RSI python numpy code needed

Algos, strategies, code
1

I developed below code for the RSI, however, the result is not matching with the Zerodha charts. Kindly help me correct the code:

import numpy as np

def rsi(prices, n=14):
    diff = np.diff(prices)
    gains = np.maximum(diff, 0)
    losses = -np.minimum(diff, 0)
    avg_gains = np.convolve(gains, np.ones(n), "valid")
    avg_loss = np.convolve(losses, np.ones(n), "valid")
    rs = avg_gains/avg_loss
    rsi = 100 - (100 / (1 + rs))
    return rsi

SBIN_CLOSING = np.array([563.05, 563.75, 561.50, 558.95, 559.95, 560.35, 564.45, 568.60, 564.75, 571.75, 594.70, 608.25, 608.45, 611.70, 614.15, 614.25, 612.40, 619.85, 623.65, 648.25])

rsi(SBIN_CLOSING)
2

I wrote below python code which matches the RSI of the Zerodha charts. Hope this code would help other community members as well. Suggestions are welcomed.

import numpy as np

def rsi(prices, n=14):
    """
    Returns a numpy array containing Relative Strength Index from nth Day (by default n=14)
    """
    diff = np.diff(prices) # Price change
    gains = np.maximum(diff, 0) # If gain, returns the amount of gain, 0 otherwise
    losses = -np.minimum(diff, 0) # If loss, returns the absolute amount of loss, 0 otherwise
    init_avg_gain = np.sum(gains[:n])/n # Average gain on the nth day
    init_avg_loss = np.sum(losses[:n])/n # Average loss on the n4th day
    avg_gains = smoothing_avg(init_avg_gain, gains, n) # Numpy array containing average gains from the nth day
    avg_losses = smoothing_avg(init_avg_loss, losses, n) # Numpy array containing average losses from the nth day
    rs = avg_gains/avg_losses # Relative Strength
    rsi = 100 - (100 / (1 + rs)) # Relative Strength Index
    return rsi #Returning Relative Strength Index

def smoothing_avg(init_value, value, n):
    """
    Returns a numpy array containing smoothing average from nth Day
    """
    avg = np.zeros(len(value)-n+1) # Numpy array of one-dimentional null matrix
    avg[0] = init_value # Setting nth days average value as the first value of the null matrix
    for element in range(1,len(avg)): # Looping over the null entries of the null matrix
        avg[element] = ((avg[element-1]*(n-1))+value[n+element-1])/n # Calculating smoothing average
    return avg # Returning smoothing average