tarunlnmiit

import random
import requests
import streamlit as st

UNSPLASH_ACCESS_KEY = "<API KEY GOES HERE>"
UNSPLASH_PHOTOS_URL = "https://api.unsplash.com/search/photos?"

def main():
    st.title('Social Media Magic')
    with st.form(key='unsplash_images', clear_on_submit=True):

tarunlnmiit

import streamlit as st
from langchain.chains import ConversationChain
from langchain.chains.conversation.memory import ConversationEntityMemory
from langchain.chains.conversation.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE
from langchain.chat_models import ChatOpenAI


def main():
    st.title("ChatGPT ChatBot🤖")
    st.markdown(

tarunlnmiit

tarunlnmiit

tarunlnmiit

from sklearn import datasets
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report


iris = datasets.load_iris()
X = iris.data

tarunlnmiit

#!/usr/bin/env python
# coding: utf-8

from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from time import sleep, strftime
from random import randint
import pandas as pd

tarunlnmiit

tarunlnmiit

def calculatePredicatedValue(X, W):
    f_x = np.dot(X, W)
    for i in range(len(f_x)):
        if f_x[i][0] > 0:
            f_x[i][0] = 1
        else:
            f_x[i][0] = 0
    return f_x

tarunlnmiit

def calculateError(Y, f_x):
    errorCount = 0
    for i in range(len(f_x)):
        if Y[i][0] != f_x[i][0]:
            errorCount += 1

    return errorCount

tarunlnmiit

def calculateGradient(W, X, Y, f_x, learningRate):
    gradient = (Y - f_x) * X
    gradient = np.sum(gradient, axis=0)
    # gradient = np.array([float("{0:.4f}".format(val)) for val in gradient])
    temp = np.array(learningRate * gradient).reshape(W.shape)
    W = W + temp
    return gradient, W.astype(float)