Hunter-X-Hunter-Anime-Classification
/
pages
/06-🤖 HxH Character Anime Classification with Deep Learning.py
| import numpy as np | |
| import streamlit as st | |
| from PIL import Image | |
| from models.deep_learning import DeepLearningGradCAM, DeepLearningModel | |
| from utils import configs | |
| from utils.functional import ( | |
| generate_empty_space, | |
| get_default_images, | |
| get_most_salient_object, | |
| set_page_config, | |
| set_seed, | |
| ) | |
| # Set seed | |
| set_seed() | |
| # Set page config | |
| set_page_config("HxH Character Anime Classification with Deep Learning", "🤖") | |
| # Sidebar | |
| name_model = st.sidebar.selectbox("Select Model", tuple(configs.NAME_MODELS.keys())) | |
| support_set_method = st.sidebar.selectbox( | |
| "Select Support Set Method", configs.SUPPORT_SET_METHODS | |
| ) | |
| freeze_model = st.sidebar.checkbox("Freeze Model", value=True) | |
| pretrained_model = st.sidebar.checkbox("Pretrained Model", value=True) | |
| # Load Model | |
| def load_model( | |
| name_model: str, support_set_method: str, freeze_model: bool, pretrained_model: bool | |
| ): | |
| deep_learning_model = DeepLearningModel( | |
| name_model, freeze_model, pretrained_model, support_set_method | |
| ) | |
| custom_grad_cam = DeepLearningGradCAM( | |
| name_model, freeze_model, pretrained_model, support_set_method | |
| ) | |
| return deep_learning_model, custom_grad_cam | |
| deep_learning_model, custom_grad_cam = load_model( | |
| name_model, support_set_method, freeze_model, pretrained_model | |
| ) | |
| # Application Description | |
| st.markdown("# ❓ Application Description") | |
| st.write( | |
| f""" | |
| Welcome to our HxH Character Anime Classification with Deep Learning application! 🤖 | |
| This app is designed to help you classify your favorite HxH anime characters with the power of deep learning. Our state-of-the-art model allows for accurate and efficient identification of HxH characters from your favorite scenes. With an easy-to-use interface, even those with limited knowledge of deep learning can classify characters with ease. | |
| Gone are the days of struggling to identify characters by memory or manual inspection. Our app does the hard work for you, freeing up your time to enjoy the show. Whether you're a hardcore HxH fan or just discovering the series, our app will enhance your viewing experience and make it more enjoyable. | |
| Don't hesitate, give our HxH Character Anime Classification with Deep Learning app a try today and let us know what you think! 🦸 | |
| DISCLAIMER: The output of this app only {", ".join(configs.CLASS_CHARACTERS)} | |
| """ | |
| ) | |
| uploaded_file = st.file_uploader( | |
| "Upload image file", type=["jpg", "jpeg", "png", "bmp", "tiff"] | |
| ) | |
| select_default_images = st.selectbox("Select default images", get_default_images()) | |
| st.caption("Default Images will be used if no image is uploaded.") | |
| select_image_button = st.button("Select Image") | |
| if select_image_button: | |
| st.success("Image selected") | |
| if select_image_button and uploaded_file is not None: | |
| image = np.array(Image.open(uploaded_file).convert("RGB")) | |
| st.session_state["image"] = image | |
| elif select_image_button and uploaded_file is None: | |
| image = np.array(Image.open(select_default_images).convert("RGB")) | |
| st.session_state["image"] = image | |
| if st.session_state.get("image") is not None: | |
| image = st.session_state.get("image") | |
| col1, col2, col3 = st.columns(3) | |
| col2.write("## 📸 Preview Image") | |
| col2.image(image, use_column_width=True) | |
| predict_image_button = col2.button("Classify Image Character") | |
| generate_empty_space(2) | |
| if predict_image_button: | |
| with st.spinner("Classifying Image Character..."): | |
| result_class = deep_learning_model.predict(image) | |
| if result_class["character"] == configs.CLASS_CHARACTERS[-1]: | |
| result_grad_cam = custom_grad_cam.get_grad_cam(image) | |
| else: | |
| result_grad_cam = custom_grad_cam.get_grad_cam_with_output_target( | |
| image, configs.CLASS_CHARACTERS.index(result_class["character"]) | |
| ) | |
| inference_time = result_class["inference_time"] | |
| col1, col2, col3 = st.columns(3) | |
| col1.write("### 🙂 Source Image") | |
| col1.image(image, use_column_width=True) | |
| col2.write("### 😎 Grad CAM Image") | |
| col2.image(result_grad_cam, use_column_width=True) | |
| col3.write("### 🤔 Most Salient Object") | |
| col3.image(get_most_salient_object(image), use_column_width=True) | |
| st.write("### 📝 Result") | |
| st.write(f"Predicted Character: {result_class['character'].title()}") | |
| st.write(f"Confidence Score: {result_class['confidence'] * 100:.2f}%") | |
| st.write(f"Inference Time: {inference_time:.2f} s") | |
| st.session_state["image"] = None | |