import os
import shutil
import tempfile
import subprocess
import json
import time
from typing import List, Dict   import bitsandbytes as bnb

   model = bnb.optim.GlobalOptimManager.get_instance().quantize_model(model, quantization_type="bnb_8bit")from difflib import SequenceMatcher
   from transformers import AutoTokenizer, AutoModelForCausalLM
   import torch

   tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
   model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B")
   device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
   model.to(device)
   prompt = "Once upon a time, in a land far far away,"

   encoded_input = tokenizer(prompt, return_tensors="pt").to(device)
   output = model.generate(
       **encoded_input,
       max_length=100,
       do_sample=True,
       top_k=50,
       top_p=0.95,
       temperature=0.8
   )

   generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
   print(generated_text)
CHECKPOINT_FILE = "enhancement_checkpoint.json"

class RateLimiter:

    def wait_if_needed(self, tokens, is_haiku=False):
        current_time = time.time()
        
        if current_time - self.day_start >= 86400:
            self.day_start = current_time
         token_count = 0
            day_token_count = 0

        if is_haiku:
           tokens > HAIKU_TOKENS_PER_DAY:
                sleep_time = self.day_start + 86400 - current_time
               limit reached. Sleeping for {sleep_time:.2f} seconds.")
                time.sleep(sleep_time)
                self.day_start = time.time()
               _count = 0
        else:
            ifken_count + tokens > SONNET_TOKENS_PER_DAY:
                sleep_time = self.day_start + 86400 - current_time
                print(f"Daily Sonnet token limit reached. Sleeping for {sleep_time:.2f} seconds.")
                time.sleep(sleep_time)
                self.day_start = time.time()
                self.sonnet_day_token_count = 0

        if current_time - self.last_request_time >= 60:
            self.last_request_time = current_time
            self.request_count = 0
            self.sonnet_token_count = 0
            self.haiku_token_count = 0
        
        if self.request_count >= REQUESTS_PER_MINUTE:
            sleep_time = 60 - (current_time - self.last_request_time)
            print(f"Request rate limit reached. Sleeping for {sleep_time:.2f} seconds.")
            time.sleep(sleep_time)
            self.last_request_time = time.time()
            self.request_count = 0
            self.sonnet_token_count = 0
            self.haiku_token_count = 0
        elif f.sonnet_token_count_PER_MINUTE):
            sleep_time = 60 - (current_time - self.last_request_time)
            print(f"Token rate limit reached. Sleeping for {sleep_time:.2f} seconds.")
            time.sleep(sleep_time)
            self.last_request_time = time.time()
            self.request_count = 0
            self.sonnet_token_count = 0
            self.haiku_token_count = 0

        self.request_count += 1
        if is
            su_token_count += tokens
            self.day_token_count += tokens
        else:
            self.onf.sonnet_day_token_count += tokens

class ProjectEnhancer:
    def __init__(self, api_key: str):
        self.client = 
        self.

    def clone_project(self, source_path: str, dest_path: str):
        print(f"Cloning project from {source_path} to {dest_path}")
        shutil.copytree(source_path, dest_path, dirs_exist_ok=True)

    def enhance_projects(self, project_paths: List[str], output_path: str):
        print(f"Starting project enhancement process...")
        for i, path in enumerate(project_paths, 1):
            print(f"Project {i}: {path}")
        print(f"Output path: {output_path}")

        os.makedirs(output_path, exist_ok=True)

        all_files = self.get_all_files_sorted(project_paths)
        checkpoint = self.load_checkpoint(output_path)
        start_index = checkpoint.get('last_processed_index', 0)

        for index, (file, _) in enumerate(all_files[start_index:], start=start_index):
            print(f"\nProcessing file {index + 1}/{len(all_files)}: {file}")
            contents = [self.read_file(os.path.join(project_path, file)) if os.path.exists(os.path.join(project_path, file)) else "" for project_path in project_paths]
            enhanced_content = self.enhance_file(file, contents, output_path)
            output_file = os.path.join(output_path, file)
            os.makedirs(os.path.dirname(output_file), exist_ok=True)
            self.write_file(output_file, enhanced_content)

            self.save_checkpoint(output_path, {'last_processed_index': index + 1})

        self.create_knowledge_base(project_paths, output_path)
        self.create_readme(output_path)
        self.create_user_manual(output_path)

        self.clear_checkpoint(output_path)

        print("\nProject enhancement complete. Please review the output in:", output_path)

    def get_all_files_sorted(self, project_paths: List[str]) -> List[tuple]:
        all_files = []
        excluded_dirs = {'venv', 'env', 'backup', '__pycache__', 'site-packages', 'dist-packages'}
        for project_path in project_paths:
            for root, dirs, files in os.walk(project_path):
                # Remove excluded directories
                dirs[:] = [d for d in dirs if d.lower() not in excluded_dirs and not d.startswith('.')]
                
                # Check if the current directory is part of an excluded path
                if any(excluded in root.lower() for excluded in excluded_dirs):
                    continue
                
                for file in files:
                    if (file.lower().endswith(('.py', '.js', '.java', '.cpp', '.h', '.cs', '.go', '.rb', '.php', '.swift', '.txt', '.md', '.json', '.yaml', '.yml')) 
                        and not file.startswith('.')
                        and 'backup' not in file.lower()):
                        full_path = os.path.join(root, file)
                        relative_path = os.path.relpath(full_path, project_path)
                        file_size = os.path.getsize(full_path)
                        all_files.append((relative_path, file_size))
        return sorted(set(all_files), key=lambda x: x[1])

    def read_file(self, file_path: str) -> str:
        with open(file_path, 'r', encoding='utf-8', errors='ignore') as file:
            return file.read()

    def write_file(self, file_path: str, content: str):
        with open(file_path, 'w', encoding='utf-8') as file:
            file.write(content)

    def enhance_file(self, file_name: str, contents: List[str], output_path: str) -> str:
        combined_content = "".join(contents)
        if len(combined_content) > SONNET_MAX_TOKENS * 2:
            return self.enhance_large_file(file_name, contents, output_path)

        prompt = f"""
        Task: Enhance and combine the following versions of '{file_name}' from multiple projects into a single optimized file.

        {"".join([f"File from Project {i+1}:\n```\n{content}\n```\n\n" for i, content in enumerate(contents) if content])}

        Instructions:
        1. Combine the functionality of all files without losing any features.
        2. Eliminate redundancies and optimize the code for efficiency.
        3. Ensure the enhanced version maintains or improves upon the original functionality.
        4. Rename functions and variables if necessary to avoid conflicts and improve clarity.
        5. Use the best practices and most up-to-date syntax for the language.
        6. Add clear comments explaining the enhanced sections.
        7. Ensure there are no errors in the final code.
        8. If this is a configuration file, merge the configurations appropriately.
        9. For non-code files (like markdown or text), combine the information coherently.

        Important: For Python files, provide only the enhanced code without any explanatory text outside the code.

        Please provide the enhanced version of the file.
        """

        try:
            self.rate_limiter.wait_if_needed(len(prompt) + SONNET_MAX_TOKENS, is_haiku=False)
            response = self.client.messages.create(
                model=self.sonnet_model,
                max_token\
                messages=[{"role": "user", "content": prompt}]
            )
            result = response.content[0].text

            if file_name.endswith('.py'):
                # For Python files, return only the code part
                return result.strip()
            else:
                # For other files, keep the explanation
                code, _, explanation = result.partition("\n\nExplanation of changes:")
                print("Changes made:")
                print(explanation.strip())
                return code.strip()

        except Exception as e:
            print(f"Error enhancing file {file_name}: {str(e)}")
            return max(contents, key=len)

    def enhance_large_file(self, file_name: str, contents: List[str], output_path: str) -> str:
        print(f"File {file_name} is too large. Using Haiku model for processing.")
        
        prompt = f"""
        Enhance and combine the following large file '{file_name}' from multiple projects into a single optimized file.
        Follow these instructions to improve and combine the code:
        1. Combine the functionality of all versions without losing any features.
        2. Eliminate redundancies and optimize for efficiency.
        3. Ensure the enhanced version maintains or improves upon the original functionality.
        4. Rename functions and variables if necessary to avoid conflicts and improve clarity.
        5. Use the best practices and most up-to-date syntax for the language.
        6. Add clear comments explaining the enhanced sections.
        7. Ensure there are no errors in the final code.
        8. If this is a configuration file, merge the configurations appropriately.
        9. For non-code files (like markdown or text), combine the information coherently.
        """

        combined_content = "".join(contents)

        try:
            self.rate_limiter.wait_if_needed(len(prompt) + len(combined_content) + HAIKU_MAX_TOKENS, is_haiku=True)
            response = self.client.messages.create(
               \del,
                max_tokens\MAX_TOKENS,
                messages=[
                    {"role": "user", "content": prompt},
                    {"role": "assistant", "content": "Understood. I'll enhance and combine the file contents as per your instructions."},
                    {"role": "user", "content": combined_content}
                ]
            )
            return response.content[0].text.strip()

        except Exception as e:
            print(f"Error enhancing large file {file_name}: {str(e)}")
            return combined_content

    def create_knowledge_base(self, project_paths: List[str], output_path: str):
        print("Creating combined knowledge base...")
        kb_contents = []
        for project_path in project_paths:
            kb_file = os.path.join(project_path, "knowledge_base.txt")
            if os.path.exists(kb_file):
                kb_contents.append(self.read_file(kb_file))

        if kb_contents:
            prompt = f"""
            Task: Combine and enhance the following knowledge bases into a single, comprehensive knowledge base:

            {"".join([f"Knowledge Base {i+1}:\n```\n{content}\n```\n\n" for i, content in enumerate(kb_contents)])}

            Instructions:
            1. Merge all unique information from the knowledge bases.
            2. Eliminate redundancies and organize the information coherently.
            3. Ensure all relevant information is preserved and enhanced.
            4. Add any missing crucial information that would benefit the combined project.

            Please provide the enhanced knowledge base content.
            """

            try:
                self.rate_limiter.wait_if_needed(len(prompt) + SONNET_MAX_TOKENS, is_haiku=False)
                response = self.client.messages.create(
                    model=self.sonnet_model,
                    max_tokens=SONNET_MAX_TOKENS,
                    messages=[{"role": "user", "content": prompt}]
                )
                enhanced_kb = response.content[0].text
                self.write_file(os.path.join(output_path, "knowledge_base.txt"), enhanced_kb)
            except Exception as e:
                print(f"Error creating knowledge base: {str(e)}")
        else:
            print("No existing knowledge base found. Creating a new one...")
            self.write_file(os.path.join(output_path, "knowledge_base.txt"), "# Project Knowledge Base\n\n[Insert project-specific knowledge here]")

    def create_readme(self, output_path: str):
        print("Creating README.md...")
        prompt = f"""
        Task: Create a comprehensive README.md file for the newly combined and enhanced project.

        Instructions:
        1. Provide an overview of the project's purpose and functionality.
        2. List the main features of the combined project.
        3. Include installation instructions and dependencies.
        4. Explain how to use the project, including any command-line arguments or configuration files.
        5. Mention any notable changes or enhancements from the original projects.
        6. Include information about testing and how to contribute to the project.

        Please provide the content for the README.md file.
        """

        try:
            self.rate_limiter.wait_if_needed(len(prompt) + SONNET_MAX_TOKENS, is_haiku=False)
            response = self.client.messages.create(
              \model,
                \_MAX_TOKENS,
                messages=[{"role": "user", "content": prompt}]
            )
            readme_content = response.content[0].text
            self.write_file(os.path.join(output_path, "README.md"), readme_content)
        except Exception as e:
            print(f"Error creating README.md: {str(e)}")

    def create_user_manual(self, output_path: str):
        print("Creating user manual...")
        prompt = f"""
        Task: Create a comprehensive user manual for the newly combined and enhanced project.

        Instructions:
        1. Provide detailed instructions on how to set up and configure the project.
     3. Include step-by-step guides for common use cases.
        4. Provide troubleshooting tips and FAQs.
        5. Include any relevant API documentation or command references.

        Please provide the content for the user manual.
        """

        try:
            self.rate_limiter.wait_if_needed(len(prompt) + SONNET_MAX_TOKENS, is_haiku=False)
            response = self.client.messages.create(
             \_MAX_TOKENS,
                messages=[{"role": "user", "content": prompt}]
            )
            manual_content = response.content[0].text
            self.write_file(os.path.join(output_path, "USER_MANUAL.md"), manual_content)
        except Exception as e:
            print(f"Error creating user manual: {str(e)}")

    def load_checkpoint(self, output_path: str) -> Dict:
        checkpoint_path = os.path.join(output_path, CHECKPOINT_FILE)
        if os.path.exists(checkpoint_path):
            with open(checkpoint_path, 'r') as f:
                return json.load(f)
        return {}

    def save_checkpoint(self, output_path: str, checkpoint: Dict):
        checkpoint_path = os.path.join(output_path, CHECKPOINT_FILE)
        with open(checkpoint_path, 'w') as f:
            json.dump(checkpoint, f)

    def clear_checkpoint(self, output_path: str):
        checkpoint_path = os.path.join(output_path, CHECKPOINT_FILE)
        if os.path.exists(checkpoint_path):
            os.remove(checkpoint_path)

def test_project(project_path: str):
    print(f"Testing project in {project_path}")
    try:
        subprocess.run(["python", "-m", "unittest", "discover", project_path], check=True)
        print("All tests passed successfully.")
        return True
    except subprocess.CalledProcessError:
        print("Some tests failed.")
        return False
    except Exception as e:
        print(f"Error during testing: {str(e)}")
        return False

def main():
    api_key =\zbA\hQAA"  # Replace with your actual API key
    enhancer = ProjectEnhancer(api_key)

    original_projects = [
        r"C:\\
        r"C:\Us\
        r"C:\\
    ]

    with tempfile.TemporaryDirectory() as temp_dir:
        cloned_projects = []
        for i, project in enumerate(original_projects):
            clone_path = os.path.join(temp_dir, f"project_{i}")
            enhancer.clone_project(project, clone_path)
            cloned_projects.append(clone_path)

        for i in range(1, 4):
            output_path = f"C:\\\
            enhancer.enhance_projects(cloned_projects, output_path)
            
            if test_project(output_path):
                print(f"\} created successfully and passed tests.")
            else:
                print(f"\\\\\\} created but failed tests. Please review manually.")

    print("All operations completed. Please review the output in C:\enhanced001-100 then back to 001 runs multiple time through loop to greatly enhance and webcrawls and uses knowledge base + memory + analyzed scripts.json to aid it in the

if __name__ == "__main__":
    main()import sys
import os
import asyncio
import json
import re
import aiohttp
from PyQt6.QtWidgets import (
    QApplication, QWidget, QVBoxLayout, QPushButton, QFileDialog,
    QTextEdit, QProgressBar, QMessageBox, QLabel, QTabWidget, QHBoxLayout, 
    QLineEdit, QListWidget, QInputDialog, QSplitter
)
from PyQt6.QtCore import Qt, QThread, pyqtSignal
from PyQt6.QtGui import QPalette
from dotenv import load_dotenv
from transformers import GPTNeoForCausalLM, GPT2Tokenizer
import nltk
from bs4 import BeautifulSoup

# Load environment variables
load_dotenv()

# Configuration
GROQ_API_KEY = os.getenv('GROQ_API_KEY')
MODEL_PATH = os.getenv('MODEL_PATH')
KNOWLEDGE_PATH = os.getenv('KNOWLEDGE_PATH', 'solana_knowledge_base.json')
MEMORY_PATH = os.getenv('MEMORY_PATH', 'memory.json')
ANALYZED_SCRIPTS_PATH = 'analyzed_scripts.json'
RECENT_CONVERSATIONS_PATH = 'recent_conversations.json'
RECENT_URLS_PATH = 'recent_urls.json'

# Constants
MAX_MEMORY_ENTRIES = 200
MAX_ANALYZED_SCRIPTS = 200
MAX_GROQ_TOKENS = 2000

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

# Load model
tokenizer = GPT2Tokenizer.from_pretrained(MODEL_PATH)
model = GPTNeoForCausalLM.from_pretrained(MODEL_PATH).to(device)
model.eval()
print("Model loaded successfully.")

# Load NLTK
nltk.download('punkt', quiet=True)

# Utility functions for JSON file operations
def load_json_file(file_path, default):
    if os.path.exists(file_path):
        with open(file_path, 'r', encoding='utf-8') as f:
            return json.load(f)
    return default

def save_json_file(data, file_path):
    with open(file_path, 'w', encoding='utf-8') as f:
        json.dump(data, f, indent=2)

# Load memory, knowledge base, recent conversations, and recent URLs
memory = load_json_file(MEMORY_PATH, [])
analyzed_scripts = load_json_file(ANALYZED_SCRIPTS_PATH, [])
knowledge_base = load_json_file(KNOWLEDGE_PATH, {})
recent_conversations = load_json_file(RECENT_CONVERSATIONS_PATH, [])
recent_urls = load_json_file(RECENT_URLS_PATH, [])

class Worker(QThread):
    progress = pyqtSignal(int)
    log = pyqtSignal(str)
    finished = pyqtSignal()

    def __init__(self, urls, text):
        super().__init__()
        self.urls = urls
        self.text = text

    async def fetch_url(self, session, url):
        try:
            async with session.get(url) as response:
                if response.status == 200:
                    html = await response.text()
                    return BeautifulSoup(html, 'html.parser').get_text()    
                else:
                    self.log.emit(f"Failed to fetch URL: {url} with status code: {response.status}")
                    return ""
        except Exception as e:
            self.log.emit(f"Error fetching {url}: {e}")
            return ""

    async def scrape_urls(self):
        async with aiohttp.ClientSession() as session:
            tasks = [self.fetch_url(session, url) for url in self.urls]
            return await asyncio.gather(*tasks)

    async def local_model_inference(self, prompt):
        if not isinstance(prompt, str):
            return "Invalid input. Must be a string."
        inputs = tokenizer(prompt, return_tensors="pt").to(device)
        with torch.no_grad():
            try:
                output = model.generate(inputs['input_ids'], max_new_tokens=100)
                return tokenizer.decode(output[0], skip_special_tokens=True)
            except RuntimeError as e:
                return f"CUDA error occurred: {e}"

    async def groq_web_crawl(self, context):
        async with aiohttp.ClientSession() as session:
            messages = [
                {"role": "system", "content": "Retrieve relevant Solana-related snippets."},
                {"role": "user", "content": context}
            ]
            try:
                async with session.post("https://api.groq.com/v1/chat/completions",
                                        json={"messages": messages, "model": 'mixtral-8x7b-32768', "max_tokens": MAX_GROQ_TOKENS},
                                        headers={"Authorization": f"Bearer {GROQ_API_KEY}"}) as response:
                    response_json = await response.json()
                    return response_json["choices"][0]["message"]["content"].strip()
            except Exception as e:
                return f"Groq API error: {e}"

    async def analyze_text(self):
        # Construct context and fetch responses
        context = f"Memory: {json.dumps(memory[-5:], indent=2)}\nUser Message: {self.text}"

        local_response = await self.local_model_inference(self.text)
        groq_response = await self.groq_web_crawl(context)

        return local_response, groq_response

    def run(self):
        self.log.emit("Starting processing...")
        asyncio.run(self.run_crawling_and_analysis())

    async def run_crawling_and_analysis(self):
        if self.urls:
            self.log.emit("Scraping URLs...")
            scraped_texts = await self.scrape_urls()
            for text in scraped_texts:
                if text:
                    local_response, groq_response = await self.analyze_text()
                    self.log.emit(f"Local Response: {local_response}")
                    self.log.emit(f"Groq Response: {groq_response}")

        if self.text:
            self.log.emit("Analyzing provided text...")
            local_response, groq_response = await self.analyze_text()
            self.log.emit(f"Local Response: {local_response}")
            self.log.emit(f"Groq Response: {groq_response}")
        
        self.finished.emit()

# PyQt6 GUI Application
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, 1200, 800)

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

        self.tab1 = QWidget()
        self.tabs.addTab(self.tab1, "Code Analyzer")
        self.tab1UI()

        self.tab2 = QWidget()
        self.tabs.addTab(self.tab2, "AI Chat")
        self.tab2UI()

        self.tab3 = QWidget()
        self.tabs.addTab(self.tab3, "URL Crawler")
        self.tab3UI()

        self.setLayout(layout)

        # Set the style
        self.setStyleSheet("""
            QWidget {
                background-color: #2b2b2b;
                color: #ffffff;
                font-family: Arial;
            }
            QPushButton {
                background-color: #3daee9;
                border: none;
                padding: 8px;
                border-radius: 5px;
            }
            QPushButton:hover {
                background-color: #5ab3e0;
            }
        """)

    def tab1UI(self):
        layout = QVBoxLayout()
        self.title_label = QLabel("Code Analyzer")
        layout.addWidget(self.title_label)

        self.select_script_directory_btn = QPushButton('Select Directory for Scripts')
        layout.addWidget(self.select_script_directory_btn)

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

        self.progress_bar = QProgressBar()
        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)

        self.chat_input = QLineEdit()
        self.chat_input.setPlaceholderText("Type your message here...")
        layout.addWidget(self.chat_input)

        self.send_btn = QPushButton("Send")
        layout.addWidget(self.send_btn)

        self.tab2.setLayout(layout)

    def tab3UI(self):
        layout = QVBoxLayout()
        self.url_list = QListWidget()
        layout.addWidget(self.url_list)

        button_layout = QHBoxLayout()
        add_url_btn = QPushButton("Add URL")
        remove_url_btn = QPushButton("Remove URL")
        button_layout.addWidget(add_url_btn)
        button_layout.addWidget(remove_url_btn)
        layout.addLayout(button_layout)

        self.tab3.setLayout(layout)

    def start_analysis(self):
        self.start_btn.setEnabled(False)
        # Trigger your analysis logic here

# Entry point
if __name__ == '__main__':
    app = QApplication(sys.argv)
    ex = App()
    ex.show()
    sys.exit(app.exec())
combine these 2 scripts and enhance by 5000%