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| import streamlit as st | |
| import numpy as np | |
| from PIL import Image | |
| import requests | |
| # fix sidebar | |
| st.markdown(""" | |
| <style> | |
| .css-vk3wp9 { | |
| background-color: rgb(255 255 255); | |
| } | |
| .css-18l0hbk { | |
| padding: 0.34rem 1.2rem !important; | |
| margin: 0.125rem 2rem; | |
| } | |
| .css-nziaof { | |
| padding: 0.34rem 1.2rem !important; | |
| margin: 0.125rem 2rem; | |
| background-color: rgb(181 197 227 / 18%) !important; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True | |
| ) | |
| hide_st_style = """ | |
| <style> | |
| #MainMenu {visibility: hidden;} | |
| footer {visibility: hidden;} | |
| header {visibility: hidden;} | |
| </style> | |
| """ | |
| st.markdown(hide_st_style, unsafe_allow_html=True) | |
| # Function to load and predict image | |
| def predict(image): | |
| # Dummy prediction | |
| classes = ['cat', 'dog'] | |
| prediction = np.random.rand(len(classes)) | |
| prediction /= np.sum(prediction) | |
| return dict(zip(classes, prediction)) | |
| # Define app layout | |
| #st.set_page_config(page_title='Image Classification App', page_icon=':camera:', layout='wide') | |
| st.title('HappyWhale') | |
| st.markdown("[](https://wandb.ai/<username>/<project_name>?workspace=user-<username>)") | |
| st.markdown('This project aims to identify whales and dolphins by their unique characteristics. It can help researchers understand their behavior, population dynamics, and migration patterns. This project can aid researchers in identifying these marine mammals, providing valuable data for conservation efforts. [[Source Code]](https://kaggle.com/)') | |
| # Add file uploader | |
| uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) | |
| # Add test image selector | |
| test_images = { | |
| 'Cat': 'https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg', | |
| 'Dog': 'https://upload.wikimedia.org/wikipedia/commons/thumb/6/6e/Golde33443.jpg/1200px-Golde33443.jpg', | |
| 'Bird': 'https://upload.wikimedia.org/wikipedia/commons/thumb/0/0a/Scarlet_Tanager_-_male_%28cropped%29.jpg/1200px-Scarlet_Tanager_-_male_%28cropped%29.jpg' | |
| } | |
| test_image = st.selectbox('Or choose a test image', list(test_images.keys())) | |
| st.subheader('Selected Image') | |
| # Define layout of app | |
| left_column, right_column = st.columns([1, 2.5], gap="medium") | |
| with left_column: | |
| if uploaded_file is not None: | |
| image = Image.open(uploaded_file) | |
| st.image(image, use_column_width=True) | |
| else: | |
| image_url = test_images[test_image] | |
| image = Image.open(requests.get(image_url, stream=True).raw) | |
| st.image(image, use_column_width=True) | |
| if st.button('✨ Get prediction from AI', type='primary'): | |
| spacer = st.empty() | |
| prediction = predict(image) | |
| right_column.subheader('Results') | |
| for class_name, class_probability in prediction.items(): | |
| right_column.write(f'{class_name}: {class_probability:.2%}') | |
| right_column.progress(class_probability) | |
| # Display a footer with links and credits | |
| st.markdown("---") | |
| st.markdown("Built by [Shamim Ahamed](https://your-portfolio-website.com/). Data provided by [Kaggle](https://www.kaggle.com/c/)") | |
| #st.markdown("Data provided by [The Feedback Prize - ELLIPSE Corpus Scoring Challenge on Kaggle](https://www.kaggle.com/c/feedbackprize-ellipse-corpus-scoring-challenge)") | |