import streamlit as st
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
from wordcloud import WordCloud

# Set page title
st.set_page_config(page_title='Layoffs Data Visualization')

# Load the CSV file
@st.cache_data  # Caching the data for improved performance
def load_data(file_path):
    data = pd.read_csv(file_path)
    return data

# Main function
def main():
    # Page title and description
    st.title("Layoffs Data Visualization")
    st.write("Explore and visualize layoffs data")

    # Load the data
    data = load_data("Data/layoffs_data.csv")

    # Sidebar options
    st.sidebar.title("Options")
    selected_option = st.sidebar.selectbox("Select an option", ("Data Preview", "Unique Industries",
                                                                "Layoffs Count by Company",
                                                                "Layoffs Count by Country",
                                                                "Bar plot of layoffs by industry",
                                                                "Line plot of layoffs over time",
                                                                "Word Cloud of Industries",
                                                                "Histogram of Laid Off Counts",
                                                                "Scatter Plot",
                                                                "List of Companies and Laid Off Employees"))

    # Data exploration
    if selected_option == "Data Preview":
        st.subheader("Data Preview")
        num_rows = st.number_input("Number of rows to display", min_value=1, value=len(data))
        st.dataframe(data.head(num_rows))

    elif selected_option == "Unique Industries":
        st.subheader("Unique Industries")
        unique_industries = data['Industry'].unique()
        st.write(unique_industries)

    elif selected_option == "Layoffs Count by Company":
        st.subheader("Layoffs Count by Company")
        company_counts = data['Company'].value_counts()
        st.write(company_counts)

    elif selected_option == "Layoffs Count by Country":
        st.subheader("Layoffs Count by Country")
        country_counts = data['Country'].value_counts()
        st.write(country_counts)

    # Data visualization
    elif selected_option == "Bar plot of layoffs by industry":
        st.subheader("Bar plot of layoffs by industry")
        industry_counts = data['Industry'].value_counts()
        fig, ax = plt.subplots(figsize=(10, 6))
        sns.barplot(x=industry_counts.index, y=industry_counts.values, ax=ax)
        ax.set_xticklabels(industry_counts.index, rotation=90)
        st.pyplot(fig)

    elif selected_option == "Line plot of layoffs over time":
        st.subheader("Line plot of layoffs over time")
        data['Date'] = pd.to_datetime(data['Date'])
        layoffs_over_time = data.groupby('Date').sum()['Laid_Off_Count']
        fig, ax = plt.subplots(figsize=(10, 6))
        sns.lineplot(x=layoffs_over_time.index, y=layoffs_over_time.values, ax=ax)
        ax.set_xlabel("Date")
        ax.set_ylabel("Layoffs Count")
        st.pyplot(fig)

    elif selected_option == "Word Cloud of Industries":
        st.subheader("Word Cloud of Industries")
        industry_text = ' '.join(data['Industry'].dropna().values.tolist())
        wordcloud = WordCloud(width=800, height=400).generate(industry_text)
        plt.figure(figsize=(10, 6))
        plt.imshow(wordcloud, interpolation='bilinear')
        plt.axis("off")
        st.pyplot(plt)

    elif selected_option == "Histogram of Laid Off Counts":
        st.subheader("Histogram of Laid Off Counts")
        fig, ax = plt.subplots(figsize=(10, 6))
        sns.histplot(data['Laid_Off_Count'], kde=True, ax=ax)
        st.pyplot(fig)

    elif selected_option == "Scatter Plot":
        st.subheader("Scatter Plot")
        fig = px.scatter(data, x='Funds_Raised', y='Laid_Off_Count', color='Stage')
        st.plotly_chart(fig)

    elif selected_option == "List of Companies and Laid Off Employees":
        st.subheader("List of Companies and Laid Off Employees")
        company_employees = data.groupby('Company').sum()['Laid_Off_Count'].reset_index()
        st.dataframe(company_employees)

if __name__ == '__main__':
    main()