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import streamlit as st
from streamlit_drawable_canvas import st_canvas
import cv2
import numpy as np
from tensorflow.keras.models import load_model
from PIL import Image
import easyocr
import pandas as pd
# Load the model for Myanmar character recognition
model = load_model('mm.h5')
# Initialize EasyOCR reader for English
reader = easyocr.Reader(['en'], gpu=False)
class_lists = [
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"Ah",
"Aha",
"au2",
"au3",
"ay2",
"ba_htoat_chite",
"ba_kone",
"da_htway",
"da_out_chite",
"da_yay_hmote",
"da_yin_kout",
"e1",
"e2",
"eeare",
"ga_khi",
"ga_nge",
"ha",
"hsa_lain",
"hta_hsin_htu",
"hta_wun_beare",
"ka_kji",
"kha_khway",
"la",
"la_kji",
"ma",
"na_kji",
"na_ngear",
"nga",
"nga_kyi",
"O",
"pa_sout",
"pfa_u_htoat",
"sah_lone",
"ta_thun_lyin_chate",
"ta_wun_pu",
"tha",
"u1",
"u2",
"un",
"wa",
"yah_kout",
"yah_pet_let",
"za_kwear",
"za_myin_hsware"
]
# Streamlit UI
st.title('Text and Character Recognizer')
st.markdown('''
Select the mode for recognition:
''')
# Choose mode
mode = st.radio("Mode", ('English Text Recognition', 'Myanmar Character Recognition'))
if mode == 'English Text Recognition':
uploaded_file = st.file_uploader("Upload your file here...", key="uploader_english")
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image', use_column_width=True)
# EasyOCR to recognize text
result = reader.readtext(np.array(image))
for detection in result:
st.write(f'Detected text: {detection[1]}, Confidence: {detection[2]}')
elif mode == 'Myanmar Character Recognition':
col1, col2 = st.columns(2)
with col1:
uploaded_file = st.file_uploader("Upload your file here...", key="uploader_myanmar")
with col2:
# Initialize canvas
canvas_result = st_canvas(
fill_color="rgba(255, 165, 0, 0.3)",
stroke_width=3,
stroke_color="#ffffff",
background_color="#000000",
update_streamlit=True,
width=200,
height=200,
drawing_mode="freedraw",
key="canvas",
)
# Process the image for prediction
image_data = None
if uploaded_file is not None:
image_data = Image.open(uploaded_file).convert('RGB')
elif canvas_result.image_data is not None:
image_data = Image.fromarray(np.uint8(canvas_result.image_data)).convert('RGB')
if image_data is not None:
# Convert PIL image to OpenCV format
image_cv = np.array(image_data)
image_cv = cv2.cvtColor(image_cv, cv2.COLOR_RGB2BGR)
resized_image = cv2.resize(image_cv, (200, 200))
# Prepare image for model input
model_input = resized_image[np.newaxis, :, :, :3]
st.write('Model Input')
st.image(model_input, width=200) # Display the input image to model
if st.button('Predict Myanmar Character'):
# Predict the class
val = model.predict(model_input)
predicted_class_index = np.argmax(val)
mm_text = class_lists[predicted_class_index]
st.write(f'Result: {mm_text}, Index: {predicted_class_index}')
st.bar_chart(val[0])
else:
if mode == 'Myanmar Character Recognition':
st.write("Please upload an image or draw in the canvas above.")
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