\"canvas_code_execution\",\"canvas_code_network_access\",\"canvas_o1\",\"canvas_opt_in\",\"chat_preferences_available\",\"chatgpt_ios_attest\",\"d3_controls\",\"d3_editor_gpts\",\"gizmo_canvas_toggle\",\"gizmo_reviews\",\"gizmo_support_emails\",\"model_ab_use_v2\",\"new_plugin_oauth_endpoint\",\"no_auth_training_enabled_by

\"canvas_code_execution\",\"canvas_code_network_access\",\"canvas_o1\",\"canvas_opt_in\",\"chat_preferences_available\",\"chatgpt_ios_attest\",\"d3_controls\",\"d3_editor_gpts\",\"gizmo_canvas_toggle\",\"gizmo_reviews\",\"gizmo_support_emails\",\"model_ab_use_v2\",\"new_plugin_oauth_endpoint\",\"no_auth_training_enabled_by_default\",\"o3-mini\",

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fetch_plinko_data()

import requests import openai import pandas as pd import numpy as np from bs4 import BeautifulSoup from dotenv import load_dotenv import os import time # Load environment variables from .env file load_dotenv() variables openai.api_key = os.getenv('OPENAI_API_KEY=sk-5k98H4I4kx3m234Lf1VfJ8wETc5RrTu5KlW') # # Function to fetch live Plinko data def fetch_plinko_data(): url = "https://1xlite-581089.top/en/allgamesentrance/plinko" response = requests.get(url) if response.status_code != 200: print("Failed to fetch data") return None soup = BeautifulSoup(response.text, 'html.parser') game_data = [] for row in soup.find_all("div", class_="game-row"): try: bet_amount = row.find("span", class_="bet-amount").text.strip() multiplier = row.find("span", class_="multiplier").text.strip() win_loss = row.find("span", class_="win-loss").text.strip() bet_amount = float(bet_amount.replace("BDT", "").strip()) multiplier = float(multiplier) win_loss = float(win_loss.replace("BDT", "").strip()) game_data.append({ "bet_amount": bet_amount, "multiplier": multiplier, "win_loss": win_loss }) except AttributeError: continue # Skip rows with missing data return game_data # Store round data in a log round_log = [] def log_round_data(bet_amount, multiplier, win_loss): round_log.append({ "bet_amount": bet_amount, "multiplier": multiplier, "win_loss": win_loss }) def get_round_log_df(): return pd.DataFrame(round_log) # Function to generate dynamic betting strategy using GPT-3 def smart_betting_strategy(current_rounds): history = "\n".join([f"Round {i+1}: Bet = {r['bet_amount']} BDT, Multiplier = {r['multiplier']}, Outcome = {r['win_loss']} BDT" for i, r in enumerate(current_rounds)] ) prompt = f"Given the following round outcomes, suggest a smart betting strategy for the next round:\n\n{history}\n\nSuggested Strategy:" response = openai.Completion.create( engine="gpt-3.5-turbo", # Change to your preferred GPT model prompt=prompt, max_tokens=150, n=1, stop=None, temperature=0.7 ) strategy = response.choices[0].text.strip() return strategy # Function to calculate volatility def calculate_volatility(df): return np.std(df["multiplier"]) # Adjust bet size based on volatility def adjust_bet_size_based_on_volatility(volatility, base_bet=20): if volatility > 0.5: return base_bet * 0.8 elif volatility < 0.2: return base_bet * 1.2 else: return base_bet # Automated loop to run the betting system def run_auto_betting_system(): bet_amount = 20 # Base bet round_number = 1 while round_number <= 100: # Running for 100 rounds print(f"\nRound {round_number}:") # Fetch live Plinko data plinko_data = fetch_plinko_data() if plinko_data is None: print("Failed to fetch data, skipping round.") continue # Log the fetched data for data in plinko_data: log_round_data(data['bet_amount'], data['multiplier'], data['win_loss']) # Get the current round log and calculate volatility df = get_round_log_df() volatility = calculate_volatility(df) adjusted_bet = adjust_bet_size_based_on_volatility(volatility) print(f"Adjusted Bet Size: {adjusted_bet} BDT") # Get strategy from OpenAI strategy = smart_betting_strategy(df.to_dict(orient='records')) print("Suggested Strategy:", strategy) # Simulate next round (or interact with actual game API if available) round_number += 1 time.sleep(2) # Simulate a delay between rounds # Run the betting system if __name__ == "__main__": run_auto_betting_system()

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