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app.py
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app.py
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import streamlit as st
import cv2
import numpy as np
import pickle
from tensorflow.keras.models import load_model
from preprocess import preprocess_image
from streamlit_drawable_canvas import st_canvas
# Set the page title and favicon
st.set_page_config(page_title="BD-HDR",
page_icon="figures/plot_words.png", layout="wide", initial_sidebar_state="expanded")
st.markdown('<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css" integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous">', unsafe_allow_html=True)
# open css file
with open('style.css') as f:
st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
# Load the trained CNN model
model = load_model("cnn_model.h5", compile=False)
lb = pickle.load(open("label_binarizer.pkl", "rb"))
# Streamlit app
def main():
st.markdown(
"""
<style>
section[data-testid="stSidebar"] {
width: 245px !important; # Set the width to your desired value
}
</style>
""",
unsafe_allow_html=True,
)
# Top bar with blue background and white text
st.markdown("""
<nav class="navbar fixed-top navbar-expand-lg navbar-dark" style="background-color: #3498DB;">
<a class="navbar-brand" style="color: white;text-align: center;">BD-HDR</a>
</nav>
""", unsafe_allow_html=True)
st.sidebar.markdown("""
<nav class="navbar fixed-top navbar-expand-lg navbar-dark" style="background-color: #3498DB;top:2px;padding:7px;width:244px;">
<a class="navbar-brand" style="color: white;text-align: center; ">BD-HDR</a>
</nav>
""", unsafe_allow_html=True)
# Introduction and instructions
st.write("Welcome to our Hand-Written Bengali District Name Recognition System!")
st.write("With this system, you can easily predict the district name from hand-written Bengali (Bangladeshi) text images. It utilizes a powerful Deep Learning model that has been trained on a diverse dataset of hand-written district names to provide accurate predictions.")
# How to Use the System
st.markdown("### How to Use the System:")
st.markdown(
"- Upload an image of a hand-written Bengali district name. Or, draw a Bengali district name on the whiteboard.")
st.markdown(
"- To adjust the drawing stroke width, you can find an option on the left sidebar.")
st.markdown(
"- Click the \"Predict\" button to get the district name prediction.")
st.write("To help you understand the system better, we have provided some example images on the left.You can easily interact with the system by dragging and dropping these example images or by uploading your own hand-written district name image.")
# drawing stroke width
stroke_width = st.sidebar.slider("Stroke Width:", 1, 20, 2)
# Add example images in the sidebar
st.sidebar.markdown(
"<p style='color: red;'>Example Images:</p>", unsafe_allow_html=True)
example_image_paths = [
"figures/word_62_AH_19.jpg",
"figures/word_6_AC_01.jpg",
"figures/word_13_AB_01.jpg",
"figures/word_28_AH_18.jpg"
]
for i, image_path in enumerate(example_image_paths):
image = cv2.imread(image_path)
st.sidebar.image(
image, caption=f"Example {i+1}", use_column_width=False, width=150)
# input field
col1, col2 = st.columns(2)
# From for predict draw word
with col1:
with st.form('Form1', clear_on_submit=False):
# Free drawing board
st.markdown(
"<h3 style='color: #3366cc;'>Draw District Name:</h3>", unsafe_allow_html=True)
# st.title("Canvas")
drawing = st_canvas(
fill_color="rgba(255, 255, 255, 1)",
stroke_width=stroke_width,
stroke_color="black",
background_color="white",
height=100,
width=300,
key="full_app",
)
submitted2 = st.form_submit_button('Predict')
if submitted2 and drawing is not None:
image = np.array(drawing.image_data)
preprocessed_image = preprocess_image(image)
# Make predictions using the loaded model
prediction = model.predict(preprocessed_image)
predicted_label = lb.inverse_transform(prediction)[0]
confidence = prediction.max()
if confidence > 0.9:
col1_1, col1_2 = st.columns(2)
# Display the input image
col1_1.markdown(
"<p style='color: #3366cc;'>Input Image:</p>", unsafe_allow_html=True)
col1_1.image(image, caption="",
use_column_width=True)
# The model predicts
col1_2.markdown(
f"<p style='color: green;'>Predicted District:</p>", unsafe_allow_html=True)
col1_2.markdown(
f"<span style='color: red;font-size: 3rem;'>{predicted_label}</span>", unsafe_allow_html=True)
# Max probability
# st.markdown(f"<p>Confidence Level: {confidence:.2f}</p>", unsafe_allow_html=True)
else:
st.warning('Please Input Appropriate Word', icon="⚠️")
# From for predict input image
with col2:
with st.form('Form2', clear_on_submit=False):
# Image Input field
st.markdown(
"<h3 style='color: #3366cc;'><center>Upload an Image:</center></h3>", unsafe_allow_html=True)
file = st.file_uploader("", type=[
"jpg", "jpeg", "png", "gif", "bmp"], key="fileUploader", help="Accepted formats: JPG, JPEG, PNG, GIF")
submitted1 = st.form_submit_button('Predict')
if submitted1:
if file is not None:
# If the user uploaded an image, read and preprocess it
image = cv2.imdecode(np.fromstring(
file.read(), np.uint8), cv2.IMREAD_COLOR)
preprocessed_image = preprocess_image(image)
# Make predictions using the loaded model
prediction = model.predict(preprocessed_image)
predicted_label = lb.inverse_transform(prediction)[0]
confidence = prediction.max()
if confidence > 0.9:
col2_1, col2_2 = st.columns(2)
# Display the results
col2_1.markdown(
"<p style='color: #3366cc;'>Inout Image:</p>", unsafe_allow_html=True)
col2_1.image(image, caption="",
use_column_width=True)
# The model predicts the character as:
col2_2.markdown(
f"<p style='color: green;'>Predicted District:</p>", unsafe_allow_html=True)
col2_2.markdown(
f"<span style='color: red;font-size: 3rem;'>{predicted_label}</span>", unsafe_allow_html=True)
else:
st.warning('Please Input Appropriate Image', icon="⚠️")
else:
st.warning('Please Input an Image', icon="⚠️")
st.write("We hope you enjoy using our Hand-Written Bengali District Name Recognition System! Feel free to explore the example images, test the model with your own inputs, and experience the efficiency of our Deep Learning model.")
st.write("Please note that this system is intended for educational and demonstration purposes. If you have any questions or feedback, don't hesitate to reach out to us. Happy predicting!")
# Copyright text in bottom middle
st.markdown(
"""
<style>
.footer {
position: fixed;
left: 0;
bottom: 0;
width: 100%;
background-color: #3366cc;
color: white;
text-align: center;
padding: 10px;
}
</style>
""",
unsafe_allow_html=True
)
st.markdown("<div class='footer'>© 2023 Team_AAA. All rights reserved.</div>",
unsafe_allow_html=True)
if __name__ == "__main__":
main()