from solana.rpc.api import Client
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
import smtplib
from email.mime.text import MIMEText

client = Client("https://api.mainnet-beta.solana.com")

def get_transactions(wallet_address, limit=100):
    response = client.get_confirmed_signature_for_address2(wallet_address, limit=limit)
    return response['result']

def analyze_emotions(df):
    df['timestamp'] = pd.to_datetime(df['blockTime'], unit='s')
    df['transaction_volume'] = df['meta']['preBalances'] - df['meta']['postBalances']
    df['transaction_speed'] = df['blockTime'].diff().fillna(0)
    features = df[['transaction_volume', 'transaction_speed']].values
    scaler = StandardScaler()
    scaled_features = scaler.fit_transform(features)
    kmeans = KMeans(n_clusters=3, random_state=0)
    df['emotion'] = kmeans.fit_predict(scaled_features)
    return df

def send_email_alert(subject, body, to_email):
    msg = MIMEText(body)
    msg['Subject'] = subject
    msg['From'] = "[email protected]"
    msg['To'] = to_email
    server = smtplib.SMTP('smtp.example.com', 587)
    server.starttls()
    server.login("[email protected]", "your_password")
    server.sendmail("[email protected]", to_email, msg.as_string())
    server.quit()

def check_for_alerts(df):
    if df['emotion'].iloc[-1] != df['emotion'].iloc[-2]:
        send_email_alert("Market Alert: Emotion-based trend change", 
                          f"New predicted market emotion: {df['emotion'].iloc[-1]}", 
                          "[email protected]")

wallet_address = "YourWalletAddress"
transactions = get_transactions(wallet_address)
df = pd.DataFrame(transactions)
df = analyze_emotions(df)
check_for_alerts(df)