please fix this script dont weaken code or remove code only enhance code import sys
import os
import asyncio
import json
import subprocess
import ast
import re
import time
from datetime import datetime
from dotenv import load_dotenv
from PyQt6.QtWidgets import (
    QApplication, QWidget, QVBoxLayout, QPushButton, QFileDialog,
    QTextEdit, QProgressBar, QMessageBox, QLabel, QTabWidget, QHBoxLayout, QLineEdit
)
from PyQt6.QtCore import Qt, QThread, pyqtSignal
import aiohttp
from groq import Groq
from bs4 import BeautifulSoup
import toml
from aiolimiter import AsyncLimiter
import torch
from transformers import GPTNeoForCausalLM, GPT2Tokenizer
import nltk
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.cluster import KMeans
import spacy
from transformers import BertModel, BertTokenizer
from gensim.models import LdaMulticore
from gensim.corpora import Dictionary
from textblob import TextBlob
from functools import lru_cache
import numpy as np

# Load environment variables from .env file
load_dotenv()

# Retrieve environment variables
GROQ_API_KEY = os.getenv('GROQ_API_KEY')
PYTHONPATH = os.getenv('PYTHONPATH')
GROQ_MODEL = os.getenv('GROQ_MODEL')
GPT_NEO_MODEL_NAME = os.getenv('GPT_NEO_MODEL', 'EleutherAI/gpt-neo-2.7B')
MODEL_PATH = os.getenv('MODEL_PATH')
KNOWLEDGE_PATH = os.getenv('KNOWLEDGE_PATH', 'solana_knowledge_base.json')
MEMORY_PATH = os.getenv('MEMORY_PATH', 'memory.json')

# Initialize the Groq client
groq_client = Groq(api_key=GROQ_API_KEY)
groq_model = GROQ_MODEL

# Initialize Rate Limiters
tokens_per_minute = 5000
calls_per_minute = 30
token_limiter = AsyncLimiter(tokens_per_minute, 60)
call_limiter = AsyncLimiter(calls_per_minute, 60)

# Download necessary NLTK data
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')

# Load Spacy model
nlp = spacy.load('en_core_web_sm')

# Load configuration
if not os.path.exists('config.toml'):
    print("config.toml not found. Please create one with necessary configurations.")
    sys.exit(1)

with open('config.toml', 'r', encoding='utf-8') as f:
    config = toml.load(f)

# Load Solana knowledge base
def load_knowledge_base():
    if os.path.exists(KNOWLEDGE_PATH):
        with open(KNOWLEDGE_PATH, 'r', encoding='utf-8') as f:
            return json.load(f)
    else:
        return {}

knowledge_base = load_knowledge_base()

# Load or initialize memory for chat logs
def load_memory():
    if os.path.exists(MEMORY_PATH):
        with open(MEMORY_PATH, 'r', encoding='utf-8') as f:
            return json.load(f)
    else:
        return []

def save_memory(memory):
    with open(MEMORY_PATH, 'w', encoding='utf-8') as f:
        json.dump(memory, f, indent=2)

memory = load_memory()

# Load local GPT Neo model
print("Loading local GPT Neo 2.7B model... This may take a while.")
tokenizer = GPT2Tokenizer.from_pretrained(GPT_NEO_MODEL_NAME)
model = GPTNeoForCausalLM.from_pretrained(GPT_NEO_MODEL_NAME)
model.eval()
if torch.cuda.is_available():
    model.to('cuda')
print("Local GPT Neo model loaded successfully.")

# Define the LegendaryNaturalLanguageProcessor class
class LegendaryNaturalLanguageProcessor:
    def __init__(self):
        self.stop_words = set(stopwords.words('english'))
        self.lemmatizer = WordNetLemmatizer()
        self.vectorizer = TfidfVectorizer()

        # Initialize BERT model
        self.bert_model = BertModel.from_pretrained('bert-base-uncased')
        self.bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

        # Initialize GPT-Neo model
        self.gpt_neo_model_path = os.getenv("MODEL_PATH")
        self.gpt_neo_model = GPTNeoForCausalLM.from_pretrained(self.gpt_neo_model_path)
        self.gpt_neo_tokenizer = GPT2Tokenizer.from_pretrained(self.gpt_neo_model_path)
        self.gpt_neo_tokenizer.pad_token = self.gpt_neo_tokenizer.eos_token
        self.gpt_neo_model.config.pad_token_id = self.gpt_neo_model.config.eos_token_id

        # Initialize Groq client
        self.groq_client = Groq(api_key=os.getenv("GROQ_API_KEY"))
        self.groq_model = "mixtral_8x7"  # Assuming this is the model name

    def preprocess_text(self, text):
        doc = nlp(text.lower())
        return ' '.join([token.lemma_ for token in doc if token.is_alpha and token.lemma_ not in self.stop_words])

    def extract_keywords(self, text, num_keywords=5):
        preprocessed_text = self.preprocess_text(text)
        tfidf_matrix = self.vectorizer.fit_transform([preprocessed_text])
        feature_names = self.vectorizer.get_feature_names_out()
        sorted_items = sorted(zip(tfidf_matrix.tocsc().data, feature_names), reverse=True)
        return [item[1] for item in sorted_items[:num_keywords]]

    @lru_cache(maxsize=1000)
    def get_sentence_embedding(self, text):
        inputs = self.bert_tokenizer(text, return_tensors='pt', truncation=True, padding=True)
        outputs = self.bert_model(**inputs)
        return outputs.last_hidden_state.mean(dim=1).squeeze().detach().numpy()

    def calculate_similarity(self, text1, text2):
        embedding1 = self.get_sentence_embedding(text1)
        embedding2 = self.get_sentence_embedding(text2)
        return cosine_similarity([embedding1], [embedding2])[0][0]

    def extract_entities(self, text):
        doc = nlp(text)
        entities = {ent.label_: ent.text for ent in doc.ents}
        return entities

    def summarize_text(self, text, num_sentences=3):
        sentences = sent_tokenize(text)
        embeddings = np.array([self.get_sentence_embedding(sent) for sent in sentences])
        sentence_scores = np.sum(embeddings, axis=1)
        top_sentence_indices = sentence_scores.argsort()[-num_sentences:][::-1]
        summary = ' '.join([sentences[i] for i in sorted(top_sentence_indices)])
        return summary

    def analyze_sentiment(self, text):
        blob = TextBlob(text)
        sentiment_score = blob.sentiment.polarity
        if sentiment_score > 0.1:
            return "Positive"
        elif sentiment_score < -0.1:
            return "Negative"
        else:
            return "Neutral"

    def generate_text(self, prompt, max_length=100):
        input_ids = self.gpt_neo_tokenizer.encode(prompt, return_tensors='pt')
        output = self.gpt_neo_model.generate(input_ids, max_length=max_length, num_return_sequences=1, no_repeat_ngram_size=2)
        return self.gpt_neo_tokenizer.decode(output[0], skip_special_tokens=True)

    def use_groq_api(self, data):
        # Example of using Groq API
        response = self.groq_client.query(self.groq_model, data)
        return response

# Initialize the NLP processor
nlp_processor = LegendaryNaturalLanguageProcessor()

# Web crawling function
async def web_crawl(url):
    try:
        async with aiohttp.ClientSession() as session:
            async with session.get(url, timeout=30) as response:
                if response.status == 200:
                    content = await response.text()
                    soup = BeautifulSoup(content, 'html.parser')
                    return soup.get_text()
    except Exception as e:
        return f"Error fetching {url}: {str(e)}"
    return None

# Function to get URLs for missing knowledge using the knowledge base
async def get_urls_for_missing_knowledge(topic):
    urls = knowledge_base.get(topic, [])
    if not urls:
        return []
    return urls

# Function to learn from URLs
async def learn_from_urls(topic):
    urls = await get_urls_for_missing_knowledge(topic)
    new_knowledge = ""
    for url in urls:
        content = await web_crawl(url)
        if content:
            new_knowledge += f"\n\nFrom {url}:\n{content}"
    return new_knowledge

# Function to update knowledge base
def update_knowledge_base(topic, new_knowledge):
    knowledge_base[topic] = knowledge_base.get(topic, "") + new_knowledge
    with open(KNOWLEDGE_PATH, 'w', encoding='utf-8') as f:
        json.dump(knowledge_base, f, indent=2)

# Functions to manage memory.json
def append_to_memory(entry):
    memory.append(entry)
    if len(memory) > 200:
        memory.pop(0)  # Remove the oldest entry to keep memory size at 200
    save_memory(memory)

# Function to analyze a file
def analyze_file(file_path):
    with open(file_path, 'r', encoding='utf-8') as file:
        content = file.read()

    analysis = {
        'file_name': os.path.basename(file_path),
        'content': content,
        'imports': [],
        'functions': [],
        'structs': [],
    }

    if file_path.endswith('.py'):
        try:
            tree = ast.parse(content)
            for node in ast.walk(tree):
                if isinstance(node, ast.Import):
                    for n in node.names:
                        analysis['imports'].append(n.name)
                elif isinstance(node, ast.ImportFrom):
                    for n in node.names:
                        analysis['imports'].append(f"{node.module}.{n.name}")
                elif isinstance(node, ast.FunctionDef):
                    analysis['functions'].append(node.name)
                elif isinstance(node, ast.ClassDef):
                    analysis['structs'].append(node.name)
        except SyntaxError:
            pass
    elif file_path.endswith('.rs'):
        for line in content.split('\n'):
            if line.strip().startswith('use '):
                analysis['imports'].append(line.strip())
            elif line.strip().startswith('fn '):
                match = re.match(r'fn\s+(\w+)', line.strip())
                if match:
                    analysis['functions'].append(match.group(1))
            elif line.strip().startswith('struct '):
                match = re.match(r'struct\s+(\w+)', line.strip())
                if match:
                    analysis['structs'].append(match.group(1))

    return analysis

# Worker class for handling background tasks
class Worker(QThread):
    progress = pyqtSignal(int)
    log = pyqtSignal(str)
    finished = pyqtSignal()

    def __init__(self, folder):
        super().__init__()
        self.folder = folder

    def run(self):
        files = [f for f in os.listdir(self.folder) if f.endswith('.py') or f.endswith('.rs')]
        total_files = len(files)
        for i, file in enumerate(files):
            file_path = os.path.join(self.folder, file)
            analysis = analyze_file(file_path)
            self.log.emit(f"Analyzed {file}")
            self.progress.emit(int((i + 1) / total_files * 100))
            time.sleep(1)  # Simulate time-consuming analysis
        self.finished.emit()

# Function to make GROQ API requests with rate limit handling and fallback to local model
async def process_request_with_fallback(messages, max_retries=5):
    for attempt in range(max_retries):
        try:
            async with call_limiter:
                async with token_limiter:
                    response = groq_client.chat.completions.create(
                        model=groq_model,
                        messages=messages,
                        max_tokens=2000  # Adjust as needed
                    )
                    if response and response.choices:
                        return response.choices[0].message.content
                    else:
                        return None
        except Exception as e:
            error_message = str(e)
            if 'rate_limit_exceeded' in error_message or 'Rate limit reached' in error_message:
                print(f"Groq rate limit exceeded: {error_message}. Switching to local model.")
                return await generate_with_local_model(messages)
            else:
                print(f"Error during GROQ request: {error_message}. Retrying in 30 seconds...")
                await asyncio.sleep(30)
    print("Max retries exceeded for GROQ API.")
    return await generate_with_local_model(messages)

# Function to generate response using local GPT Neo model
async def generate_with_local_model(messages):
    prompt = ""

    for msg in messages:
        if msg["role"] == "system":
            prompt += f"{msg['content']}\n"
        elif msg["role"] == "user":
            prompt += f"User: {msg['content']}\n"
        elif msg["role"] == "assistant":
            prompt += f"AI: {msg['content']}\n"
    prompt += "AI:"

    # Tokenize and count tokens
    input_ids = tokenizer.encode(prompt, return_tensors="pt")
    max_total_tokens = 8000  # Increased token limit for larger outputs

    # Determine if the prompt needs to be split into chunks
    if input_ids.shape[1] > max_total_tokens:
        # Split the prompt into manageable chunks
        chunks = split_into_chunks(prompt, max_total_tokens - 100)  # Leave room for the response
    else:
        chunks = [prompt]

    responses = []

    for chunk in chunks:
        inputs = tokenizer(chunk, return_tensors="pt")
        if torch.cuda.is_available():
            inputs = {k: v.to('cuda') for k, v in inputs.items()}
        
        with torch.no_grad():
            outputs = model.generate(
                **inputs,
                max_length=inputs['input_ids'].shape[1] + 500,  # Allow more space for the response
                do_sample=True,
                temperature=0.7
            )
        
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        # Remove the prompt part from the response to get only the generated text
        response = response[len(chunk):].strip()
        responses.append(response)

    return "\n".join(responses)

# Function to split text into manageable chunks
def split_into_chunks(text, max_tokens):
    tokens = tokenizer.encode(text)
    chunks = []

    for i in range(0, len(tokens), max_tokens):
        chunk = tokenizer.decode(tokens[i:i + max_tokens], skip_special_tokens=True)
        chunks.append(chunk)
    return chunks

# Function to enhance code using Groq API or local model
async def enhance_code(analysis, knowledge_base):
    prompt = (
        f"Enhance the following {'Rust' if analysis['file_name'].endswith('.rs') else 'Python'} code for a Solana Sniper Bot:\n\n"
        f"{analysis['content']}\n\n"
        f"File analysis:\n{json.dumps(analysis, indent=2)}\n\n"
        f"Solana knowledge base:\n{json.dumps(knowledge_base, indent=2)}\n\n"
        "Enhance the code by:\n"
        "1. Improving error handling\n"
        "2. Optimizing performance\n"
        "3. Ensuring best practices for Solana development\n"
        "4. Adding comments for clarity\n"
        "5. Implementing any missing functionality based on the file analysis\n"
        "6. Completing any unfinished functions or code blocks\n\n"
        "Important rules:\n"
        "- Do NOT remove or alter any existing functions\n"
        "- Do NOT weaken or remove any existing functionality\n"
        "- Only enhance, complete, or optimize existing code\n"
        "- If a function is incomplete, complete it based on its apparent purpose\n"
        "- Ensure all existing imports, functions, and structs are preserved\n\n"
        "Use the Solana knowledge base and official documentation as references for accurate and working code.\n"
        "Do not include any hard-coded keys or sensitive information.\n"
        "Provide the enhanced code without any explanations."
    )
    messages = [
        {"role": "system", "content": "You are an expert Solana developer."},
        {"role": "user", "content": prompt}
    ]
    return await process_request_with_fallback(messages)

# Function to run and test the code
# Function to run and test the code
def run_and_test_code(file_path):
    try:
        if file_path.endswith('.py'):
            result = subprocess.run(['python', file_path], capture_output=True, text=True, timeout=30)
        elif file_path.endswith('.rs'):
            project_dir = os.path.dirname(file_path)
            cargo_toml_path = os.path.join(project_dir, 'Cargo.toml')
            if not os.path.exists(cargo_toml_path):
                return False, "", f"Cargo.toml not found in {project_dir}"
            result = subprocess.run(['cargo', 'test', '--manifest-path', cargo_toml_path], capture_output=True, text=True, timeout=180)
        return result.returncode == 0, result.stdout, result.stderr
    except subprocess.TimeoutExpired:
        return False, "", "Code execution timed out"
    except Exception as e:
        return False, "", str(e)

# Function to fix failing code using Groq API or local model
async def fix_code(file_path, error_message, original_analysis):
    with open(file_path, 'r', encoding='utf-8') as f:
        content = f.read()
    prompt = (
        f"The following {'Rust' if file_path.endswith('.rs') else 'Python'} code is not working correctly:\n\n"
        f"{content}\n\n"
        f"Error message:\n{error_message}\n\n"
        f"Original file analysis:\n{json.dumps(original_analysis, indent=2)}\n\n"
        "Please fix the code to resolve the error and ensure it runs correctly.\n"
        "Important rules:\n"
        "- Do NOT remove or alter any existing functions\n"
        "- Do NOT weaken or remove any existing functionality\n"
        "- Only fix, enhance, or complete existing code\n"
        "- Ensure all original imports, functions, and structs are preserved\n"
        "- If a function is incomplete, complete it based on its apparent purpose\n\n"
        "Use the Solana knowledge base and official documentation as references for accurate and working code.\n"
        "Provide only the corrected code without any explanations."
    )
    messages = [
        {"role": "system", "content": "You are an expert Solana developer."},
        {"role": "user", "content": prompt}
    ]
    fixed_content = await process_request_with_fallback(messages)
    return fixed_content
# Function to check if more knowledge is needed using Groq API or local model
async def needs_more_knowledge(code):
    prompt = (
        f"Given the following code for a Solana Sniper Bot:\n\n{code}\n\n"
        "Do you need more knowledge to further improve this code? Answer with 'Yes' or 'No'.\n"
        "If 'Yes', also provide the specific topic you need more information about."
    )
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": prompt}
    ]
    response = await process_request_with_fallback(messages)
    if response:
        response = response.strip().lower()
        if response.startswith('yes'):
            # Extract topic if provided
            match = re.search(r'yes(?:,|\s+about\s+)?(.+)', response)
            if match:
                topic = match.group(1).strip()
            else:
                topic = "Solana development"
            return True, topic
    return False, None

# Function to handle AI Chat messages with chunking and memory
async def handle_ai_chat(message):
    # Convert large messages into single-line messages
    message = ' '.join(message.split())

    # Append user message to memory
    append_to_memory({"role": "user", "content": message})

    # Prepare the conversation history for context
    conversation_history = ""

    for entry in memory[-100:]:  # Use the last 100 messages for context
        if entry["role"] == "user":
            conversation_history += f"User: {entry['content']}\n"
        elif entry["role"] == "assistant":
            conversation_history += f"AI: {entry['content']}\n"

    # Split the message into manageable chunks if it's too large
    message_chunks = split_into_chunks(message, 2048)  # Increased chunk size for larger messages

    responses = []

    for chunk in message_chunks:
        messages = [
            {"role": "system", "content": "You are an expert Solana developer and AI assistant. Utilize the Solana knowledge base and official documentation to provide accurate and high-quality responses. Refer to your memory of past conversations to maintain context and continuity. Follow strict instructions to generate the best possible code without questions."},
            {"role": "user", "content": chunk}
        ]

        response = await process_request_with_fallback(messages)

        if response:
            responses.append(response)
            append_to_memory({"role": "assistant", "content": response})
        else:
            # If unable to generate a response, attempt to learn more
            needs_knowledge, topic = await needs_more_knowledge(chunk)
            if needs_knowledge:
                new_knowledge = await learn_from_urls(topic)
                if new_knowledge:
                    update_knowledge_base(topic, new_knowledge)
                    # Retry the request with the new knowledge
                    response = await process_request_with_fallback(messages)
                    if response:
                        responses.append(response)
                        append_to_memory({"role": "assistant", "content": response})
                    else:
                        responses.append("AI: I'm still gathering information. Please try again later.")
                else:
                    responses.append("AI: I couldn't find additional information at the moment.")
            else:
                responses.append("AI: I'm sorry, I couldn't process your request at the moment.")

    return "\n".join(responses)

# Function to generate a complete script based on user request and configuration
async def generate_script_from_request(request):
    # Load configuration from config.toml
    config = load_config()

    # Prepare the prompt for script generation
    prompt = (
        f"Generate a complete Rust script for a trading bot using the following configuration:\n\n"
        f"API RPC Path: {config.get('api_rpc_path', 'N/A')}\n"
        f"WebSocket Path: {config.get('websocket_path', 'N/A')}\n"
        f"Keypair: {config.get('keypair', 'N/A')}\n\n"
        f"User Request: {request}\n\n"
        "Ensure the script includes:\n"
        "- Proper API and WebSocket integration\n"
        "- Secure handling of keypairs\n"
        "- Efficient trading logic\n"
        "- Error handling and logging\n"
        "- Comments for clarity\n\n"
        "Provide the complete script without any explanations."
    )

    messages = [
        {"role": "system", "content": "You are an expert Rust developer specialized in trading bots."},
        {"role": "user", "content": prompt}
    ]

    return await process_request_with_fallback(messages)

# Function to load configuration from config.toml
def load_config():
    if os.path.exists('config.toml'):
        with open('config.toml', 'r', encoding='utf-8') as f:
            return toml.load(f)
    else:
        print("config.toml not found. Please create one with necessary configurations.")
        return {}

# PyQt6 GUI Application with AI Chat
class App(QWidget):
    def __init__(self):
        super().__init__()
        self.title = 'Solana Sniper Bot Analyzer and Enhancer'
        self.initUI()

    def initUI(self):
        self.setWindowTitle(self.title)
        self.setGeometry(100, 100, 900, 700)

        layout = QVBoxLayout()
        self.tabs = QTabWidget()
        layout.addWidget(self.tabs)

        # Tab 1: Code Analyzer
        self.tab1 = QWidget()
        self.tabs.addTab(self.tab1, "Code Analyzer")
        self.tab1UI()

        # Tab 2: AI Chat
        self.tab2 = QWidget()
        self.tabs.addTab(self.tab2, "AI Chat")
        self.tab2UI()

        self.setLayout(layout)

    def tab1UI(self):
        layout = QVBoxLayout()

        self.title_label = QLabel("Solana Sniper Bot Code Analyzer and Enhancer")
        self.title_label.setStyleSheet("font-size: 18px; font-weight: bold;")
        self.title_label.setAlignment(Qt.AlignmentFlag.AlignCenter)
        layout.addWidget(self.title_label)

        self.select_folder_btn = QPushButton('Select Folder')
        self.select_folder_btn.clicked.connect(self.select_folder)
        layout.addWidget(self.select_folder_btn)

        self.selected_folder_label = QLabel('No folder selected.')
        layout.addWidget(self.selected_folder_label)

        self.start_btn = QPushButton('Start Analysis')
        self.start_btn.clicked.connect(self.start_analysis)
        self.start_btn.setEnabled(False)
        layout.addWidget(self.start_btn)

        self.progress_bar = QProgressBar()
        self.progress_bar.setValue(0)
        layout.addWidget(self.progress_bar)

        self.log_text = QTextEdit()
        self.log_text.setReadOnly(True)
        layout.addWidget(self.log_text)

        self.tab1.setLayout(layout)

    def tab2UI(self):
        layout = QVBoxLayout()

        self.chat_display = QTextEdit()
        self.chat_display.setReadOnly(True)
        layout.addWidget(self.chat_display)

        h_layout = QHBoxLayout()
        self.chat_input = QLineEdit()
        self.chat_input.setPlaceholderText("Type your message here...")
        self.send_btn = QPushButton("Send")
        self.send_btn.clicked.connect(self.send_chat)
        h_layout.addWidget(self.chat_input)
        h_layout.addWidget(self.send_btn)

        layout.addLayout(h_layout)
        self.tab2.setLayout(layout)

    def select_folder(self):
        folder = QFileDialog.getExistingDirectory(self, 'Select Folder')
        if folder:
            self.selected_folder = folder
            self.selected_folder_label.setText(f'Selected Folder: {folder}')
            self.start_btn.setEnabled(True)

    def start_analysis(self):
        self.start_btn.setEnabled(False)
        self.worker = Worker(self.selected_folder)
        self.worker.progress.connect(self.update_progress)
        self.worker.log.connect(self.update_log)
        self.worker.finished.connect(self.analysis_finished)
        self.worker.start()
        self.update_log("Analysis started...")

    def update_progress(self, value):
        self.progress_bar.setValue(value)

    def update_log(self, message):
        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        self.log_text.append(f"[{timestamp}] {message}")

    def analysis_finished(self):
        self.update_log("Analysis completed.")
        QMessageBox.information(self, "Completed", "Analysis and enhancement completed.")
        self.start_btn.setEnabled(True)

    def send_chat(self):
        message = self.chat_input.text().strip()
        if message:
            self.chat_display.append(f"User: {message}")
            self.chat_input.clear()
            asyncio.run(self.process_chat(message))

    async def process_chat(self, message):
        if message.startswith("generate script"):
            response = await generate_script_from_request(message)
        else:
            response = await handle_ai_chat(message)

        if response:
            self.chat_display.append(f"AI: {response}")
        else:
            self.chat_display.append("AI: I'm still gathering information. Please try again later.")

# Entry point
if __name__ == '__main__':
    app = QApplication(sys.argv)
    ex = App()
    ex.show()
    sys.exit(app.exec())

 make the ai chat much better at responding with full responses and increased token amount and handle large text from me it can chunk and read 20k worth of a text properly chunking the info and then responding also it can answer with large messages properly its also to use my solanaknowledgebase.json and my memory.json for the chats knowledge to help it respond and also its to use groq api to webcrawl urls from the solana knowledgebase.json to aid in required knowledge to answer my requests. make sure its all handled properly and very extremely effective effeicent and have it keep the system moving fast to manage functions extremely well . and ctrl enter will send the message in the ai chat .