import streamlit as st
import numpy as np
from PIL import Image
import requests
import ModelClass
from glob import glob
import torch
import torch.nn as nn
import numpy as np

@st.cache_resource
def load_model():
    return ModelClass.get_model()

@st.cache_data
def get_images():
    l = glob('./inputs/*')
    l = {i.split('/')[-1]: i for i in l}
    return l
    

def infer(img):
    image = img.convert('RGB')
    image = ModelClass.get_transform()(image)
    image = image.unsqueeze(dim=0)
    
    model = load_model()
    model.eval()
    with torch.no_grad():
        out = model(image)
        out = nn.Softmax()(out).squeeze()
        return out
        
        
        

st.set_page_config(
    page_title="ActionNet",
    page_icon="🧊",
    layout="centered",
    initial_sidebar_state="expanded",
    menu_items={
        'Get Help': 'https://www.extremelycoolapp.com/help',
        'Report a bug': "https://www.extremelycoolapp.com/bug",
        'About': """
        # This is a header. This is an *extremely* cool app!
        How how are you doin.
        
        ---
        I am fine
        
        
        <style>
        </style>
        """
    }
    )


# 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;
            }
        .css-1y4p8pa {
            padding: 3rem 1rem 10rem;
            max-width: 58rem;
        }
    </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)



def predict(image):
    # Dummy prediction
    classes = ['cat', 'dog']
    prediction = np.random.rand(len(classes))
    prediction /= np.sum(prediction)
    return dict(zip(classes, prediction))

def app():
    
    st.title('ActionNet')
    # 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('Human Action Recognition using CNN: A Conputer Vision project that trains a ResNet model to classify human activities. The dataset contains 15 activity classes, and the model predicts the activity from input images.')
    
    
    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
    
    test_images = get_images()
    test_image = st.selectbox('Or choose a test image', list(test_images.keys()))
    
    
    st.markdown('#### Selected Image')
    
    left_column, right_column = st.columns([1.5, 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(image_url)
            st.image(image, use_column_width=True)
            
        
        if st.button('✨ Get prediction from AI', type='primary'):
            spacer = st.empty()
            
            res = infer(image)
            prob = res.numpy()
            idx = np.argpartition(prob, -6)[-6:]
            right_column.markdown('#### Results')
            
            idx = list(idx)
            idx.sort(key=lambda x: prob[x].astype(float), reverse=True)
            for i in idx:
                
                class_name = ModelClass.get_class(i).replace('_', ' ').capitalize()
                class_probability = prob[i].astype(float)
                right_column.write(f'{class_name}: {class_probability:.2%}')
                right_column.progress(class_probability)
            
    
    
    st.markdown("---")
    st.markdown("Built by [Shamim Ahamed](https://www.shamimahamed.com/). Data provided by [aiplanet](https://aiplanet.com/challenges/data-sprint-76-human-activity-recognition/233/overview/about)")

    
app()