import streamlit as st
import hopsworks
import pandas as pd
import os
import time
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta

# Constants
DATA_DIR = "data"
TIMESTAMP_FILE = "last_download_time.txt"

# Initialize Hopsworks connection
def connect_to_hopsworks():
    st.write("Connecting to Hopsworks...")
    project_name = "id2223AirQuality"
    api_key = os.getenv("HOPSWORKS_API_KEY")
    conn = hopsworks.connection(api_key_value=api_key)
    project = conn.get_project(project_name)
    return project

# Fetch data from Hopsworks feature group
def fetch_data_from_feature_group(project, feature_group_name, version):
    feature_store = project.get_feature_store()
    feature_group = feature_store.get_feature_group(name=feature_group_name, version=version)
    data = feature_group.read()
    return data

# Save data locally
def save_data_locally(data, filename):
    os.makedirs(DATA_DIR, exist_ok=True)
    filepath = os.path.join(DATA_DIR, filename)
    data.to_csv(filepath, index=False)
    
    # Save timestamp
    timestamp_path = os.path.join(DATA_DIR, TIMESTAMP_FILE)
    with open(timestamp_path, "w") as f:
        f.write(str(datetime.now()))
    return filepath

# Load local data
def load_local_data(filename):
    filepath = os.path.join(DATA_DIR, filename)
    if os.path.exists(filepath):
        return pd.read_csv(filepath)
    else:
        return None

# Check if local data is valid
def is_local_data_valid():
    timestamp_path = os.path.join(DATA_DIR, TIMESTAMP_FILE)
    if not os.path.exists(timestamp_path):
        return False
    try:
        with open(timestamp_path, "r") as f:
            last_download_time = datetime.fromisoformat(f.read().strip())
        # Check if the data is more than a day old
        if datetime.now() - last_download_time > timedelta(days=1):
            return False
        return True
    except Exception as e:
        st.warning(f"Error reading timestamp: {e}")
        return False

# Plot graphs
def plot_graphs(data):
    st.write("### Data Preview")
    st.dataframe(data.head())

    #st.write("### Histogram")
    #column = st.selectbox("Select column for histogram", data.columns)
    #fig, ax = plt.subplots()
    #sns.histplot(data[column], kde=True, ax=ax)
    #st.pyplot(fig)

    #st.write("### Correlation Matrix")
    #fig, ax = plt.subplots()
    #sns.heatmap(data.corr(), annot=True, cmap="coolwarm", ax=ax)
    #st.pyplot(fig)

# Streamlit UI
def main():
    st.title("Hopsworks Feature Group Explorer")
    
    # Initialize session state
    if "hopsworks_project" not in st.session_state:
        st.session_state.hopsworks_project = None
    if "data" not in st.session_state:
        st.session_state.data = None

    # User inputs for feature group and version
    """st.sidebar.title("Data Settings")
    feature_group_name = st.sidebar.text_input("Feature Group Name", value="predictions")
    version = st.sidebar.number_input("Feature Group Version", value=1, min_value=1)
    filename = st.sidebar.text_input("Local Filename", value="data.csv")
    """
    # Check for valid local data
    if is_local_data_valid():
        st.write("Using cached local data.")
        st.session_state.data = load_local_data(filename)
    else:
        # Fetch data if local data is invalid
        if st.session_state.hopsworks_project is None:
            st.write("Initializing Hopsworks connection...")
            st.session_state.hopsworks_project = connect_to_hopsworks()
            st.success("Connected to Hopsworks!")
        
        project = st.session_state.hopsworks_project
        data = fetch_data_from_feature_group(project, "predictions", 1)
        print(data.head())
        filepath = save_data_locally(data, "./data")
        st.session_state.data = data
        st.success(f"Data fetched and saved locally at {filepath}")

    # Display data and graphs
    if st.session_state.data is not None:
        plot_graphs(st.session_state.data)

main()

# Visa alla busslinjer? Söka?
    # Hur se riktning?
# Filtrera på busslinje och riktning
# Filtrera på tid 
    # Ska användaren ange tid
# Se alla unika trip ids
# Mappa position till stop
# Visa någon sorts graf för alla bussar inom den tiden
    # Ska det vara för alla stopp eller bara de som användaren angivit att den ska åka