import streamlit as st
import pandas as pd
import plotly.express as px
from huggingface_hub import HfApi
import io
from datetime import datetime, timedelta
import time

# Set page config for a wider layout and custom theme
st.set_page_config(layout="wide", page_title="Job Listings Dashboard")

# Custom CSS for black background and styling
st.markdown("""
<style>
    .stApp {
        background-color: #000000;
        color: #FFFFFF;
    }
    .stButton>button {
        background-color: #4e79a7;
        color: white;
    }
    .stSelectbox, .stMultiSelect {
        color: #FFFFFF;
    }
    .stDataFrame {
        background-color: #1E1E1E;
    }
    .plotly-graph-div {
        background-color: #1E1E1E;
    }
    .big-font {
        font-size: 48px;
        font-weight: bold;
        text-align: center;
    }
</style>
""", unsafe_allow_html=True)

# Hugging Face setup
HF_TOKEN = st.secrets["HF_TOKEN"]
HF_USERNAME = st.secrets["HF_USERNAME"]
DATASET_NAME = "jobeasz"

@st.cache_data(ttl=3600)
def load_and_concat_data():
    api = HfApi()
    dataset_files = api.list_repo_files(repo_id=f"{HF_USERNAME}/{DATASET_NAME}", repo_type="dataset")
    csv_files = [file for file in dataset_files if file.endswith('.csv')]

    all_data = []
    for file in csv_files:
        try:
            file_content = api.hf_hub_download(repo_id=f"{HF_USERNAME}/{DATASET_NAME}", filename=file, repo_type="dataset", token=HF_TOKEN)
            df = pd.read_csv(file_content)
            all_data.append(df)
        except Exception:
            pass  # Silently skip files that can't be processed

    if not all_data:
        return pd.DataFrame()

    concatenated_df = pd.concat(all_data, ignore_index=True)
    
    columns_to_keep = [
        'site', 'job_url', 'title', 'company', 'location',
        'job_type', 'date_posted', 'is_remote', 'company_url'
    ]
    filtered_df = concatenated_df[columns_to_keep].reset_index(drop=True)
    filtered_df['date_posted'] = pd.to_datetime(filtered_df['date_posted'], errors='coerce')
    
    # Drop duplicates and rows with NaT in date_posted
    filtered_df = filtered_df.drop_duplicates().dropna(subset=['date_posted'])
    
    return filtered_df

@st.cache_data()
def get_unique_values(df):
    return {
        'companies': df['company'].unique(),
        'locations': df['location'].unique(),
        'job_types': df['job_type'].unique()
    }

def create_chart(data, _x, y, title, color_sequence):
    fig = px.bar(data, x=_x, y=y, title=title, color_discrete_sequence=color_sequence)
    fig.update_layout(plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)', font_color='#FFFFFF')
    return fig

def create_time_series(df):
    df_by_date = df.groupby('date_posted').size().reset_index(name='count')
    fig = px.line(df_by_date, x='date_posted', y='count', title="Job Postings Over Time", color_discrete_sequence=['#4e79a7'])
    fig.update_layout(plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)', font_color='#FFFFFF')
    return fig

def display_dashboard(df):
    col1, col2 = st.columns(2)

    with col1:
        st.subheader("Job Postings Overview")
        st.metric("Total Job Postings", len(df))
        st.metric("Unique Companies", df['company'].nunique())
        st.metric("Unique Locations", df['location'].nunique())

        min_date = df['date_posted'].min().date()
        max_date = df['date_posted'].max().date()
        st.write(f"Job postings from {min_date} to {max_date}")

    with col2:
        top_companies = df['company'].value_counts().head(10)
        fig = create_chart(top_companies, top_companies.index, top_companies.values, "Top 10 Companies", ['#4e79a7'])
        st.plotly_chart(fig, use_container_width=True)

    # Job Postings Over Time Chart
    fig_time_series = create_time_series(df)
    st.plotly_chart(fig_time_series, use_container_width=True)

    col3, col4 = st.columns(2)

    with col3:
        top_locations = df['location'].value_counts().head(10)
        fig = create_chart(top_locations, top_locations.index, top_locations.values, "Top 10 Locations", ['#f28e2b'])
        st.plotly_chart(fig, use_container_width=True)

    with col4:
        job_types = df['job_type'].value_counts()
        fig = px.pie(names=job_types.index, values=job_types.values, title="Job Types Distribution", color_discrete_sequence=px.colors.qualitative.Pastel)
        fig.update_layout(plot_bgcolor='rgba(0,0,0,0)', paper_bgcolor='rgba(0,0,0,0)', font_color='#FFFFFF')
        st.plotly_chart(fig, use_container_width=True)

@st.cache_data
def filter_dataframe(df, companies, locations, job_types):
    filtered_df = df
    if companies:
        filtered_df = filtered_df[filtered_df['company'].isin(companies)]
    if locations:
        filtered_df = filtered_df[filtered_df['location'].isin(locations)]
    if job_types:
        filtered_df = filtered_df[filtered_df['job_type'].isin(job_types)]
    return filtered_df

def display_data_explorer(df):
    st.subheader("Data Explorer")

    show_all = st.radio("Display", ("All Data", "Filtered Data"))

    if show_all == "Filtered Data":
        unique_values = get_unique_values(df)
        col1, col2, col3 = st.columns(3)
        with col1:
            companies = st.multiselect("Select Companies", options=unique_values['companies'])
        with col2:
            locations = st.multiselect("Select Locations", options=unique_values['locations'])
        with col3:
            job_types = st.multiselect("Select Job Types", options=unique_values['job_types'])
        
        filtered_df = filter_dataframe(df, companies, locations, job_types)
    else:
        filtered_df = df

    st.write(f"Showing {len(filtered_df)} job listings")
    
    def make_clickable(url):
        return f'<a href="{url}" target="_blank" style="color: #4e79a7;">Link</a>'

    filtered_df['job_url'] = filtered_df['job_url'].apply(make_clickable)
    filtered_df['company_url'] = filtered_df['company_url'].apply(make_clickable)

    st.write(filtered_df.to_html(escape=False, index=False), unsafe_allow_html=True)

def main():
    st.title("Job Listings Dashboard")

    df = load_and_concat_data()

    if df.empty:
        st.error("No data available. Please check your dataset.")
        return

    # Sidebar for navigation
    st.sidebar.title("Navigation")
    page = st.sidebar.radio("Go to", ["Dashboard", "Data Explorer"])

    if page == "Dashboard":
        display_dashboard(df)
    elif page "Data Explorer":
        display_data_explorer(df)

if __name__ == "__main__":
    main()