please do me a huge favor please write this script here import os
import sys
import asyncio
import aiohttp
import json
import logging
import hashlib
import re
import gc
import tempfile
import cProfile
import pstats
import io
import sqlite3
import subprocess
from datetime import datetime
from typing import List, Dict, Any
from collections import Counter

import torch
from torch.cuda.amp import autocast
from transformers import GPTNeoForCausalLM, GPT2Tokenizer
import ray
from PyQt6.QtWidgets import QApplication, QMainWindow, QWidget, QVBoxLayout, QHBoxLayout, QPushButton, QTextEdit, QLabel, QProgressBar, QFileDialog, QMessageBox, QTabWidget, QSplitter, QLineEdit
from PyQt6.QtCore import Qt, QRunnable, QThreadPool, QObject, pyqtSignal, pyqtSlot, QTimer, QEvent
from PyQt6.QtGui import QTextCursor
import aiosqlite
from bs4 import BeautifulSoup
import psutil

# Initialize logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# Initialize Ray
ray.init()

# Load environment variables
GROQ_API_KEY = os.getenv('GROQ_API_KEY')
GROQ_MODEL = os.getenv('GROQ_MODEL', 'mixtral-8x7b-32768')
GPT_NEO_MODEL = os.getenv('GPT_NEO_MODEL', 'EleutherAI/gpt-neo-2.7B')
MODEL_PATH = os.getenv('MODEL_PATH', 'local_gpt_neo_model')
KNOWLEDGE_PATH = os.getenv('KNOWLEDGE_PATH', 'solana_knowledge_base.db')
MEMORY_PATH = os.getenv('MEMORY_PATH', 'memory.json')

# Initialize CUDA device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

# Load model and tokenizer
print("Loading local GPT-Neo model with bitsandbytes quantization...")
model = GPTNeoForCausalLM.from_pretrained(GPT_NEO_MODEL, torch_dtype=torch.float16, low_cpu_mem_usage=True).to(device)
tokenizer = GPT2Tokenizer.from_pretrained(GPT_NEO_MODEL)
print(f"Model loaded: {GPT_NEO_MODEL}")

# Load memory
try:
    with open(MEMORY_PATH, 'r') as f:
        memory = json.load(f)
except FileNotFoundError:
    memory = []

def save_memory(memory):
    with open(MEMORY_PATH, 'w') as f:
        json.dump(memory, f)

class WorkerSignals(QObject):
    progress = pyqtSignal(int)
    log = pyqtSignal(str)
    finished = pyqtSignal()
    result = pyqtSignal(object)
    error = pyqtSignal(str)

class AsyncWorker(QRunnable):
    def __init__(self, fn, *args, **kwargs):
        super().__init__()
        self.fn = fn
        self.args = args
        self.kwargs = kwargs
        self.signals = WorkerSignals()

    @pyqtSlot()
    def run(self):
        try:
            loop = asyncio.new_event_loop()
            asyncio.set_event_loop(loop)
            result = loop.run_until_complete(self.fn(*self.args, **self.kwargs))
            loop.close()
            self.signals.result.emit(result)
        except Exception as e:
            self.signals.error.emit(str(e))
            logging.error(f"AsyncWorker error: {str(e)}")
        finally:
            self.signals.finished.emit()

class SolanaKnowledgeDB:
    def __init__(self, db_path: str):
        self.db_path = db_path

    async def init_db(self):
        if os.path.exists(self.db_path) and os.path.getsize(self.db_path) > 0:
            try:
                async with aiosqlite.connect(self.db_path) as db:
                    await db.execute('SELECT 1 FROM knowledge_base LIMIT 1')
            except sqlite3.DatabaseError:
                os.remove(self.db_path)
                print(f"Removed invalid database file: {self.db_path}")

        async with aiosqlite.connect(self.db_path) as db:
            await db.execute('''CREATE TABLE IF NOT EXISTS knowledge_base
                                (id TEXT PRIMARY KEY, content TEXT, category TEXT, timestamp DATETIME DEFAULT CURRENT_TIMESTAMP)''')
            await db.commit()

    async def add_knowledge(self, content: str, category: str):
        content_hash = hashlib.sha256(content.encode()).hexdigest()
        async with aiosqlite.connect(self.db_path) as db:
            await db.execute('INSERT OR REPLACE INTO knowledge_base (id, content, category) VALUES (?, ?, ?)',
                             (content_hash, content, category))
            await db.commit()

    async def search_knowledge(self, query: str) -> List[Dict[str, Any]]:
        query_words = set(re.findall(r'\w+', query.lower()))
        
        async with aiosqlite.connect(self.db_path) as db:
            cursor = await db.execute('SELECT * FROM knowledge_base')
            rows = await cursor.fetchall()
            
            results = []
            for row in rows:
                content_words = set(re.findall(r'\w+', row[1].lower()))
                similarity = len(query_words.intersection(content_words)) / len(query_words.union(content_words))
                results.append((similarity, {'id': row[0], 'content': row[1], 'category': row[2], 'timestamp': row[3]}))
            
            results.sort(key=lambda x: x[0], reverse=True)
            return [item[1] for item in results[:5]]  # Return top 5 results

class WebCrawler:
    def __init__(self):
        self.session = None

    async def create_session(self):
        if self.session is None:
            self.session = aiohttp.ClientSession()

    async def close_session(self):
        if self.session:
            await self.session.close()
            self.session = None

    async def crawl_sourcegraph(self, query):
        await self.create_session()
        url = f"https://sourcegraph.com/search?q={query}&patternType=literal"
        async with self.session.get(url) as response:
            if response.status == 200:
                html = await response.text()
                soup = BeautifulSoup(html, 'html.parser')
                results = soup.find_all('div', class_='result-container')
                return [result.get_text() for result in results]
            else:
                return []

    async def crawl_official_docs(self, url):
        await self.create_session()
        async with self.session.get(url) as response:
            if response.status == 200:
                html = await response.text()
                soup = BeautifulSoup(html, 'html.parser')
                content = soup.find('main')
                return content.get_text() if content else ""
            else:
                return ""

class App(QMainWindow):
    def __init__(self):
        super().__init__()
        self.setWindowTitle('Legendary Solana Sniper Bot Analyzer and Enhancer (GOD MODE)')
        self.setGeometry(100, 100, 1200, 800)

        self.selected_folder = ''
        self.threadpool = QThreadPool()
        self.knowledge_db = SolanaKnowledgeDB(KNOWLEDGE_PATH)
        self.web_crawler = WebCrawler()

        self.init_ui()
        self.init_knowledge_db()

    def init_knowledge_db(self):
        worker = AsyncWorker(self.knowledge_db.init_db)
        worker.signals.error.connect(self.handle_db_init_error)
        self.threadpool.start(worker)

    def handle_db_init_error(self, error_message):
        QMessageBox.critical(self, "Database Error", f"Failed to initialize the knowledge database: {error_message}")
        logging.error(f"Database initialization error: {error_message}")

    def init_ui(self):
        central_widget = QWidget()
        self.setCentralWidget(central_widget)
        layout = QVBoxLayout(central_widget)

        # Create tabs
        self.tabs = QTabWidget()
        layout.addWidget(self.tabs)

        # Analyzer tab
        analyzer_tab = QWidget()
        analyzer_layout = QVBoxLayout(analyzer_tab)
        self.tabs.addTab(analyzer_tab, "Analyzer")

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

        self.selected_folder_label = QLabel('No folder selected')
        analyzer_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)
        analyzer_layout.addWidget(self.start_btn)

        self.progress_bar = QProgressBar()
        analyzer_layout.addWidget(self.progress_bar)

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

        # Enhancer tab
        enhancer_tab = QWidget()
        enhancer_layout = QVBoxLayout(enhancer_tab)
        self.tabs.addTab(enhancer_tab, "Enhancer")

        splitter = QSplitter(Qt.Orientation.Horizontal)
        enhancer_layout.addWidget(splitter)

        self.original_code = QTextEdit()
        self.original_code.setPlaceholderText("Paste your original code here...")
        splitter.addWidget(self.original_code)

        self.enhanced_code = QTextEdit()
        self.enhanced_code.setReadOnly(True)
        self.enhanced_code.setPlaceholderText("Enhanced code will appear here...")
        splitter.addWidget(self.enhanced_code)

        enhance_btn = QPushButton('Enhance Code')
        enhance_btn.clicked.connect(self.enhance_code)
        enhancer_layout.addWidget(enhance_btn)

        # Chat tab
        chat_tab = QWidget()
        chat_layout = QVBoxLayout(chat_tab)
        self.tabs.addTab(chat_tab, "AI Chat")

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

        self.chat_input = QTextEdit()
        self.chat_input.setFixedHeight(50)
        chat_layout.addWidget(self.chat_input)

        send_btn = QPushButton('Send')
        send_btn.clicked.connect(self.send_chat)
        chat_layout.addWidget(send_btn)

        # Web Crawler tab
        crawler_tab = QWidget()
        crawler_layout = QVBoxLayout(crawler_tab)
        self.tabs.addTab(crawler_tab, "Web Crawler")

        self.crawler_input = QLineEdit()
        self.crawler_input.setPlaceholderText("Enter search query or URL...")
        crawler_layout.addWidget(self.crawler_input)

        crawler_btn = QPushButton('Crawl')
        crawler_btn.clicked.connect(self.start_crawl)
        crawler_layout.addWidget(crawler_btn)

        self.crawler_results = QTextEdit()
        self.crawler_results.setReadOnly(True)
        crawler_layout.addWidget(self.crawler_results)

        # Export buttons
        export_layout = QHBoxLayout()
        layout.addLayout(export_layout)

        export_analysis_btn = QPushButton('Export Analysis')
        export_analysis_btn.clicked.connect(self.export_analysis)
        export_layout.addWidget(export_analysis_btn)

        export_chat_btn = QPushButton('Export Chat')
        export_chat_btn.clicked.connect(self.export_chat_history)
        export_layout.addWidget(export_chat_btn)

        # Set up event filter for Ctrl+Enter in chat input
        self.chat_input.installEventFilter(self)

        # Timer for updating performance metrics
        self.performance_timer = QTimer(self)
        self.performance_timer.timeout.connect(self.update_performance_metrics)
        self.performance_timer.start(1000)  # Update every second

    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.progress_bar.setValue(0)
        self.log_text.clear()

        worker = AsyncWorker(self.analyze_folder)
        worker.signals.progress.connect(self.update_progress)
        worker.signals.log.connect(self.update_log)
        worker.signals.finished.connect(self.analysis_finished)
        worker.signals.error.connect(self.handle_worker_error)

        self.threadpool.start(worker)
        self.update_log("Analysis started...")

    async def analyze_folder(self):
        files = [f for f in os.listdir(self.selected_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.selected_folder, file)
            self.signals.log.emit(f"Analyzing {file}...")

            with open(file_path, 'r') as f:
                content = f.read()

            analysis = await self.analyze_code(content)
            self.signals.log.emit(f"File: {file}")
            self.signals.log.emit(f"Analysis: {json.dumps(analysis, indent=2)}")

            enhanced_code = await self.enhance_code(content, analysis)
            self.signals.log.emit(f"Enhanced code for {file}")

            enhanced_file_path = os.path.join(self.selected_folder, f"enhanced_{file}")
            with open(enhanced_file_path, 'w') as f:
                f.write(enhanced_code)

            progress = int((i + 1) / total_files * 100)
            self.signals.progress.emit(progress)

    async def analyze_code(self, code):
        # Implement more sophisticated code analysis here
        analysis = {
            "num_functions": len(re.findall(r'\bdef\b', code)),
            "num_classes": len(re.findall(r'\bclass\b', code)),
            "num_imports": len(re.findall(r'\bimport\b', code)),
            "lines_of_code": len(code.splitlines()),
            "complexity": self.calculate_complexity(code),
            "potential_issues": self.identify_potential_issues(code),
        }
        return analysis

    def calculate_complexity(self, code):
        # Implement a basic complexity calculation (e.g., cyclomatic complexity)
        # This is a simplified version and can be expanded for more accurate results
        complexity = 0
        complexity += len(re.findall(r'\bif\b', code))
        complexity += len(re.findall(r'\bfor\b', code))
        complexity += len(re.findall(r'\bwhile\b', code))
        complexity += len(re.findall(r'\bexcept\b', code))
        return complexity

    def identify_potential_issues(self, code):
        issues = []
        if 'print(' in code:
            issues.append("Consider using logging instead of print statements")
        if 'except:' in code:
            issues.append("Avoid bare except clauses")
        if 'global ' in code:
            issues.append("Minimize use of global variables")
        # Add more issue identifications as needed
        return issues

    async def enhance_code(self, code, analysis):
        prompt = f"""Enhance the following Solana code:

{code}

Code analysis:
{json.dumps(analysis, indent=2)}

Enhance the code by:
1. Improving error handling
2. Optimizing performance
3. Ensuring best practices for Solana development
4. Adding comments for clarity
5. Implementing any missing functionality based on the analysis
6. Completing any unfinished functions or code blocks
7. Addressing the potential issues identified in the analysis

Provide the full, enhanced code without omissions.
"""
        return await self.generate_with_groq(prompt)

    async def generate_with_groq(self, prompt):
        headers = {
            "Authorization": f"Bearer {GROQ_API_KEY}",
            "Content-Type": "application/json"
        }
        data = {
            "model": GROQ_MODEL,
            "messages": [{"role": "user", "content": prompt}],
            "temperature": 0.7,
            "max_tokens": 8000,
            "top_p": 1,
            "stream": False
        }
        async with aiohttp.ClientSession() as session:
            async with session.post("https://api.groq.com/v1/chat/completions", headers=headers, json=data) as response:
                if response.status == 200:
                    result = await response.json()
                    return result['choices'][0]['message']['content']
                else:
                    error_msg = await response.text()
                    raise Exception(f"Groq API error: {error_msg}")

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

    def update_log(self, message):
        self.log_text.append(message)
        self.log_text.moveCursor(QTextCursor.MoveOperation.End)

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

    def handle_worker_error(self, error_message):
        self.update_log(f"Error: {error_message}")
        QMessageBox.critical(self, "Error", error_message)
        self.start_btn.setEnabled(True)

    def enhance_code(self):
        original_code = self.original_code.toPlainText()
        if not original_code:
            QMessageBox.warning(self, "Warning", "Please enter some code to enhance.")
            return

        self.enhanced_code.clear()
        self.enhanced_code.setPlaceholderText("Enhancing code...")

        worker = AsyncWorker(self.perform_code_enhancement, original_code)
        worker.signals.result.connect(self.update_enhanced_code)
        worker.signals.error.connect(self.handle_enhancement_error)

        self.threadpool.start(worker)

    async def perform_code_enhancement(self, code):
        analysis = await self.analyze_code(code)
        return await self.enhance_code(code, analysis)

    def update_enhanced_code(self, enhanced_code):
        self.enhanced_code.setPlainText(enhanced_code)

    def handle_enhancement_error(self, error_message):
        self.enhanced_code.setPlainText(f"Error during enhancement: {error_message}")

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

            worker = AsyncWorker(self.handle_ai_chat, message)
            worker.signals.result.connect(self.update_chat_display)
            worker.signals.error.connect(self.handle_chat_error)

            self.threadpool.start(worker)

    async def handle_ai_chat(self, message):
        try:
            memory.append({"role": "user", "content": message})
            save_memory(memory)

            knowledge_context = await self.get_relevant_knowledge(message)
            
            full_context = [
                {"role": "system", "content": "You are an expert AI assistant specializing in Solana development and trading bots."},
                {"role": "system", "content": f"Knowledge context: {knowledge_context}"},
            ] + memory[-10:]  # Include last 10 messages for context

            response = await self.generate_with_groq("\n".join([f"{m['role']}: {m['content']}" for m in full_context]))
            
            memory.append({"role": "assistant", "content": response})
            save_memory(memory)

            return response
        except Exception as e:
            logging.error(f"Error in handle_ai_chat: {str(e)}")
            return f"An error occurred: {str(e)}"

    async def get_relevant_knowledge(self, query):
        results = await self.knowledge_db.search_knowledge(query)
        return "\n".join([result['content'] for result in results])

    def update_chat_display(self, message):
        self.chat_display.append(f"AI: {message}")
        self.chat_display.moveCursor(QTextCursor.MoveOperation.End)

    def handle_chat_error(self, error_message):
        self.chat_display.append(f"Error: {error_message}")

    def start_crawl(self):
        query = self.crawler_input.text().strip()
        if not query:
            QMessageBox.warning(self, "Warning", "Please enter a search query or URL.")
            return

        self.crawler_results.clear()
        self.crawler_results.setPlaceholderText("Crawling...")

        worker = AsyncWorker(self.perform_crawl, query)
        worker.signals.result.connect(self.update_crawler_results)
        worker.signals.error.connect(self.handle_crawler_error)

        self.threadpool.start(worker)

    async def perform_crawl(self, query):
        if query.startswith('http'):
            return await self.web_crawler.crawl_official_docs(query)
        else:
            return await self.web_crawler.crawl_sourcegraph(query)

    def update_crawler_results(self, results):
        if isinstance(results, list):
            self.crawler_results.setPlainText("\n\n".join(results))
        else:
            self.crawler_results.setPlainText(results)

    def handle_crawler_error(self, error_message):
        self.crawler_results.setPlainText(f"Error during crawling: {error_message}")

    def export_analysis(self):
        file_path, _ = QFileDialog.getSaveFileName(self, "Save Analysis Results", "", "Text Files (*.txt);;All Files (*)")
        if file_path:
            with open(file_path, 'w') as f:
                f.write(self.log_text.toPlainText())
            QMessageBox.information(self, "Export Successful", f"Analysis results exported to {file_path}")

    def export_chat_history(self):
        file_path, _ = QFileDialog.getSaveFileName(self, "Save Chat History", "", "Text Files (*.txt);;All Files (*)")
        if file_path:
            with open(file_path, 'w') as f:
                f.write(self.chat_display.toPlainText())
            QMessageBox.information(self, "Export Successful", f"Chat history exported to {file_path}")

    def eventFilter(self, source, event):
        if (source is self.chat_input and
            event.type() == QEvent.Type.KeyPress and
            event.key() == Qt.Key.Key_Return and
            event.modifiers() == Qt.KeyboardModifier.ControlModifier):
            self.send_chat()
            return True
        return super().eventFilter(source, event)

    def update_performance_metrics(self):
        cpu_percent = psutil.cpu_percent()
        memory_percent = psutil.virtual_memory().percent
        gpu_utilization = self.get_gpu_utilization()
        
        metrics = f"CPU: {cpu_percent}% | RAM: {memory_percent}% | GPU: {gpu_utilization}%"
        self.statusBar().showMessage(metrics)

    def get_gpu_utilization(self):
        try:
            output = subprocess.check_output(['nvidia-smi', '--query-gpu=utilization.gpu', '--format=csv,noheader,nounits'])
            utilization = output.decode('utf-8').strip()
            return f"{utilization}%"
        except:
            return "N/A"

def main():
    app = QApplication(sys.argv)
    ex = App()
    ex.show()
    sys.exit(app.exec())

if __name__ == '__main__':
    main()

 BUT PLEASE USE THIS I MADE A MISTAKE AND IT NEEDS TO USE THIS INSTEAD # Update this line in the imports
from transformers import GPTNeoForCausalLM, GPT2Tokenizer

# ... (keep other imports as they were)

# Update these lines in the environment variables section
GPT_NEO_MODEL = os.getenv('GPT_NEO_MODEL', 'EleutherAI/gpt-neo-1.3B')
MODEL_PATH = os.getenv('MODEL_PATH', 'local_gpt_neo_model')

# ... (keep other variables as they were)

# Update the model loading section
print("Loading local GPT-Neo model with bitsandbytes quantization...")
model = GPTNeoForCausalLM.from_pretrained(GPT_NEO_MODEL, torch_dtype=torch.float16, low_cpu_mem_usage=True).to(device)
tokenizer = GPT2Tokenizer.from_pretrained(GPT_NEO_MODEL)
print(f"Model loaded: {GPT_NEO_MODEL}")

# ... (keep the rest of the code as it was, but update the chat handling function)

class App(QMainWindow):
    # ... (keep other methods as they were)

    async def handle_ai_chat(self, message):
        try:
            memory.append({"role": "user", "content": message})
            save_memory(memory)

            knowledge_context = await self.get_relevant_knowledge(message)
            
            full_context = (
                f"You are an expert AI assistant specializing in Solana development and trading bots. "
                f"Knowledge context: {knowledge_context}\n\n"
            )
            
            for m in memory[-5:]:  # Include last 5 messages for context
                full_context += f"{m['role'].capitalize()}: {m['content']}\n"
            
            full_context += "Assistant: "

            inputs = tokenizer.encode(full_context, return_tensors="pt").to(device)
            attention_mask = torch.ones(inputs.shape, dtype=torch.long, device=device)
            
            with torch.no_grad():
                outputs = model.generate(
                    inputs,
                    attention_mask=attention_mask,
                    max_length=len(inputs[0]) + 150,
                    num_return_sequences=1,
                    no_repeat_ngram_size=2,
                    temperature=0.7,
                )

            response = tokenizer.decode(outputs[0], skip_special_tokens=True)
            response = response[len(full_context):].strip()
            
            memory.append({"role": "assistant", "content": response})
            save_memory(memory)

            return response
        except Exception as e:
            logging.error(f"Error in handle_ai_chat: {str(e)}")
            return f"An error occurred: {str(e)}"

    # ... (keep other methods as they were)

# ... (keep the rest of the code as it was)