sunilkumardash9

def add_text(history, text):
    global messages  #message[list] is defined globally
    history = history + [(text,'')]
    messages = messages + [{"role":'user', 'content': text}]
    return history, ""

sunilkumardash9

def generate_response(history, model ):
        global messages, cost

        response = openai.ChatCompletion.create(
            model = model,
            messages=messages,
            temperature=0.2,
        )

        response_msg = response.choices[0].message.content

sunilkumardash9

with gr.Blocks() as demo:
    
    radio = gr.Radio(value='gpt-3.5-turbo', choices=['gpt-3.5-turbo','gpt-4'], label='models')
    chatbot = gr.Chatbot(value=[], elem_id="chatbot").style(height=650)
    with gr.Row():
        with gr.Column(scale=0.90):
            txt = gr.Textbox(
                show_label=False,
                placeholder="Enter text and press enter",
            ).style(container=False) 

sunilkumardash9

from fastapi import FastAPI
from pydantic import BaseModel
from joblib import load
import pandas as pd
import json
import uvicorn

app = FastAPI()

model = load('my-model2')

sunilkumardash9

import streamlit as st
import requests
import json
from requests import ConnectionError

st.title('HR-analytics App') #title to be shown
st.image('office.jpg') #add an image
st.header('Enter the employee data:') #header to be shown in app

satisfaction_level = st.number_input('satisfaction level',min_value=0.00, max_value=1.00)

sunilkumardash9

from fastapi import FastAPI
from joblib import load
import regex as re
from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS
from spamClassify import my_classifier
#from sklearn.feature_extraction.text import TfidfVectorizer
#from xgboost import XGBRFClassifier


app = FastAPI()

sunilkumardash9

import numpy as np
from numpy import log,dot,e,shape
import matplotlib.pyplot as plt

sunilkumardash9

def standardize(X_tr):
    for i in range(shape(X_tr)[1]):
        X_tr[:,i] = (X_tr[:,i] - np.mean(X_tr[:,i]))/np.std(X_tr[:,i])

sunilkumardash9

def initialize(self,X):
    weights = np.zeros((shape(X)[1]+1,1))
    X = np.c_[np.ones((shape(X)[0],1)),X]
    return weights,X

sunilkumardash9

from sklearn.datasets import make_classification
X,y = make_classification(n_features=4)
#spliting train,test data
from sklearn.model_selection import train_test_split
X_tr,X_te,y_tr,y_te = train_test_split(X,y,test_size=0.15)