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import streamlit as st
from streamlit_drawable_canvas import st_canvas
import cv2
from tensorflow.keras.models import load_model
import numpy as np
from PIL import Image
#!pip install Pillow==9.0.0
import io
import streamlit as st
ms = st.session_state
if "themes" not in ms:
ms.themes = {"current_theme": "light",
"refreshed": True,
"light": {"theme.base": "dark",
"theme.backgroundColor": "black",
"theme.primaryColor": "#c98bdb",
"theme.secondaryBackgroundColor": "#5591f5",
"theme.textColor": "white",
"theme.textColor": "white",
"button_face": "🌜"},
"dark": {"theme.base": "light",
"theme.backgroundColor": "white",
"theme.primaryColor": "#5591f5",
"theme.secondaryBackgroundColor": "#82E1D7",
"theme.textColor": "#0a1464",
"button_face": "🌞"},
}
def ChangeTheme():
previous_theme = ms.themes["current_theme"]
tdict = ms.themes["light"] if ms.themes["current_theme"] == "light" else ms.themes["dark"]
for vkey, vval in tdict.items():
if vkey.startswith("theme"): st._config.set_option(vkey, vval)
ms.themes["refreshed"] = False
if previous_theme == "dark": ms.themes["current_theme"] = "light"
elif previous_theme == "light": ms.themes["current_theme"] = "dark"
btn_face = ms.themes["light"]["button_face"] if ms.themes["current_theme"] == "light" else ms.themes["dark"]["button_face"]
st.button(btn_face, on_click=ChangeTheme)
if ms.themes["refreshed"] == False:
ms.themes["refreshed"] = True
st.rerun()
# Load the model. Ensure you have the 'compatible_mm.h5' model file in the current directory.
model = load_model('mm.h5')
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"
]
st.title('Burmese Character and Digit Recognizer')
st.markdown('''
Try to write something!
''')
SIZE = 200 # Setting the size for drawing and for model input
# Uploading a file
uploaded_file = st.file_uploader("Upload your file here...")
# Initialize rescaled to None
rescaled = None
# Handle file upload
if uploaded_file is not None:
# Read the uploaded image file
bytes_data = uploaded_file.getvalue()
image = Image.open(io.BytesIO(bytes_data))
# Convert the image to RGB (in case it's not)
image = image.convert('RGB')
# Resize the image to match the model's expected input
image = image.resize((SIZE, SIZE), Image.Resampling.LANCZOS)
# Convert the image to a numpy array and expand dimensions
rescaled = np.expand_dims(np.array(image), axis=0)
# Handle canvas drawing
else:
# Setting up the canvas
canvas_result = st_canvas(
fill_color="rgba(255, 165, 0, 0.3)", # Fixed fill color with some opacity
stroke_width=10,
stroke_color='#FFFFFF',
background_color="#000000",
background_image=None,
update_streamlit=True,
height=SIZE,
width=SIZE,
drawing_mode="freedraw",
key="canvas",
)
# If the user draws on the canvas
if canvas_result.image_data is not None:
# Convert the canvas drawing to an image
canvas_image = Image.fromarray((canvas_result.image_data).astype('uint8'), mode='RGBA')
canvas_image = canvas_image.convert('RGB') # Convert to RGB
canvas_image = canvas_image.resize((SIZE, SIZE), Image.Resampling.LANCZOS) # Resize to model's expected input size
# Convert to numpy and adjust dimensions
rescaled = np.expand_dims(np.array(canvas_image), axis=0)
if st.button('Predict') and rescaled is not None:
# Predict the class of the input image
val = model.predict(rescaled)
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])
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