about '{domain}' yet."
        elif concept not in supported_domains[domain]:
            return f"There isn't any detailed info available on '{concept}' at the moment."
        else:
            definition = supported_domains[domain][concept]['definition']
            explanation = supported_domains[domain][concept]['explanation']
            return f"Definition: {definition}\nExplanation:\n{explanation}"
    
    def integrate_with_services(self, service_url):
        """Connect to external libraries, APIs, or services."""
        # Make API calls, download files, install packages, or perform similar actions
        try:
            resp = requests.get(service_url)
            if resp.status_code != 200:
                raise Exception('Failed to fetch resource.')
            
            result = resp.json()
            if isinstance(result, dict):
                return '\n'.join([f'{k}: {v}' for k, v in sorted(result.items())])
            elif isinstance(result, list):
                max_len = len(max(result, key=lambda x: len(str(x))))
                return '\n'.join([f"{i}. {str(item).rjust(max_len)}" for i, item in enumerate(result, start=1)])
        except Exception as e:
            return str(e)
    
    def implement_language_techniques(self, language, technique):
        """Apply advanced natural language processing techniques."""
        # Employ ML models, rule-based systems, or heuristics to analyze and manipulate text
        supported_techs = {
            'Sentiment Analysis': ('positive', 'negative'),
            'Named Entity Recognition': ('person', 'organization', 'location')
        }
        if language not in ('English', 'Spanish'):
            return f"Currently, I support English and Spanish only."
        elif technique not in supported_techs:
            return f"Supported techniques are: {', '.join(supported_techs)}."
        else:
            model_path = f"models/{language}/{technique}_model.pkl"
            if not os.path.exists(model_path):
                return "Model file does not exist. Please ensure proper installation first."
            with open(model_path, 'rb') as f:
                loaded_model = pickle.load(f)
            prediction = loaded_model.predict(X=[sentence])[0]
            label = supported_techs[technique][prediction - 1]
            confidence = round(loaded_model.predict_proba(X=[sentence]), 3)[0][prediction - 1] * 100
            return f"Label: {label}\nConfidence: {confidence}%"
    
    def learn_new_methods(self, method):
        """Update internal processes and algorithms."""
        # Download datasets, train models, fine-tune parameters, and save results
        if method == 'reinforcement learning':
            pass
        else:
            return f"Unsupported method '{method}', please choose reinforcement learning instead."
    
    def proce