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("[![View in W&B](https://img.shields.io/badge/View%20in-W%26B-blue)](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)")