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| import streamlit as st | |
| import numpy as np | |
| from PIL import Image | |
| from tensorflow.keras.models import load_model | |
| from streamlit_drawable_canvas import st_canvas | |
| 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() | |
| # Function to preprocess the image | |
| def preprocess_image(image, target_size): | |
| if image.mode != "RGB": | |
| image = image.convert("RGB") | |
| image = image.resize(target_size) | |
| image = np.expand_dims(image, axis=0) | |
| return image | |
| # Function to predict the digit | |
| def predict_digit(model, image): | |
| processed_image = preprocess_image(image, (200, 200)) # Match your model's input size | |
| prediction = model.predict(processed_image) | |
| return np.argmax(prediction), np.max(prediction) | |
| # Load your trained model | |
| model = load_model("last_burmese_Digit_recognizer_model.h5") | |
| # Streamlit app | |
| st.title("Burmese Digit Recognizer") | |
| # Upload image file or draw | |
| st.markdown("## Upload an Image or Draw") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| file = st.file_uploader("Upload Here", type=['png', 'jpg', 'jpeg']) | |
| with col2: | |
| # Drawable canvas | |
| canvas_result = st_canvas( | |
| fill_color="rgba(255, 165, 0, 0.3)", # Drawing parameters | |
| stroke_width=3, | |
| stroke_color="#ffffff", | |
| background_color="#000000", | |
| background_image=None if file else st.session_state.get("background", None), | |
| update_streamlit=True, | |
| width=400, | |
| height=400, | |
| drawing_mode="freedraw", | |
| key="canvas", | |
| ) | |
| image = None # Initialize image variable | |
| # Process uploaded image or drawing | |
| if file is not None: | |
| image = Image.open(file) # Read image with PIL | |
| elif canvas_result.image_data is not None: | |
| image = Image.fromarray(np.array(canvas_result.image_data, dtype=np.uint8)).convert('RGB') | |
| if image is not None: | |
| st.image(image, caption='Uploaded Image') # Display the uploaded/drawn image | |
| # Predict the digit | |
| digit, confidence = predict_digit(model, image) | |
| st.write(f"Predicted Digit: {digit} with confidence {confidence:.2f}") | |
| else: | |
| st.write("Please upload an image or use the canvas to draw.") | |