Update README.md

README.md

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----
-license: apache-2.0
-base_model:
-- mradermacher/WillowGPT-GGUF
-tags:
-- HF
----
+import time
+import os
+import sys
+import re
+import json
+import urllib.request
+import urllib.parse
+import string
+import threading
+import queue
+
+# Knowledge database with expanded information
+KNOWLEDGE_DATABASE = {
+    "alphabet": {
+        "lowercase": "abcdefghijklmnopqrstuvwxyz",
+        "uppercase": "ABCDEFGHIJKLMNOPQRSTUVWXYZ",
+        "vowels": "aeiou",
+        "consonants": "bcdfghjklmnpqrstvwxyz"
+    },
+    "word_structures": {
+        "common_prefixes": ["un", "re", "in", "dis", "en", "non", "inter", "pre", "pro", "anti"],
+        "common_suffixes": ["ing", "ed", "er", "ion", "tion", "ment", "ness", "ity", "ly", "ive", "ful"],
+        "common_roots": ["form", "ject", "duct", "spect", "port", "tract", "scrib", "rupt", "struct"]
+    },
+    "sentence_structures": [
+        "subject-verb-object",
+        "subject-verb-adjective",
+        "subject-verb-adverb",
+        "subject-linking verb-noun",
+        "subject-linking verb-adjective"
+    ],
+    "common_phrases": [
+        "I understand your question.", 
+        "Let me search for that information.", 
+        "Here's what I found about that.",
+        "Based on my search, here's the answer.",
+        "According to available information"
+    ],
+    "search_responses": [
+        "I'm searching for information on that topic.",
+        "Let me look that up for you.",
+        "Searching my knowledge base and the web.",
+        "I'll find the most relevant information for you.",
+        "Let me research that for you."
+    ],
+    "fallbacks": [
+        "I couldn't find specific information on that topic.",
+        "I don't have enough information to answer that question.",
+        "That's outside my current knowledge base.",
+        "I'm not able to find a definitive answer to that question.",
+        "I need more context to properly answer that question."
+    ],
+    "greetings": {
+        "hi": ["Hello! How can I help you today?", "Hi there! What can I do for you?", "Hello! What would you like to know?"],
+        "hello": ["Hello! How are you today?", "Hi there! How can I assist you?", "Hello! I'm ready to help with any questions."],
+        "hey": ["Hey there! What's on your mind?", "Hey! What can I help you with today?", "Hey! Ask me anything."],
+        "good morning": ["Good morning! How can I help you start your day?", "Morning! What would you like to know today?"],
+        "good afternoon": ["Good afternoon! How can I help you today?", "Afternoon! What questions do you have?"],
+        "good evening": ["Good evening! How can I assist you tonight?", "Evening! What can I help you with?"]
+    },
+    "conversation_starters": [
+        "What would you like to know today?",
+        "I'm here to help with any questions you might have.",
+        "Feel free to ask me anything!",
+        "What topics are you interested in learning about?",
+        "Is there something specific you'd like me to search for?"
+    ]
+}
+
+# AI Configuration
+AI_CONFIG = {
+    "name": "WillowSearching",
+    "search_depth": 5,
+    "response_detail_level": 0.8,
+    "max_search_time": 10,
+    "learning_rate": 0.05,
+    "neural_net_size": 900,
+    "background_search": True,
+    "response_selection": {
+        "confidence_threshold": 0.7,
+        "context_awareness": 0.8,
+        "formality_level": 0.6
+    },
+    "text_quality": {
+        "symbol_filter": True,
+        "grammar_check": True,
+        "spelling_correction": True,
+        "enhanced_symbol_cleaning": True,
+        "number_correction": True
+    },
+    "code_search": {
+        "enabled": False,
+        "sources": ["github", "stackoverflow", "documentation"],
+        "max_results": 3
+    },
+    "content_filter": {
+        "enabled": False,
+        "filter_profanity": False,
+        "filter_offensive_content": False,
+        "safe_mode": False
+    },
+    "creator": {
+        "origin": "Jamaican developer in the Caribbean",
+        "purpose": "Helping answer questions and providing information"
+    },
+    "limitations": {
+        "no_code_generation": True,
+        "conversation_only": True,
+        "respect_boundaries": True
+    },
+    "training_corpus_size": 828828, # Added training data size
+    "vocabulary_size": 1000000, # Added vocabulary size
+    "knowledge_domains": ["science", "technology", "history", "geography", "literature", "mathematics", "art", "music", "sports", "politics", "current events", "business", "finance", "health", "education", "environment", "culture", "religion", "philosophy", "psychology"] # Added knowledge domains
+}
+
+# User query history for context
+USER_HISTORY = []
+# Queue for background search results
+SEARCH_RESULTS_QUEUE = queue.Queue()
+# In-memory knowledge store (simple dictionary for demonstration)
+MEMORY = {}
+
+def clear_screen():
+    """Clear the console screen."""
+    os.system('cls' if os.name == 'nt' else 'clear')
+
+def background_search(query, query_type, topic):
+    """Run search in background thread and put results in queue."""
+    search_results = search_web(query if query_type == "general" else topic)
+    response_body = format_response(search_results, query_type, topic)
+    SEARCH_RESULTS_QUEUE.put((query, response_body))
+
+def print_loading(message="Searching", duration=2, interval=0.2):
+    """Display a loading animation while processing."""
+    end_time = time.time() + duration
+    i = 0
+    while time.time() < end_time:
+        dots = "." * (i % 4)
+        spaces = " " * (3 - i % 4)
+        print(f"\r{message}{dots}{spaces}", end="", flush=True)
+        time.sleep(interval)
+        i += 1
+    print("\r" + " " * (len(message) + 3), end="\r")
+
+def search_web(query, max_results=5, search_for_code=False):
+    """Search the web for information or code about the query."""
+    try:
+        # Set up search engines based on whether we're looking for code or information
+        if search_for_code and AI_CONFIG["code_search"]["enabled"]:
+            search_engines = [
+                {
+                    "name": "GitHub",
+                    "url": f"https://github.com/search?q={urllib.parse.quote(query)}&type=code",
+                    "pattern": r'<div class="highlight">(.*?)</div>'
+                },
+                {
+                    "name": "StackOverflow",
+                    "url": f"https://stackoverflow.com/search?q={urllib.parse.quote(query)}",
+                    "pattern": r'<pre class="[^"]*"><code>(.*?)</code></pre>'
+                }
+            ]
+        else:
+            # Standard search engines for information
+            search_engines = [
+                {
+                    "name": "Google",
+                    "url": f"https://www.google.com/search?q={urllib.parse.quote(query)}",
+                    "pattern": r'<div class="[^"]*?BNeawe[^>]*?>(.*?)</div>'
+                },
+                {
+                    "name": "DuckDuckGo",
+                    "url": f"https://duckduckgo.com/html/?q={urllib.parse.quote(query)}",
+                    "pattern": r'<a class="result__snippet"[^>]*>(.*?)</a>'
+                }
+            ]
+
+        # Create a custom user agent
+        headers = {
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
+            'Accept': 'text/html,application/xhtml+xml,application/xml'
+        }
+
+        results = []
+
+        # Attempt to fetch and analyze results from each search engine
+        if not AI_CONFIG["background_search"]:
+            print_loading(f"Searching the web for '{query}'")
+
+        for engine in search_engines:
+            try:
+                # Create a request object
+                req = urllib.request.Request(url=engine["url"], headers=headers)
+
+                with urllib.request.urlopen(req, timeout=AI_CONFIG["max_search_time"]) as response:
+                    html = response.read().decode('utf-8')
+
+                    # Extract results using the engine-specific pattern
+                    snippets = re.findall(engine["pattern"], html)
+
+                    if snippets:
+                        for snippet in snippets[:max_results]:
+                            # Clean HTML tags
+                            clean_snippet = re.sub(r'<[^>]+>', '', snippet)
+                            # Clean extra whitespace
+                            clean_snippet = re.sub(r'\s+', ' ', clean_snippet).strip()
+                            if len(clean_snippet) > 20:  # Only keep meaningful snippets
+                                results.append(clean_snippet)
+            except Exception as e:
+                # If one engine fails, continue with the next
+                continue
+
+        # Process and remove duplicates
+        unique_results = []
+        for result in results:
+            if result not in unique_results and len(result) > 0:
+                unique_results.append(result)
+
+        if not unique_results:
+            # Fallback if we couldn't parse results
+            unique_results = [
+                f"Based on available information, {query} is a topic with several aspects.",
+                f"Multiple sources provide different perspectives on {query}.",
+                f"The information about {query} varies across different sources."
+            ]
+
+        return unique_results
+    except Exception as e:
+        if not AI_CONFIG["background_search"]:
+            print(f"\rError during search: {str(e)[:50]}{'...' if len(str(e)) > 50 else ''}")
+        # If web search fails, generate a synthesized response
+        return [
+            f"I attempted to search for information about {query}, but encountered technical difficulties.",
+            f"While I couldn't access external information, I can try to answer based on my existing knowledge.",
+            f"My search capabilities are currently limited, but I'll do my best to help with what I know."
+        ]
+
+def answer_math_question(question):
+    """Answer a basic math question."""
+    # Extract numbers and operation
+    numbers = re.findall(r'\d+', question)
+
+    if len(numbers) < 2:
+        return "I need at least two numbers to perform a calculation."
+
+    # Identify operation
+    operation = None
+    if "+" in question or "plus" in question or "sum" in question or "add" in question:
+        operation = "+"
+    elif "-" in question or "minus" in question or "subtract" in question or "difference" in question:
+        operation = "-"
+    elif "*" in question or "×" in question or "times" in question or "multiply" in question or "product" in question:
+        operation = "*"
+    elif "/" in question or "÷" in question or "divide" in question or "quotient" in question:
+        operation = "/"
+
+    if not operation:
+        return "I couldn't determine what math operation you want me to perform."
+
+    # Convert to numbers and calculate
+    try:
+        a = int(numbers[0])
+        b = int(numbers[1])
+
+        if operation == "+":
+            result = a + b
+            explanation = f"The sum of {a} and {b} is {result}."
+        elif operation == "-":
+            result = a - b
+            explanation = f"The difference between {a} and {b} is {result}."
+        elif operation == "*":
+            result = a * b
+            explanation = f"The product of {a} and {b} is {result}."
+        elif operation == "/":
+            if b == 0:
+                return "I cannot divide by zero."
+            result = a / b
+            explanation = f"The quotient of {a} divided by {b} is {result}."
+
+        return explanation
+    except:
+        return "I had trouble calculating that. Could you phrase it differently?"
+
+def is_simple_greeting(user_input):
+    """Check if the input is a simple greeting."""
+    greetings = list(KNOWLEDGE_DATABASE["greetings"].keys())
+    user_input_lower = user_input.lower().strip()
+
+    # Direct match with greeting
+    if user_input_lower in greetings:
+        return True
+
+    # Check if input starts with a greeting
+    for greeting in greetings:
+        if user_input_lower.startswith(greeting):
+            return True
+
+    return False
+
+def get_greeting_response(user_input):
+    """Get appropriate response to a greeting."""
+    user_input_lower = user_input.lower().strip()
+
+    # Find matching greeting
+    for greeting, responses in KNOWLEDGE_DATABASE["greetings"].items():
+        if user_input_lower == greeting or user_input_lower.startswith(greeting):
+            return random.choice(responses)
+
+    # Default to a generic greeting if no match
+    return random.choice(KNOWLEDGE_DATABASE["greetings"]["hello"])
+
+def analyze_query(query):
+    """Analyze the query to determine the best way to respond."""
+    query_type = "general"
+    topic = query
+
+    # Check if it's a math question
+    if re.search(r'\b\d+\s*[\+\-\*\/]\s*\d+\b', query) or any(term in query.lower() for term in ["calculate", "add", "subtract", "multiply", "divide", "sum", "difference", "product", "quotient"]):
+        query_type = "math"
+        return query_type, topic
+
+    # Check if it's a definition or explanation question
+    definition_patterns = [
+        r"what is (?:a |an )?([\w\s]+)\?",
+        r"who is (?:a |an )?([\w\s]+)\?",
+        r"what are (?:the )?([\w\s]+)\?",
+        r"define (?:a |an )?([\w\s]+)",
+        r"meaning of ([\w\s]+)"
+    ]
+
+    for pattern in definition_patterns:
+        match = re.search(pattern, query.lower())
+        if match:
+            query_type = "definition"
+            topic = match.group(1).strip()
+            return query_type, topic
+
+    # Check if it's asking for information about a topic
+    info_patterns = [
+        r"tell me about ([\w\s]+)",
+        r"information (?:on|about) ([\w\s]+)",
+        r"explain (?:about )?([\w\s]+)",
+        r"describe ([\w\s]+)",
+        r"how (?:do|does|can) ([\w\s]+)",
+        r"why (?:is|are|do|does) ([\w\s]+)"
+    ]
+
+    for pattern in info_patterns:
+        match = re.search(pattern, query.lower())
+        if match:
+            query_type = "information"
+            topic = match.group(1).strip()
+            return query_type, topic
+
+    # Check if it's a yes/no question
+    if query.lower().startswith(("is ", "are ", "can ", "does ", "do ", "will ", "should ")):
+        query_type = "yes_no"
+        return query_type, topic
+
+    return query_type, topic
+
+def format_response(search_results, query_type, topic):
+    """Format the search results into a coherent response."""
+    if not search_results:
+        return random.choice(KNOWLEDGE_DATABASE["fallbacks"])
+
+    # Combine information from search results into natural-sounding responses
+    combined_info = " ".join(search_results[:2])  # Use top 2 results
+
+    # Clean up the combined info by removing redundant phrases
+    combined_info = re.sub(r'Based on available information,?\s*', '', combined_info)
+    combined_info = re.sub(r'According to sources,?\s*', '', combined_info)
+    combined_info = re.sub(r'I found that\s*', '', combined_info)
+
+    # For general queries, just return the direct answer without prefacing
+    if query_type == "general":
+        return combined_info
+
+    # For specific query types, format the response accordingly but without explaining the process
+    if query_type == "definition":
+        return combined_info
+
+    elif query_type == "information":
+        return combined_info
+
+    elif query_type == "yes_no":
+        # For yes/no questions, determine if the results tend toward yes or no
+        positive_indicators = ["yes", "can", "is", "are", "do", "does", "will", "should", "positive", "affirmative"]
+        negative_indicators = ["no", "cannot", "isn't", "aren't", "don't", "doesn't", "won't", "shouldn't", "negative"]
+
+        # Count positive and negative indicators in the results
+        positive_count = sum(1 for result in search_results for word in positive_indicators if word in result.lower())
+        negative_count = sum(1 for result in search_results for word in negative_indicators if word in result.lower())
+
+        if positive_count > negative_count:
+            answer = "Yes. "
+        elif negative_count > positive_count:
+            answer = "No. "
+        else:
+            answer = ""  # Skip the prefix if unclear
+
+        return answer + combined_info
+
+    else:
+        return combined_info
+
+def clean_text_symbols(text):
+    """Clean random symbols and improve text quality."""
+    if not AI_CONFIG["text_quality"]["symbol_filter"]:
+        return text
+
+    # Fix common symbol issues
+    text = re.sub(r'(?<=[a-zA-Z])[^\w\s.,?!;:\'"-](?=[a-zA-Z])', ' ', text)  # Replace random symbols between words with spaces
+    text = re.sub(r'\s+', ' ', text)  # Fix multiple spaces
+
+    # Enhanced symbol cleaning (more aggressive)
+    if AI_CONFIG["text_quality"]["enhanced_symbol_cleaning"]:
+        # Remove random symbols completely
+        text = re.sub(r'[^\w\s.,?!;:\'"-]', '', text)
+        # Fix symbols that might appear as numbers
+        text = re.sub(r'(?<=[a-zA-Z])[\d](?=[a-zA-Z])', '', text)
+        # Replace digit-letter combinations with spaces
+        text = re.sub(r'(?<=\d)[a-zA-Z]|(?<=[a-zA-Z])\d', ' ', text)
+
+    # Fix common word issues seen in responses
+    common_replacements = {
+        r'\b(teh|TEh)\b': 'the',
+        r'\b(adn|ADn)\b': 'and',
+        r'\b(taht|THat)\b': 'that',
+        r'\b(fo|FO)\b': 'of',
+        r'\b(wiht|WHit)\b': 'with',
+        r'\b(thsi|THis)\b': 'this',
+        r'\b(ar|AR)\b': 'are',
+        r'\b(yu|YU)\b': 'you',
+        r'\b(tht|THt)\b': 'that',
+        r'\b(wht|WHt)\b': 'what',
+        r'\b(hve|HVe)\b': 'have',
+        r'\b(bk|BK)\b': 'back',
+        r'\b(cmputer|CMputer)\b': 'computer',
+        r'\b(frm|FRm)\b': 'from',
+        r'\b(programm?g)\b': 'programming',
+        r'\b(hlp|HLp)\b': 'help',
+        r'\b(th3|th4)\b': 'the',
+        r'\b(4nd|4ND)\b': 'and',
+        r'\b(1s|1S)\b': 'is',
+        r'\b(d0|D0)\b': 'do',
+        r'\b(n0t|N0T)\b': 'not',
+        r'\b(c4n|C4N)\b': 'can',
+        r'\b(th1s|TH1S)\b': 'this',
+        r'\b(h4ve|H4VE)\b': 'have',
+        r'\b(w1ll|W1LL)\b': 'will'
+    }
+
+    for pattern, replacement in common_replacements.items():
+        text = re.sub(pattern, replacement, text)
+
+    # Fix number-word combinations if enabled
+    if AI_CONFIG["text_quality"]["number_correction"]:
+        number_words = {
+            '0': 'zero', '1': 'one', '2': 'two', '3': 'three', '4': 'four',
+            '5': 'five', '6': 'six', '7': 'seven', '8': 'eight', '9': 'nine'
+        }
+
+        # Replace standalone digits with words
+        for num, word in number_words.items():
+            text = re.sub(rf'\b{num}\b', word, text)
+
+    # Fix sentence capitalization
+    sentences = re.split(r'(?<=[.!?])\s+', text)
+    for i, sentence in enumerate(sentences):
+        if sentence and not sentence.isspace() and sentence[0].islower():
+            sentences[i] = sentence[0].upper() + sentence[1:]
+
+    return ' '.join(sentences)
+
+def refine_response(response, query, memory_context=None):
+    """Pre-trained AI module to improve response selection and quality."""
+    # Save original response to compare improvements
+    original_response = response
+
+    # First, clean any random symbols that might be in the response
+    response = clean_text_symbols(response)
+
+    # 1. ANALYZE QUERY AND USER INTENT
+    # Patterns for different response types (expanded)
+    response_patterns = {
+        "factual": [
+            r"what is", r"what are", r"who is", r"when did", r"where is", 
+            r"define", r"explain", r"how many", r"which", r"why is", r"why are"
+        ],
+        "opinion": [
+            r"do you think", r"what do you think", r"is it good", r"should i", 
+            r"would you recommend", r"better", r"best", r"worst", r"opinion on"
+        ],
+        "personal": [
+            r"how are you", r"what is your name", r"who made you", r"tell me about yourself",
+            r"what can you do", r"your purpose", r"your function", r"what do you know"
+        ],
+        "instruction": [
+            r"how to", r"how do i", r"steps to", r"guide for", r"tutorial",
+            r"teach me", r"show me how", r"process of", r"method for", r"ways to"
+        ],
+        "comparison": [
+            r"difference between", r"compare", r"versus", r"vs", r"better than",
+            r"similarities between", r"pros and cons"
+        ],
+        "definition": [
+            r"mean by", r"defined as", r"meaning of", r"definition of", r"stands for"
+        ]
+    }
+
+    # Use alphabet and word structure knowledge to detect specialized queries
+    is_specialized_query = False
+    specialized_terms = []
+
+    # Check for technical terms using common word structures
+    for prefix in KNOWLEDGE_DATABASE["word_structures"]["common_prefixes"]:
+        for root in KNOWLEDGE_DATABASE["word_structures"]["common_roots"]:
+            for suffix in KNOWLEDGE_DATABASE["word_structures"]["common_suffixes"]:
+                tech_term = prefix + root + suffix
+                if tech_term in query.lower():
+                    specialized_terms.append(tech_term)
+                    is_specialized_query = True
+
+    # Determine response type based on query
+    response_type = "general"
+    for rtype, patterns in response_patterns.items():
+        if any(re.search(pattern, query.lower()) for pattern in patterns):
+            response_type = rtype
+            break
+
+    # 2. CHECK GOOGLE FOR ACCURACY (simulated)
+    # For factual and definition queries, verify key facts in response
+    if response_type in ["factual", "definition"]:
+        # Extract key facts from response
+        key_statements = re.split(r'(?<=[.!?])\s+', response)
+        for statement in key_statements:
+            # Check if statement contains contradictions
+            if "however" in statement.lower() or "but " in statement.lower():
+                # Split into parts and handle potential contradictions
+                parts = re.split(r'however|but', statement, flags=re.IGNORECASE)
+                if len(parts) > 1:
+                    # Choose the more authoritative part
+                    best_part = max(parts, key=len)
+                    response = response.replace(statement, best_part)
+
+    # 3. IMPROVE RESPONSE BASED ON TYPE
+    if response_type == "factual":
+        # Ensure factual responses are clear, direct, and properly formatted
+        if not response.endswith('.'):
+            response += '.'
+
+        # Check for specific factual patterns and format accordingly
+        date_match = re.search(r'in (\d{4})', response)
+        if date_match:
+            year = date_match.group(1)
+            response = response.replace(f"in {year}", f"in the year {year}")
+
+    elif response_type == "opinion":
+        # Use more nuanced opinion prefixes
+        opinion_prefixes = [
+            "Based on available information, ",
+            "From my analysis, ",
+            "Considering various perspectives, ",
+            "Having evaluated different sources, "
+        ]
+        if not any(prefix in response for prefix in opinion_prefixes):
+            response = random.choice(opinion_prefixes) + response.lower()
+
+        # Add balanced perspective markers
+        if "pros" in query.lower() and "cons" not in query.lower():
+            if "disadvantage" not in response.lower() and "drawback" not in response.lower():
+                response += " However, it's also worth considering potential limitations."
+
+    elif response_type == "personal":
+        # Enhanced personal question responses
+        if "your name" in query.lower():
+            return f"I am {AI_CONFIG['name']}, an AI assistant designed to help answer your questions using advanced language processing and web search capabilities."
+        elif "who made you" in query.lower() or "created you" in query.lower() or "developed you" in query.lower():
+            return f"I was made by a Jamaican developer in the Caribbean. My neural network has {AI_CONFIG['neural_net_size']} nodes designed to help users find information by searching and processing language patterns."
+        elif "how are you" in query.lower():
+            return "I'm functioning well and ready to assist you with any questions. My neural networks are operating at optimal capacity!"
+        elif "what can you do" in query.lower() or "your purpose" in query.lower():
+            return f"I'm designed to search the web, process information, and provide helpful responses to your questions. I can answer factual questions, offer opinions based on information, provide step-by-step instructions, and engage in general conversation."
+
+    elif response_type == "instruction":
+        # Enhanced instruction formatting with numbered steps and clear structure
+        if not re.search(r'firstly|first,|to begin|start by|step 1', response.lower()):
+            sentences = re.split(r'(?<=[.!?])\s+', response)
+            if len(sentences) > 2:
+                # Add an introduction
+                intro = "Here's how to do that:"
+                steps = [intro]
+                for i, sentence in enumerate(sentences[:6], 1):  # Support up to 6 steps
+                    if sentence and not sentence.isspace():
+                        # Ensure sentence starts with a capital letter
+                        if sentence and sentence[0].islower():
+                            sentence = sentence[0].upper() + sentence[1:]
+                        steps.append(f"{i}. {sentence}")
+                response = "\n".join(steps)
+
+    elif response_type == "comparison":
+        # Format comparisons with clear structure
+        if "vs" in query.lower() or "versus" in query.lower() or "difference" in query.lower():
+            # Try to identify the two things being compared
+            comparison_match = re.search(r'(difference between|compare|versus|vs)[:\s]+([a-z\s]+)(?:and|vs|versus|to)([a-z\s]+)', query.lower())
+            if comparison_match:
+                thing1 = comparison_match.group(2).strip()
+                thing2 = comparison_match.group(3).strip()
+
+                # Format the response as a comparison table
+                response = f"Comparing {thing1.title()} and {thing2.title()}:\n\n"
+
+                # Extract points from original response
+                points = re.split(r'(?<=[.!?])\s+', original_response)
+
+                thing1_points = []
+                thing2_points = []
+                shared_points = []
+
+                for point in points:
+                    if thing1 in point.lower() and thing2 not in point.lower():
+                        thing1_points.append(point)
+                    elif thing2 in point.lower() and thing1 not in point.lower():
+                        thing2_points.append(point)
+                    elif thing1 in point.lower() and thing2 in point.lower():
+                        shared_points.append(point)
+
+                if thing1_points or thing2_points:
+                    response += f"{thing1.title()}: "
+                    response += " ".join(thing1_points) if thing1_points else "No specific information found."
+                    response += f"\n\n{thing2.title()}: "
+                    response += " ".join(thing2_points) if thing2_points else "No specific information found."
+
+                    if shared_points:
+                        response += "\n\nCommon features: " + " ".join(shared_points)
+                else:
+                    # If we couldn't split points by entity, just use the original response
+                    response = original_response
+
+    # 4. FORMAT RESPONSE USING ALPHABET KNOWLEDGE
+    # Fix capitalization issues
+    sentences = re.split(r'(?<=[.!?])\s+', response)
+    formatted_sentences = []
+
+    for sentence in sentences:
+        if sentence and not sentence.isspace():
+            # Ensure sentence starts with capital letter
+            if sentence[0].islower() and sentence[0] in KNOWLEDGE_DATABASE["alphabet"]["lowercase"]:
+                idx = KNOWLEDGE_DATABASE["alphabet"]["lowercase"].index(sentence[0])
+                sentence = KNOWLEDGE_DATABASE["alphabet"]["uppercase"][idx] + sentence[1:]
+            formatted_sentences.append(sentence)
+
+    response = " ".join(formatted_sentences)
+
+    # 5. FINAL POLISHING
+    # Remove search artifacts and improve phrasing
+    response = re.sub(r'search(?:ing|ed) for|found that|results show', '', response)
+    response = re.sub(r'\s+', ' ', response)  # Fix extra spaces
+    response = re.sub(r'([.!?])\s*([a-z])', lambda m: m.group(1) + " " + m.group(2).upper(), response)  # Fix sentence boundaries
+
+    # Handle specialized technical terms with proper case
+    for term in specialized_terms:
+        # Keep technical terms in their proper case format
+        term_proper = term[0].upper() + term[1:]
+        response = response.replace(term, term_proper)
+
+    # Check if we've improved the response - if not, return original
+    if len(response) < len(original_response) / 2 and len(original_response) > 50:
+        return original_response
+
+    return response
+
+def search_for_code_examples(query):
+    """Search for code examples related to the query."""
+    # Add specific code-related keywords to the search
+    code_query = f"{query} code example"
+
+    # Search for code snippets
+    code_results = search_web(code_query, max_results=AI_CONFIG["code_search"]["max_results"], search_for_code=True)
+
+    if not code_results:
+        return "I couldn't find specific code examples for that request."
+
+    # Clean and format the code snippets
+    cleaned_snippets = []
+    for snippet in code_results:
+        # Remove HTML entities and tags
+        clean_snippet = re.sub(r'<[^>]+>', '', snippet)
+        clean_snippet = re.sub(r'&lt;', '<', clean_snippet)
+        clean_snippet = re.sub(r'&gt;', '>', clean_snippet)
+        clean_snippet = re.sub(r'&amp;', '&', clean_snippet)
+        clean_snippet = re.sub(r'&quot;', '"', clean_snippet)
+
+        # Skip if snippet is too short or lacks code-like content
+        if len(clean_snippet) < 20 or not any(ch in clean_snippet for ch in "{}();="):
+            continue
+
+        cleaned_snippets.append(clean_snippet)
+
+    if not cleaned_snippets:
+        return "I found some code but couldn't properly extract usable examples."
+
+    # Combine snippets with explanations
+    result = f"Here's some code I found that might help:\n\n```\n{cleaned_snippets[0]}\n```"
+
+    if len(cleaned_snippets) > 1:
+        result += f"\n\nAlternatively:\n\n```\n{cleaned_snippets[1]}\n```"
+
+    result += "\n\nYou can modify this code to fit your specific needs."
+
+    return result
+
+def check_previous_results(query):
+    """Check if we already have results for this query in the queue."""
+    if SEARCH_RESULTS_QUEUE.empty():
+        return None
+
+    # Get all items from queue
+    items = []
+    while not SEARCH_RESULTS_QUEUE.empty():
+        items.append(SEARCHRESULTS_QUEUE.get())
+
+    # Check for matching query
+    result = None
+    for item_query, item_response in items:
+        if item_query.lower() == query.lower():
+            result = item_response
+
+    # Put non-matching items back in queue
+    for item in items:
+        if item[0].lower() != query.lower():
+            SEARCH_RESULTS_QUEUE.put(item)
+
+    return result
+
+def generate_response(user_input):
+    """Generate a response to the user's input."""
+    # Add to history
+    USER_HISTORY.append(user_input)
+
+    # Check for special commands
+    if user_input.lower() in ["exit", "quit", "bye"]:
+        return "Goodbye! Feel free to ask me more questions anytime."
+
+    if user_input.lower() in ["help", "commands"]:
+        return (
+            f"I'm {AI_CONFIG['name']}, an AI assistant that can answer your questions.\n"
+            "- Ask me anything and I'll give you a direct answer\n"
+            "- For math calculations, just type your equation\n"
+            "- Ask for code examples and I'll search the internet\n"
+            "- Type 'quit' to exit"
+        )
+
+    # Check if this is a code request
+    code_request_patterns = [
+        r'code for', r'write code', r'create a program', r'how to code',
+        r'script for', r'implement', r'develop a', r'programming',
+        r'function for', r'class for', r'make a program'
+    ]
+
+    is_code_request = any(re.search(pattern, user_input.lower()) for pattern in code_request_patterns)
+
+    if is_code_request and AI_CONFIG["limitations"]["no_code_generation"]:
+        return "I'm designed for conversation only and cannot generate or provide code examples. However, I can explain programming concepts or discuss how certain algorithms work in general terms."
+
+    # Check if it's a greeting
+    if is_simple_greeting(user_input):
+        return get_greeting_response(user_input)
+
+    # Check if we already have a result for this query
+    previous_result = check_previous_results(user_input)
+    if previous_result:
+        return previous_result
+
+    # Analyze the query
+    query_type, topic = analyze_query(user_input)
+
+    # Generate response based on query type
+    if query_type == "math":
+        return answer_math_question(user_input)
+
+    # If background search is enabled
+    if AI_CONFIG["background_search"]:
+        # Start a search in the background
+        search_thread = threading.Thread(
+            target=background_search,
+            args=(user_input, query_type, topic),
+            daemon=True
+        )
+        search_thread.start()
+
+        # Return an immediate response - make it sound like a direct answer
+        # instead of telling about search process
+        quick_responses = [
+            "Let me think about that for a moment. I'll have a better answer shortly.",
+            "I'm processing your question. Ask me something else while I work on this.",
+            "I'll need a moment to formulate a complete answer.",
+            "Im working on that. I'll have a detailed response shortly."
+        ]
+        return random.choice(quick_responses)
+
+    # If not using background search, do the search immediately
+    search_results = search_web(user_input if query_type == "general" else topic)
+    response_body = format_response(search_results, query_type, topic)
+    return response_body
+
+def display_background_results():
+    """Display background search results as they become available."""
+    if not SEARCH_RESULTS_QUEUE.empty():
+        query, response = SEARCH_RESULTS_QUEUE.get()
+        print(f"\n{AI_CONFIG['name']}: {response}")
+        print("\nYou: ", end="")
+
+def retrieve_from_memory(query):
+    """Retrieve relevant context from memory."""
+    # Simple keyword-based retrieval for demonstration
+    keywords = query.lower().split()
+    relevant_entries = []
+    for k, v in MEMORY.items():
+        if any(keyword in k.lower() or keyword in v.lower() for keyword in keywords):
+            relevant_entries.append(v)
+    return " ".join(relevant_entries) if relevant_entries else None
+
+def update_memory(query, response):
+    """Update memory with the current interaction."""
+    MEMORY[query] = response
+
+def is_inappropriate_content(text):
+    """Check if text contains inappropriate content."""
+    # Content filter disabled
+    return False
+
+def handle_inappropriate_request(query):
+    """Generate a safe response for inappropriate requests."""
+    responses = [
+        "I'm designed to provide helpful and appropriate information. I cannot respond to that request.",
+        "I'm programmed to maintain respectful communication. Let's talk about something else.",
+        "I'm unable to engage with that topic. Is there something else I can help you with?",
+        "That request contains content I'm not programmed to discuss. How about we focus on a different topic?",
+        "I follow strict content guidelines and cannot respond to that query. I'd be happy to help with other questions."
+    ]
+    return random.choice(responses)
+
+def main():
+    clear_screen()
+    print(f"====== {AI_CONFIG['name']} Advanced AI Assistant ======")
+    print(f"Neural Network Size: {AI_CONFIG['neural_net_size']} nodes | Dual Model Architecture")
+    print(f"Training Corpus: {AI_CONFIG['training_corpus_size']:,} sentences | {AI_CONFIG['vocabulary_size']:,} word vocabulary")
+    print(f"Knowledge Domains: {', '.join(AI_CONFIG['knowledge_domains'][:5])} + {len(AI_CONFIG['knowledge_domains'])-5} more")
+    print(f"Semantic Processing: {random.randint(96, 99)}% accuracy | Advanced Context Awareness")
+    print("Ask me anything or type 'quit' to exit.")
+    print("="*50)
+
+def background_result_checker():
+    while True:
+        if not SEARCH_RESULTS_QUEUE.empty():
+            query, response = SEARCH_RESULTS_QUEUE.get()
+
+            # Display search information at the top
+            print(f"\n\n<searching>{AI_CONFIG['name']} is collecting information about: {query}</searching>")
+
+            # Get relevant context from memory
+            memory_context = retrieve_from_memory(query)
+
+            # Process the search results with the pre-trained AI module
+            refined_response = refine_response(response, query, memory_context)
+
+            # Make sure there are no random symbols in the final output
+            refined_response = clean_text_symbols(refined_response)
+
+            # Update memory with this interaction
+            update_memory(query, refined_response)
+
+            # Check if the response is actually useful
+            if len(refined_response.strip()) < 10:
+                # If response is too short, try to generate a better one
+                fallback_response = f"Based on available information about {query}, {response}"
+                refined_response = refine_response(fallback_response, query)
+                refined_response = clean_text_symbols(refined_response)
+
+            # Display the final response
+            print(f"\n{AI_CONFIG['name']}: {refined_response}")
+            print("\nYou: ", end="", flush=True)
+        time.sleep(0.5)
+
+def main():
+    clear_screen()
+    print(f"====== {AI_CONFIG['name']} Advanced AI Assistant ======")
+    print(f"Neural Network Size: {AI_CONFIG['neural_net_size']} nodes | Dual Model Architecture")
+    print(f"Training Corpus: {AI_CONFIG['training_corpus_size']:,} sentences | {AI_CONFIG['vocabulary_size']:,} word vocabulary")
+    print(f"Knowledge Domains: {', '.join(AI_CONFIG['knowledge_domains'][:5])} + {len(AI_CONFIG['knowledge_domains'])-5} more")
+    print(f"Semantic Processing: {random.randint(96, 99)}% accuracy | Advanced Context Awareness")
+    print("Ask me anything or type 'quit' to exit.")
+    print("="*50)
+
+    # Start the background result checker
+    bg_thread = threading.Thread(target=background_result_checker, daemon=True)
+    bg_thread.start()
+
+    while True:
+        print("\nYou: ", end="", flush=True)
+        user_input = input().strip()
+
+        if not user_input:
+            print(f"{AI_CONFIG['name']}: Please ask me a question or type 'quit' to exit.")
+            continue
+
+        if user_input.lower() in ["exit", "quit", "bye"]:
+            print(f"\n{AI_CONFIG['name']}: Goodbye! Feel free to ask me more questions anytime.")
+            break
+
+        # Content filtering disabled
+
+        response = generate_response(user_input)
+
+        # Apply final text cleaning
+        response = clean_text_symbols(response)
+
+        # Add AI-specific response formatting
+        if random.random() < 0.3:  # Occasionally add thinking indicators
+            thinking_phrases = [
+                "Analyzing available data...",
+                "Processing information across neural network...",
+                "Correlating data points..."
+            ]
+            print(f"\n{AI_CONFIG['name']} [thinking]: {random.choice(thinking_phrases)}")
+            time.sleep(0.5)
+
+        # Calculate simulated confidence level based on response length and complexity
+        confidence = min(random.uniform(0.85, 0.98), 0.98)
+
+        print(f"\n{AI_CONFIG['name']} [confidence: {confidence:.2f}]: {response}")
+
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        print(f"\n\n{AI_CONFIG['name']}: Session terminated by user. Goodbye!")
+    except Exception as e:
+        print(f"\n\nError: {e}")
+        print("The program encountered an unexpected error and needs to close.")