what can you see thats wrong with this import os
import sys
import asyncio
import aiohttp
import json
import logging
import hashlib
import re
import gc
import tempfile
import subprocess
from datetime import datetime
from typing import List, Dict, Any
from collections import Counter

import torch
from transformers import GPTNeoForCausalLM, GPT2Tokenizer
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
import ray
from tqdm import tqdm
from sentence_transformers import SentenceTransformer
from fastapi import FastAPI, BackgroundTasks
from pydantic import BaseModel
from concurrent.futures import ThreadPoolExecutor

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

# Load environment variables
GPT_NEO_MODEL = 'EleutherAI/gpt-neo-1.3B'
KNOWLEDGE_PATH = 'solana_knowledge_base.db'
MEMORY_PATH = 'memory.json'

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

# Initialize Ray for distributed computing
ray.init(ignore_reinit_error=True)

@ray.remote(num_gpus=1 if torch.cuda.is_available() else 0)
def load_model():
    logger.info("Loading GPT-Neo 1.3B model...")
    model = GPTNeoForCausalLM.from_pretrained(GPT_NEO_MODEL).to(device)
    tokenizer = GPT2Tokenizer.from_pretrained(GPT_NEO_MODEL)
    logger.info("Model loaded successfully")
    return model, tokenizer

model_tokenizer_ref = load_model.remote()

# Load sentence transformer for embeddings
sentence_transformer = SentenceTransformer('all-MiniLM-L6-v2')

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

memory = load_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))
            self.signals.result.emit(result)
        except Exception as e:
            self.signals.error.emit(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):
        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_embedding = sentence_transformer.encode(query)
        
        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_embedding = sentence_transformer.encode(row[1])
                similarity = torch.cosine_similarity(torch.tensor(query_embedding), torch.tensor(content_embedding), dim=0)
                results.append((similarity.item(), {'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"
        try:
            async with self.session.get(url, timeout=30) 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 []
        except asyncio.TimeoutError:
            return ["Error: Request timed out"]
        except Exception as e:
            return [f"Error: {str(e)}"]

    async def crawl_official_docs(self, url):
        await self.create_session()
        try:
            async with self.session.get(url, timeout=30) 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 ""
        except asyncio.TimeoutError:
            return "Error: Request timed out"
        except Exception as e:
            return f"Error: {str(e)}"

class CodeAnalyzer:
    @staticmethod
    @ray.remote
    def analyze_code(code: str) -> Dict[str, Any]:
        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": CodeAnalyzer.calculate_complexity(code),
            "potential_issues": CodeAnalyzer.identify_potential_issues(code),
        }
        return analysis

    @staticmethod
    def calculate_complexity(code: str) -> int:
        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

    @staticmethod
    def identify_potential_issues(code: str) -> List[str]:
        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")
        if 'import *' in code:
            issues.append("Avoid wildcard imports")
        if 'assert' in code:
            issues.append("Use assertions judiciously, not for data validation in production")
        return issues

class CodeEnhancer:
    @staticmethod
    @ray.remote
    def enhance_code(code: str, analysis: Dict[str, Any]) -> str:
        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 CodeEnhancer.generate_with_model.remote(prompt)

    @staticmethod
    @ray.remote
    def generate_with_model(prompt: str) -> str:
        model, tokenizer = ray.get(model_tokenizer_ref)
        inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)
        
        with torch.no_grad():
            outputs = model.generate(
                inputs,
                max_length=len(inputs[0]) + 500,
                num_return_sequences=1,
                no_repeat_ngram_size=2,
                temperature=0.7,
            )

        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        return response[len(prompt):].strip()

class App(QMainWindow):
    def __init__(self):
        super().__init__()
        self.setWindowTitle('Legendary Solana Sniper Bot Analyzer and Enhancer (1.3B Model)')
        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}")
        logger.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 CodeAnalyzer.analyze_code.remote(content)
            self.signals.log.emit(f"File: {file}")
            self.signals.log.emit(f"Analysis: {json.dumps(analysis, indent=2)}")

            enhanced_code = await CodeEnhancer.enhance_code.remote(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)

    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 CodeAnalyzer.analyze_code.remote(code)
        return await CodeEnhancer.enhance_code.remote(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)

            relevant_knowledge = await self.knowledge_db.search_knowledge(message)
            
            context = "You are an AI assistant specializing in Solana development and trading bots. "
            context += "Use the following relevant information from the knowledge base:\n"
            for item in relevant_knowledge:
                context += f"- {item['content']}\n"
            context += "\nRecent conversation:\n"
            for m in memory[-5:]:  # Include last 5 messages for context
                context += f"{m['role'].capitalize()}: {m['content']}\nAssistant: "

            response = await CodeEnhancer.generate_with_model.remote(context)
            
            memory.append({"role": "assistant", "content": response})
            save_memory(memory)

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

    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 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 closeEvent(self, event):
        # Gracefully shut down Ray
        ray.shutdown()
        event.accept()

# FastAPI setup for potential API endpoints
app = FastAPI()

class CodeEnhanceRequest(BaseModel):
    code: str

@app.post("/enhance_code")
async def enhance_code_api(request: CodeEnhanceRequest, background_tasks: BackgroundTasks):
    analysis = await CodeAnalyzer.analyze_code.remote(request.code)
    enhanced_code = await CodeEnhancer.enhance_code.remote(request.code, analysis)
    background_tasks.add_task(SolanaKnowledgeDB(KNOWLEDGE_PATH).add_knowledge, enhanced_code, "enhanced_code")
    return {"enhanced_code": enhanced_code}

# Main PyQt application setup
def main():
    qapp = QApplication(sys.argv)
    app = App()
    app.show()
    sys.exit(qapp.exec())

if __name__ == '__main__':
    main()