import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
import json

from isolation_forest import apply_isolation_forest
from lstm import apply_lstm
from oc_svm import apply_oc_svm

# Title for Streamlit app
st.title('Cattle logfile analysis')

col1, col2 = st.columns(2)

# Content from upload
if 'json_content' not in st.session_state:
    st.session_state.json_content = None
    st.session_state.json_content = None
    st.session_state.json_content = None


# Select dimensions
num_dimensions = st.selectbox('Select number of dimensions:', [1, 2, 3], 
                              index=2)

# Select Algorithm
options = ["Isolation Forest", "One-Class Support Vector Machine", "Long Short-Term Memory", "None"]
algorithm_box = st.selectbox('Select algorithm:', 
                             options, 
                             index=0)

with col1:
    with st.container(border=True):
        uploaded_file = st.file_uploader("Upload JSON", type="json")
        if uploaded_file:
            st.session_state.json_content = json.loads(uploaded_file.getvalue())

# Content from local file
with col2:
    with st.container(border=True):
        st.write('Load embedded JSON')
        if st.button('Load'):
            with open('cattle_log.json', 'r') as file:
                st.session_state.json_content = json.load(file)


if st.session_state.json_content:
    X = []

    # Iterate over each log entry in the log_content
    for log_entry in st.session_state.json_content['logs']:
        # Extract and convert the necessary attributes
        total_today_str = log_entry['distanceTraveled']['totalToday'].rstrip('m')
        heart_rate = int(log_entry['healthData']['heartRate'])  # Assuming heart rate is always an integer
        weight_str = log_entry['healthData']['weight'].rstrip('kg')
        
        # Convert the distance and weight to floating-point values
        total_today = float(total_today_str)  # Convert distance to float
        weight = float(weight_str)  # Convert weight to float
        
        # Create a 3D vector for the current log entry and append it to the list of vectors
        vector_3d = [total_today, heart_rate, weight]
        X.append(vector_3d)

    # Convert X into a NumPy array for easier slicing
    X = np.array(X)

    # Generating synthetic data
    rng = np.random.RandomState(42)

    selected_algorithm_index = options.index(algorithm_box)
    if selected_algorithm_index == 0:
        plotted_result = apply_isolation_forest(rng, 
                                                X)
    elif selected_algorithm_index == 1:
        plotted_result = apply_oc_svm(X)
    elif selected_algorithm_index == 2:
        plotted_result = apply_lstm(X)
    else:
        plotted_result = array = [-1] * 50

    # Create a figure
    fig, ax = plt.subplots(figsize=(10, 7), subplot_kw={'projection': '3d'} if num_dimensions == 3 else {})
    
    # Configure the plot based on the number of dimensions
    if num_dimensions == 3:
        ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=['red' if pred == -1 else 'blue' for pred in plotted_result], s=50)
        ax.set_xlabel("Distance travelled")
        ax.set_ylabel("Heartrate")
        ax.set_zlabel("Weight")
    else:
        x_axis = X[:, 0]
        y_axis = np.zeros_like(X[:, 0]) if num_dimensions == 1 else X[:, 1]

        ax.scatter(x_axis, y_axis, c=['red' if pred == -1 else 'blue' for pred in plotted_result], s=50)
        ax.set_xlabel("Distance travelled")
        ax.set_ylabel("Heartrate" if num_dimensions > 1 else "")
        
    # Set common properties and show plot
    ax.set_title(algorithm_box)
    ax.grid(True)
    st.pyplot(fig)