import requests  # For making HTTP requests
import pandas as pd
import streamlit as st
from io import BytesIO
from bs4 import BeautifulSoup  # Add this import for BeautifulSoup
from fpdf import FPDF  # For generating PDFs

# Function to fetch data
@st.cache_data(ttl=3600)
def fetch_data():
    # URL of the website to scrape
    url = "https://www.ireland.ie/en/india/newdelhi/services/visas/processing-times-and-decisions/"
    headers = {
        "User-Agent": (
            "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
            "(KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36"
        )
    }

    # Fetch the webpage
    response = requests.get(url, headers=headers)
    if response.status_code != 200:
        st.error("Failed to fetch the webpage. Please try again later.")
        return None, None

    # Parse the HTML to find the .ods link
    soup = BeautifulSoup(response.content, "html.parser")
    file_url = None
    file_name = None
    for link in soup.find_all("a"):
        if "Visa decisions made from 1 January 2025" in link.get_text():
            file_url = link.get("href")
            file_name = link.get_text().strip()  # Extract filename from link text
            if not file_url.startswith("http"):
                file_url = requests.compat.urljoin(url, file_url)
            break

    if not file_url or not file_name:
        st.error("Could not find the visa decisions file link on the website.")
        return None, None

    # Fetch the .ods file
    ods_response = requests.get(file_url, headers=headers)
    if ods_response.status_code != 200:
        st.error("Failed to download the visa decisions file.")
        return None, None

    # Process the .ods file
    ods_file = BytesIO(ods_response.content)
    df = pd.read_excel(ods_file, engine="odf")

    # Drop unnecessary columns
    df.dropna(how="all", inplace=True)  # Drop rows with all NaN values
    df.reset_index(drop=True, inplace=True)

    # Keep only the first two columns
    if len(df.columns) > 2:
        df = df.iloc[:, :2]  # Keep only the first two columns
    
    # Rename columns
    if len(df.columns) == 2:
        df.columns = ["Application Number", "Decision"]
    else:
        st.error("Insufficient data columns detected.")
        return None, None

    df["Application Number"] = df["Application Number"].astype(str)

    return df, file_name

# Fetch the data
precomputed_df, file_name = fetch_data()

# Function to generate PDF from DataFrame
def generate_pdf(dataframe, filename):
    pdf = FPDF()
    pdf.set_auto_page_break(auto=True, margin=10)
    pdf.add_page()
    pdf.set_font("Arial", size=12)

    pdf.cell(200, 10, txt=filename, ln=True, align="C")
    pdf.ln(10)

    # Table Header
    pdf.set_font("Arial", "B", 10)
    pdf.cell(50, 10, "Application Number", border=1)
    pdf.cell(50, 10, "Decision", border=1)
    pdf.ln()

    # Table Data
    pdf.set_font("Arial", size=10)
    for _, row in dataframe.iterrows():
        pdf.cell(50, 10, row["Application Number"], border=1)
        pdf.cell(50, 10, row["Decision"], border=1)
        pdf.ln()

    pdf_output = BytesIO()
    pdf.output(pdf_output, "F")
    pdf_output.seek(0)
    return pdf_output

# Provide Download Options
if precomputed_df is not None and file_name:
    st.success("Data fetched successfully!")

    # Excel Download
    excel_buffer = BytesIO()
    precomputed_df.to_excel(excel_buffer, index=False, engine="openpyxl")
    excel_buffer.seek(0)
    st.download_button(
        label="Download Excel File",
        data=excel_buffer,
        file_name=f"{file_name}.xlsx",
        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
    )

    # PDF Download
    pdf_buffer = generate_pdf(precomputed_df, file_name)
    st.download_button(
        label="Download PDF File",
        data=pdf_buffer,
        file_name=f"{file_name}.pdf",
        mime="application/pdf",
    )