import streamlit as st
import cv2
import numpy as np
from PIL import Image
st.title('Image to Sketch')
file=st.file_uploader('Upload an portrait image', type=['jpg','png','jpeg'])
if file:
img=Image.open(file).convert('RGB')
img2=np.array(img)
img_bw=cv2.cvtColor(img2,cv2.COLOR_RGB2BGR)
gray=cv2.cvtColor(img_bw,cv2.COLOR_RGB2GRAY)
inverted=255-gray
blurred=cv2.GaussianBlur(inverted,(21,21),0)
def dodge(front,back):
return cv2.divide(front, 255-back, scale=255)
sketch=dodge(gray,blurred)
st.subheader('Orginal vs Sketch')
col1,col2=st.columns(2)
with col1:
st.image(img, caption='Original',use_container_width=True)
with col2:
st.image(sketch, caption='Sketch',use_container_width=True)
import streamlit as st
from gtts import gTTS
st.title('Text to Speech')
text=st.text_area('Enter text')
if text:
tts=gTTS(text)
tts.save('ses.mp3')
ses=open('ses.mp3','rb')
st.audio(ses.read())
import streamlit as st
import pickle
st.title('Tecrübe, yazili sinav ve mülakata göre maas tahmini : heavy_dollar_sign:')
model=pickle.load(open('maas.pkl','rb'))
tecrube=st.number_input('tercube',1,10)
yazili=st.number_input('yazili',1,10)
sozlu=st.number_input('sozlu',1,10)
if st.button('tahmin et'):
tahmin=model.predict([[tecrube,yazili,sozlu]])
tahmin=round(tahmin[0][0],2)
st.success(f'YZ tahmin edilen maasiniz:{tahmin}')
import streamlit as st
import cv2
import numpy as np
from PIL import Image
st.title('Image to Sketch')
file=st.file_uploader('Upload an portrait image', type=['jpg','png','jpeg'])
if file:
img=Image.open(file).convert('RGB')
img2=np.array(img)
img_bw=cv2.cvtColor(img2,cv2.COLOR_RGB2BGR)
gray=cv2.cvtColor(img_bw,cv2.COLOR_RGB2GRAY)
inverted=255-gray
blurred=cv2.GaussianBlur(inverted,(21,21),0)
def dodge(front,back):
return cv2.divide(front, 255-back, scale=255)
sketch=dodge(gray,blurred)
st.subheader('Orginal vs Sketch')
col1,col2=st.columns(2)
with col1:
st.image(img, caption='Original',use_container_width=True)
with col2:
st.image(sketch, caption='Sketch',use_container_width=True)
