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import requests
import pandas as pd
from io import BytesIO
from bs4 import BeautifulSoup
import streamlit as st
# Streamlit app title
st.title("Visa Application Status Checker")
# URL of the website to scrape
url = "https://www.ireland.ie/en/india/newdelhi/services/visas/processing-times-and-decisions/"
# Headers to mimic a browser request
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"
)
}
# Step 1: Function to fetch and cache the .ods file
@st.cache_data(ttl=3600, max_entries=1)
def fetch_ods_file():
response = requests.get(url, headers=headers)
if response.status_code == 200:
soup = BeautifulSoup(response.content, 'html.parser')
# Find all anchor tags
links = soup.find_all('a')
# Search for the link containing the specific text
file_url = None
for link in links:
link_text = link.get_text(strip=True)
if "Visa decisions made from 1 January 2024 to" in link_text:
file_url = link.get('href')
file_name = link_text
break
if file_url:
# Make the link absolute if it is relative
if not file_url.startswith('http'):
file_url = requests.compat.urljoin(url, file_url)
file_response = requests.get(file_url, headers=headers)
if file_response.status_code == 200:
return BytesIO(file_response.content), file_name
else:
st.error(f"Failed to download the file. Status code: {file_response.status_code}")
else:
st.error("The specified link was not found.")
else:
st.error(f"Failed to retrieve the webpage. Status code: {response.status_code}")
return None, None
# Step 2: Fetch the cached .ods file
ods_file, cached_file_name = fetch_ods_file()
if ods_file:
try:
# Step 3: Read the .ods file into a DataFrame
df = pd.read_excel(ods_file, engine='odf')
# Clean up the DataFrame by dropping unnecessary columns
df.drop(columns=["Unnamed: 0", "Unnamed: 1"], inplace=True, errors='ignore')
# Drop empty rows and reset index
df.dropna(how='all', inplace=True)
df.reset_index(drop=True, inplace=True)
# Identify the header row and reformat DataFrame
for idx, row in df.iterrows():
if row['Unnamed: 2'] == 'Application Number' and row['Unnamed: 3'] == 'Decision':
df.columns = ['Application Number', 'Decision']
df = df.iloc[idx + 1:] # Skip the header row
break
# Reset index after cleaning
df.reset_index(drop=True, inplace=True)
# Convert "Application Number" to string for consistency
df['Application Number'] = df['Application Number'].astype(str)
# Step 4: Get user input for application number using Streamlit
user_input = st.text_input("Enter your Application Number (including IRL if applicable):")
if user_input:
# Input validation logic
if "irl" in user_input.lower():
try:
application_number = int("".join(filter(str.isdigit, user_input.lower().split("irl")[-1])))
if len(str(application_number)) < 8:
st.warning("Please enter a valid application number with at least 8 digits after IRL.")
st.stop()
except ValueError:
st.error("Invalid input after IRL. Please enter only digits.")
st.stop()
else:
if not user_input.isdigit() or len(user_input) < 8:
st.warning("Please enter at least 8 digits for your VISA application number.")
st.stop()
application_number = int(user_input)
# Check if the application number exists in the DataFrame
result = df[df['Application Number'] == str(application_number)]
if not result.empty:
decision = result.iloc[0]['Decision']
st.success(f"Application Number: **{application_number}**\n\nDecision: **{decision}**")
else:
st.warning(f"No record found for Application Number: {application_number}.")
# Find the nearest application numbers
df['Application Number'] = df['Application Number'].astype(int)
df['Difference'] = abs(df['Application Number'] - application_number)
nearest_records = df.nsmallest(2, 'Difference')
if not nearest_records.empty:
st.subheader("Nearest Application Numbers")
st.table(nearest_records[['Application Number', 'Decision', 'Difference']])
else:
st.info("No nearest application numbers found.")
except Exception as e:
st.error(f"Error reading the .ods file: {e}")
else:
st.error("No file data available.")
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