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Python Script: solana_sniper_bot_analyzer.py
python import sys import os import asyncio import json import subprocess import ast import re import tempfile 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, QListWidget, QInputDialog ) from PyQt6.QtCore import Qt, QThread, pyqtSignal import aiohttp from groq import AsyncGroq from bs4 import BeautifulSoup import toml from aiolimiter import AsyncLimiter import torch from transformers import GPTNeoForCausalLM, GPT2Tokenizer import nltk import spacy
Load environment variables
load_dotenv()
Environment variables
GROQ_API_KEY = os.getenv('GROQ_API_KEY') GROQ_MODEL = os.getenv('GROQ_MODEL', 'mixtral-8x7b-32768') GPT_NEO_MODEL_NAME = os.getenv('GPT_NEO_MODEL', 'EleutherAI/gpt-neo-2.7B') KNOWLEDGE_PATH = os.getenv('KNOWLEDGE_PATH', 'solana_knowledge_base.json') MEMORY_PATH = os.getenv('MEMORY_PATH', 'memory.json') ANALYZED_SCRIPTS_PATH = 'analyzed_scripts.json'
Constants
MAX_MEMORY_ENTRIES = 200 MAX_ANALYZED_SCRIPTS = 200 MAX_GROQ_TOKENS = 1000 MAX_LOCAL_INFERENCE_LENGTH = 1000
Initialize clients and models
groq_client = AsyncGroq(api_key=GROQ_API_KEY) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") tokenizer = GPT2Tokenizer.from_pretrained(GPT_NEO_MODEL_NAME) model = GPTNeoForCausalLM.from_pretrained(GPT_NEO_MODEL_NAME).to(device) model.eval()
Rate limiters
token_limiter = AsyncLimiter(5000, 60) call_limiter = AsyncLimiter(30, 60)
NLP setup
nltk.download('punkt', quiet=True) nltk.download('stopwords', quiet=True) nltk.download('wordnet', quiet=True) nlp = spacy.load('en_core_web_sm')
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 data
memory = load_json_file(MEMORY_PATH, []) analyzed_scripts = load_json_file(ANALYZED_SCRIPTS_PATH, []) knowledge_base = load_json_file(KNOWLEDGE_PATH, {})
Function to analyze a file and return results
def analyze_file(file_path): analysis = { 'file_name': os.path.basename(file_path), 'content_length': 0, 'imports': [], 'functions': [], 'structs': [], }
javascript

with open(file_path, 'r', encoding='utf-8') as file:
    content = file.read()
    analysis['content_length'] = len(content)
    
    if file_path.endswith('.py'):
        try:
            tree = ast.parse(content)
            for node in ast.walk(tree):
                if isinstance(node, ast.Import):
                    analysis['imports'].extend(n.name for n in node.names)
                elif isinstance(node, ast.ImportFrom):
                    analysis['imports'].extend(f"{node.module}.{n.name}" for n in node.names)
                elif isinstance(node, ast.FunctionDef):
                    analysis['functions'].append(node.name)
                elif isinstance(node, ast.ClassDef):
                    analysis['structs'].append(node.name)
        except SyntaxError as e:
            print(f"Syntax error in {file_path}: {e}")

    elif file_path.endswith('.rs'):
        for line in content.split('\n'):
            if line.strip().startswith('use '):
                analysis['imports'].append(line.strip())
            elif match := re.match(r'fn\s+(\w+)', line.strip()):
                analysis['functions'].append(match.group(1))
            elif match := re.match(r'struct\s+(\w+)', line.strip()):
                analysis['structs'].append(match.group(1))

return analysis

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

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', '.rs'))]
    for i, file in enumerate(files):
        analysis = analyze_file(os.path.join(self.folder, file))
        self.log.emit(f"Analyzed {analysis['file_name']}: {analysis['content_length']} chars")
        if not any(a['file_name'] == analysis['file_name'] for a in analyzed_scripts):
            analyzed_scripts.append(analysis)
            if len(analyzed_scripts) > MAX_ANALYZED_SCRIPTS:
                analyzed_scripts.pop(0)
        self.progress.emit(int((i + 1) / len(files) * 100))
    save_json_file(analyzed_scripts, ANALYZED_SCRIPTS_PATH)
    self.finished.emit()

Functions to manage memory
def append_to_memory(entry): memory.append(entry) if len(memory) > MAX_MEMORY_ENTRIES: memory.pop(0) save_json_file(memory, MEMORY_PATH)
Local model inference
def local_model_inference(prompt, max_length=MAX_LOCAL_INFERENCE_LENGTH): inputs = tokenizer(prompt, return_tensors="pt").to(device) with torch.no_grad(): output = model.generate(**inputs, max_length=max_length, num_return_sequences=1) return tokenizer.decode(output[0], skip_special_tokens=True)
Efficient web crawling for Groq
async def efficient_web_crawl(urls): async with aiohttp.ClientSession() as session: tasks = [fetch_url(session, url) for url in urls] results = await asyncio.gather(*tasks) return " ".join(results) async def fetch_url(session, url): async with session.get(url) as response: html = await response.text() soup = BeautifulSoup(html, 'html.parser') return soup.get_text()
Groq API call for web crawling
async def groq_web_crawl(query, urls): crawled_text = await efficient_web_crawl(urls)
javascript

async with call_limiter:
    async with token_limiter:
        chat_completion = await groq_client.chat.completions.create(
            messages=[
                {"role": "system", "content": "Find and extract high-quality Solana code snippets and documentation from the following content:"},
                {"role": "user", "content": f"{crawled_text}\n\nQuery: {query}"}
            ],
            model=GROQ_MODEL,
            max_tokens=MAX_GROQ_TOKENS,
        )
return chat_completion.choices[0].message.content.strip()

Code testing function
async def test_code(code, language): with tempfile.NamedTemporaryFile(mode='w', suffix=f'.{language}', delete=False) as temp_file: temp_file.write(code) temp_file_path = temp_file.name
javascript

if language in ['py']:
    result = await run_python(temp_file_path)
elif language in ['rs']:
    result = await run_rust(temp_file_path)
else:
    result = "Unsupported language"

os.unlink(temp_file_path)
return result

async def run_python(file_path): try: result = subprocess.run(['python', file_path], capture_output=True, text=True, timeout=5) return result.stdout if result.returncode == 0 else f"Error: {result.stderr}" except subprocess.TimeoutExpired: return "Error: Execution timed out" async def run_rust(file_path): try: compile_result = subprocess.run(['rustc', file_path, '-o', 'temp_rust_executable'], capture_output=True, text=True, timeout=10) if compile_result.returncode != 0: return f"Compilation Error: {compile_result.stderr}"
javascript

    run_result = subprocess.run(['./temp_rust_executable'], capture_output=True, text=True, timeout=5)
    os.unlink('temp_rust_executable')
    return run_result.stdout if run_result.returncode == 0 else f"Runtime Error: {run_result.stderr}"
except subprocess.TimeoutExpired:
    return "Error: Execution timed out"

Process chat messages
async def process_chat_message(message, urls): context = f""" Memory: {json.dumps(memory[-5:])} Analyzed Scripts: {json.dumps(analyzed_scripts[-5:])} Knowledge Base: {json.dumps(list(knowledge_base.items())[:5])} User Message: {message} """
javascript

local_response = local_model_inference(context)

if "need more information" in local_response.lower():
    groq_response = await groq_web_crawl(message, urls)
    knowledge_base[message] = groq_response
    save_json