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import streamlit as st
import keras_ocr
import matplotlib.pyplot as plt
from PIL import Image
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
.css-1wbqy5l {visibility: hidden;}
.css-15zrgzn {visibility: hidden;}
.css-klqnuk {visibility: hidden;}
.en6cib64 {visibility: hidden;}
.css-1u4fkce {visibility: hidden;}
.en6cib62 {visibility: hidden;}
.css-19rxjzo, .ef3psqc11 {
background-color: purple;
text-color: white;
}
div.stButton > button:first-child {
background-color: darkgreen;
text-weight: bold;
}
</style>
"""
st.markdown("<h1 style='text-align:center;'> Image Text Recognition</h1>", unsafe_allow_html=True)
st.markdown("<h4 style='text-align:center;'> By Alok</h4>", unsafe_allow_html=True)
st.markdown("---")
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
placeholder=st.empty()
col1,col2=placeholder.columns(2)
col1.image("https://i.ibb.co/HdRnpzQ/example1.png", width=250)
col2.markdown("<p style='text-align:left;'> This is an example result</p>", unsafe_allow_html=True)
# Read images from folder path to image object
st.markdown("<h2>Upload an Image with Text</h2>",unsafe_allow_html=True)
uploaded_img= st.file_uploader("Browse File", type=['jpeg','jpg','png'],accept_multiple_files=False)
#container2=st.empty()
def on_click():
if uploaded_img is not None:
pipeline = keras_ocr.pipeline.Pipeline()
print(uploaded_img.name)
images = [
keras_ocr.tools.read(img) for img in [uploaded_img
]]
# generate text predictions from the images
prediction_groups = pipeline.recognize(images)
# plot the text predictions
fig, axs = plt.subplots(nrows=len(images), figsize=(10, 20))
#for ax, image, predictions in zip(axs, images, prediction_groups):
#keras_ocr.tools.drawAnnotations(image=images[0],predictions=prediction_groups[0], ax=axs)
keras_ocr.tools.drawAnnotations(image=images[0], predictions=prediction_groups[0], ax=axs )
columnbelow[0].pyplot(fig)
print(images)
if uploaded_img is not None:
btn=st.button("Recognize Text", on_click=on_click)
belowrecognize=st.empty()
columnbelow=belowrecognize.columns(1) #Returns list
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