can you please write this script exactly how i gave it to you all the words the same everything the same just fix the spacing and make sure the brackets are correct please use pyo3::prelude::*;
use {
    std::{sync::Arc, time::{Duration, Instant}, path::PathBuf, fs::{create_dir_all, File}},
    tokio::time::sleep,
    serde_json::{json, Value},
    anyhow::{anyhow, Result},
    log::{error, info, warn},
    dashmap::DashMap,
    solana_client::rpc_client::RpcClient,
    solana_sdk::{
        signature::Keypair,
        signer::Signer,
        commitment_config::CommitmentConfig,
    },
    tokio_tungstenite::{connect_async, WebSocketStream, MaybeTlsStream},
    tokio::net::TcpStream,
    csv::Writer,
};

// Constants
pub const JUPITER_V6_API: &str = "https://quote-api.jup.ag/v6";
pub const HELIUS_RPC: &str = "https://marga-e56m84-fast-mainnet.helius-rpc.com";
pub const WS_URL: &str = "wss://marga-e56m84-fast-mainnet.helius-rpc.com";
pub const GROQ_API: &str = "https://api.groq.com/v1/completions";
pub const GROQ_MODEL: &str = "mixtral-8x7b-32768";
pub const SOL_MINT: &str = "So11111111111111111111111111111111111111112";
pub const PRIORITY_FEE: u64 = 15000;
pub const RETRY_ATTEMPTS: u32 = 3;
pub const RETRY_DELAY: Duration = Duration::from_millis(100);
pub const TRADE_AMOUNT_SOL: f64 = 0.01;
pub const MIN_LIQUIDITY_USD: f64 = 1000.0;
pub const MAX_LIQUIDITY_USD: f64 = 20000.0;
pub const SLIPPAGE_BPS: u32 = 1000; // 10%
pub const POLL_INTERVAL: Duration = Duration::from_secs(1);

type WebSocketConnection = WebSocketStream<MaybeTlsStream<TcpStream>>;

#[derive(Debug, Clone)]
struct MLPrediction {
   confidence: f64,
   action: String,
   price_target: Option<f64>,
}

#[derive(Debug, Clone)]
struct ActiveTrade {
   token_mint: String,
   entry_price: f64,
   amount: u64,
   timestamp: i64,
   last_price: f64,
   take_profit: f64,
   stop_loss: f64,
   volume_24h: f64,
   liquidity: f64,
   ml_prediction: Option<MLPrediction>,
}

#[derive(Debug, Clone)]
struct TradeMetrics {
   total_trades: u64,
   successful_trades: u64,
   total_profit: f64,
   avg_execution_time: Duration,
   prediction_accuracy: f64,
}

struct TrainingData {
   price_history: Vec<f64>,
   volume_history: Vec<f64>,
   outcomes: Vec<bool>,
}

pub struct TradeManager {
   rpc_client: Arc<RpcClient>,
   keypair: Arc<Keypair>,
   active_trades: Arc<DashMap<String, ActiveTrade>>,
   trade_metrics: Arc<DashMap<String, TradeMetrics>>,
   ws_connection: Arc<tokio::sync::Mutex<Option<WebSocketConnection>>>,
   http_client: Arc<reqwest::Client>,
   training_data: Arc<DashMap<String, TrainingData>>,
   csv_writer: Arc<tokio::sync::Mutex<Writer<File>>>,
}

impl TradeManager {
    pub fn new(keypair: Keypair) -> Self {
        let rpc_client = RpcClient::new_with_commitment(
            HELIUS_RPC.to_string(),
            CommitmentConfig::confirmed(),
        );
        
        // Initialize CSV writer
        let csv_path = PathBuf::from("trading_data.csv");
        let csv_file = File::create(csv_path).expect("Failed to create CSV file");
        let csv_writer = Writer::from_writer(csv_file);
 
        Self {
            rpc_client: Arc::new(rpc_client),
            keypair: Arc::new(keypair),
            active_trades: Arc::new(DashMap::new()),
            trade_metrics: Arc::new(DashMap::new()),
            ws_connection: Arc::new(tokio::sync::Mutex::new(None)),
            http_client: Arc::new(reqwest::Client::new()),
            training_data: Arc::new(DashMap::new()),
            csv_writer: Arc::new(tokio::sync::Mutex::new(csv_writer)),
        }
    }
 
    async fn get_ml_prediction(&self, token_data: &Value) -> Result<MLPrediction> {
        let prompt = format!(
            "Analyze this token data and predict trade action:\n{}\n",
            serde_json::to_string_pretty(token_data)?
        );
 
        let response = self.http_client
            .post(GROQ_API)
            .header("Authorization", format!("Bearer {}", std::env::var("GROQ_API_KEY")?))
            .json(&json!({
                "model": GROQ_MODEL,
                "messages": [{
                    "role": "system",
                    "content": "You are a crypto trading AI. Analyze token data and provide trade predictions."
                }, {
                    "role": "user",
                    "content": prompt
                }]
            }))
            .send()
            .await?
            .json::<Value>()
            .await?;
 
        let prediction = MLPrediction {
            confidence: response["choices"][0]["message"]["score"].as_f64().unwrap_or(0.0),
            action: response["choices"][0]["message"]["content"].as_str().unwrap_or("HOLD").to_string(),
            price_target: response["choices"][0]["message"]["price_target"].as_f64(),
        };
 
        Ok(prediction)
    }
 
    async fn record_training_data(&self, token_mint: &str, trade_result: &Value) -> Result<()> {
        let mut writer = self.csv_writer.lock().await;
        writer.serialize(json!({
            "token": token_mint,
            "timestamp": chrono::Utc::now().timestamp(),
            "price": trade_result["price"],
            "volume": trade_result["volume"],
            "liquidity": trade_result["liquidity"],
            "success": trade_result["success"],
            "profit_loss": trade_result["profit_loss"],
            "prediction_accuracy": trade_result["prediction_accuracy"]
        }))?;
        writer.flush()?;
        Ok(())
    }
 
    async fn update_ml_model(&self) -> Result<()> {
        // Load training data
        let mut data = csv::Reader::from_path("trading_data.csv")?;
        for result in data.deserialize() {
            let record: Value = result?;
            // Update training data map
            if let Some(token) = record["token"].as_str() {
                self.training_data.entry(token.to_string()).or_insert(TrainingData {
                    price_history: Vec::new(),
                    volume_history: Vec::new(),
                    outcomes: Vec::new(),
                }).and_modify(|data| {
                    data.price_history.push(record["price"].as_f64().unwrap_or(0.0));
                    data.volume_history.push(record["volume"].as_f64().unwrap_or(0.0));
                    data.outcomes.push(record["success"].as_bool().unwrap_or(false));
                });
            }
        }
        Ok(())
    }
    async fn monitor_trades(&self) -> Result<()> {
        let mut interval = tokio::time::interval(POLL_INTERVAL);
 
        loop {
            interval.tick().await;
 
            for trade in self.active_trades.iter() {
                let token_mint = trade.key().clone();
                let mut trade_data = trade.value().clone();
 
                match self.fetch_token_price(&token_mint).await {
                    Ok(current_price) => {
                        trade_data.last_price = current_price;
                        let pl_percent = (current_price - trade_data.entry_price) / trade_data.entry_price * 100.0;
 
                        // Get ML prediction
                        let token_data = self.get_token_data(&token_mint).await?;
                        let prediction = self.get_ml_prediction(&token_data).await?;
                        trade_data.ml_prediction = Some(prediction.clone());
 
                        // Log trade status with ML insights
                        info!(
                            "Token: {} | Price: ${:.6} | P/L: {:.2}% | ML Confidence: {:.2}% | Action: {}",
                            token_mint, current_price, pl_percent, prediction.confidence * 100.0, prediction.action
                        );
 
                        // Check ML-adjusted exit conditions
                        let should_exit = if prediction.confidence > 0.8 {
                            prediction.action == "SELL" || 
                            current_price <= trade_data.stop_loss || 
                            current_price >= trade_data.take_profit
                        } else {
                            current_price <= trade_data.stop_loss || 
                            current_price >= trade_data.take_profit
                        };
 
                        if should_exit {
                            info!("🔄 ML-advised exit for {}", token_mint);
                            if let Err(e) = self.execute_sell(&token_mint).await {
                                error!("Failed to execute exit: {}", e);
                            }
                        }
 
                        self.active_trades.insert(token_mint.clone(), trade_data);
                    }
                    Err(e) => {
                        error!("Failed to fetch price for {}: {}", token_mint, e);
                        continue;
                    }
                }
            }
 
            // Regular model updates
            if let Err(e) = self.update_ml_model().await {
                error!("Failed to update ML model: {}", e);
            }
 
            // Save state periodically
            if let Err(e) = self.save_state().await {
                error!("Failed to save state: {}", e);
            }
        }
    }
 
    async fn execute_buy(&self, token_mint: &str) -> Result<String> {
        let start = Instant::now();
        info!("🚀 Initiating buy for {}", token_mint);
 
        // Get ML prediction before trade
        let token_data = self.get_token_data(token_mint).await?;
        let prediction = self.get_ml_prediction(&token_data).await?;
 
        if prediction.confidence > 0.7 && prediction.action != "BUY" {
            return Err(anyhow!("ML model advises against buying"));
        }
 
        // Rest of buy execution logic...
        let (volume, liquidity) = self.get_token_metrics(token_mint).await?;
        if !self.validate_trading_requirements(volume, liquidity)? {
            return Err(anyhow!("Token doesn't meet trading requirements"));
        }
 
        let amount_lamports = (TRADE_AMOUNT_SOL * 1e9) as u64;
        let quote = self.get_quote_with_retry(SOL_MINT, token_mint, amount_lamports).await?;
        let swap_data = self.get_swap_data(&quote).await?;
        let signature = self.send_priority_tx(&swap_data).await?;
        self.verify_transaction(&signature).await?;
 
        let price = self.fetch_token_price(token_mint).await?;
        self.record_trade(token_mint, price, amount_lamports, volume, liquidity).await;
 
        // Record training data
        self.record_training_data(token_mint, &json!({
            "price": price,
            "volume": volume,
            "liquidity": liquidity,
            "success": true,
            "prediction_accuracy": 1.0
        })).await?;
 
        info!("✅ Buy executed in {:?} | Signature: {}", start.elapsed(), signature);
        Ok(signature)
    }

#[pyclass]
struct QuantumCore {
    trade_manager: TradeManager,
}
    
#[pymethods]
impl QuantumCore {
    #[new]
    fn new(pool_id: String) -> Self {
        let keypair = Keypair::new();
        let trade_manager = TradeManager::new(keypair);
        Self { trade_manager }
    }
    
    pub fn monitor_pool(&mut self) -> PyResult<()> {
        tokio::spawn(async move {
            if let Err(e) = self.trade_manager.start().await {
                error!("Error: {}", e);
            }
        });
        Ok(())
    }
    
    pub fn get_stats(&self) -> PyResult<String> {
        let stats = tokio::runtime::Runtime::new()
            .unwrap()
            .block_on(self.trade_manager.get_trade_stats())
            .map_err(|e| PyErr::new::<pyo3::exceptions::PyRuntimeError, _>(e.to_string()))?;
        Ok(serde_json::to_string(&stats).unwrap())
    }
    
    pub fn predict_trade(&self, token_mint: String) -> PyResult<String> {
        let rt = tokio::runtime::Runtime::new().unwrap();
        let token_data = rt.block_on(self.trade_manager.get_token_data(&token_mint))
            .map_err(|e| PyErr::new::<pyo3::exceptions::PyRuntimeError, _>(e.to_string()))?;
               
        let prediction = rt.block_on(self.trade_manager.get_ml_prediction(&token_data))
            .map_err(|e| PyErr::new::<pyo3::exceptions::PyRuntimeError, _>(e.to_string()))?;
               
        Ok(serde_json::to_string(&prediction).unwrap())
    }
    
    pub fn execute_trade(&self, token_mint: String, is_buy: bool) -> PyResult<String> {
        let rt = tokio::runtime::Runtime::new().unwrap();
        let result = if is_buy {
            rt.block_on(self.trade_manager.execute_buy(&token_mint))
        } else {
            rt.block_on(self.trade_manager.execute_sell(&token_mint))
        }.map_err(|e| PyErr::new::<pyo3::exceptions::PyRuntimeError, _>(e.to_string()))?;
           
        Ok(result)
    }
    
    pub fn get_ml_metrics(&self) -> PyResult<String> {
        let metrics = self.trade_manager.trade_metrics.iter()
            .map(|entry| {
                  json!({
                    "token": entry.key(),
                    "prediction_accuracy": entry.value().prediction_accuracy,
                    "total_trades": entry.value().total_trades,
                    "successful_trades": entry.value().successful_trades
                })
            })
            .collect::<Vec<_>>();
               
        Ok(serde_json::to_string(&metrics).unwrap())
    }
}
    
#[pymodule]
fn solana_rust_bot(_py: Python, m: &PyModule) -> PyResult<()> {
    m.add_class::<QuantumCore>()?;
    Ok(())
}
#[pyclass]
struct QuantumCore {
    trade_manager: TradeManager,
}

#[pymethods]
impl QuantumCore {
    #[new]
    fn new(pool_id: String) -> Self {
        let keypair = Keypair::new();
        let trade_manager = TradeManager::new(keypair);
        Self { trade_manager }
    }
}