Create lit1.py

lit1.py

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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.")