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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]) | |