Create project_file.py

project_file.py

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+import streamlit as st
+import pandas as pd
+import bisect
+import requests
+from io import BytesIO
+from bs4 import BeautifulSoup
+
+# ------------------------------------------------------------------------------------
+# Step 1: Load Data (Fetch and Prepare the DataFrame)
+# ------------------------------------------------------------------------------------
+
+@st.cache_data(ttl=3600)
+def fetch_ods_file():
+    """
+    Fetches the .ods file from the visa decisions website and returns its binary content.
+
+    Returns:
+        - A BytesIO object containing the file content if successful.
+        - None if the file could not be fetched.
+    """
+    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"
+        )
+    }
+
+    response = requests.get(url, headers=headers)
+    if response.status_code == 200:
+        soup = BeautifulSoup(response.content, 'html.parser')
+        links = soup.find_all('a')
+
+        # Find the link containing the specific text
+        file_url = None
+        for link in links:
+            if "Visa decisions made from 1 January 2024 to" in link.get_text(strip=True):
+                file_url = link.get('href')
+                break
+
+        if file_url:
+            # Resolve relative URLs to absolute
+            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)
+    return None
+
+@st.cache_data
+def prepare_dataframe(file):
+    """
+    Prepares and cleans the DataFrame from the fetched .ods file.
+
+    Args:
+        file: The .ods file content as BytesIO.
+
+    Returns:
+        A cleaned and sorted DataFrame ready for searching.
+    """
+    df = pd.read_excel(file, engine='odf')
+    df.drop(columns=["Unnamed: 0", "Unnamed: 1"], inplace=True, errors="ignore")
+    df.dropna(how="all", inplace=True)
+    df.reset_index(drop=True, inplace=True)
+
+    # Identify the header row
+    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
+
+    # Process application numbers and sort the DataFrame
+    df["Application Number"] = df["Application Number"].astype(str).str.strip().astype(int)
+    df.sort_values(by="Application Number", inplace=True)
+    df.reset_index(drop=True, inplace=True)
+
+    return df
+
+# ------------------------------------------------------------------------------------
+# Step 2: Binary Search Utility for Finding Nearest Application Numbers
+# ------------------------------------------------------------------------------------
+
+def binary_search_nearest(df, target):
+    """
+    Uses binary search to find the nearest application numbers in the DataFrame.
+
+    Args:
+        df: The DataFrame containing the application numbers.
+        target: The target application number to search for.
+
+    Returns:
+        Two nearest application numbers (before and after the target).
+    """
+    application_numbers = df["Application Number"].tolist()
+    pos = bisect.bisect_left(application_numbers, target)
+
+    before = application_numbers[pos - 1] if pos > 0 else None
+    after = application_numbers[pos] if pos < len(application_numbers) else None
+
+    return before, after
+
+# ------------------------------------------------------------------------------------
+# Step 3: Search Application Status
+# ------------------------------------------------------------------------------------
+
+def search_application(df):
+    """
+    Handles the user input and searches for the application number in the DataFrame.
+
+    Args:
+        df: The DataFrame containing application numbers and decisions.
+    """
+    user_input = st.text_input("Enter your Application Number (including IRL if applicable):")
+
+    if user_input:
+        # Validate user input
+        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.")
+                    return
+            except ValueError:
+                st.error("Invalid input after IRL. Please enter only digits.")
+                return
+        else:
+            if not user_input.isdigit() or len(user_input) < 8:
+                st.warning("Please enter at least 8 digits for your VISA application number.")
+                return
+            elif len(user_input) > 8:
+                st.warning("The application number cannot exceed 8 digits. Please correct your input.")
+                return
+            application_number = int(user_input)
+
+        # Search for the application number in the DataFrame
+        result = df[df["Application Number"] == application_number]
+
+        if not result.empty:
+            decision = result.iloc[0]["Decision"]
+            if decision.lower() == "refused":
+                st.error(f"Application Number: {application_number}\n\nDecision: **Refused**")
+            elif decision.lower() == "approved":
+                st.success(f"Application Number: {application_number}\n\nDecision: **Approved**")
+            else:
+                st.info(f"Application Number: {application_number}\n\nDecision: **{decision}**")
+        else:
+            st.warning(f"No record found for Application Number: {application_number}.")
+
+            # Find nearest application numbers using binary search
+            before, after = binary_search_nearest(df, application_number)
+
+            nearest_records = pd.DataFrame({
+                "Nearest Application": ["Before", "After"],
+                "Application Number": [before, after],
+                "Decision": [
+                    df[df["Application Number"] == before]["Decision"].values[0] if before else None,
+                    df[df["Application Number"] == after]["Decision"].values[0] if after else None
+                ],
+                "Difference": [
+                    application_number - before if before else None,
+                    after - application_number if after else None
+                ]
+            }).dropna()
+
+            if not nearest_records.empty:
+                st.subheader("Nearest Application Numbers")
+                st.table(nearest_records.reset_index(drop=True))
+            else:
+                st.info("No nearest application numbers found.")
+
+# ------------------------------------------------------------------------------------
+# Main Streamlit Application Logic
+# ------------------------------------------------------------------------------------
+
+def main():
+    st.title("Visa Application Status Checker")
+
+    # Fetch and prepare the data
+    ods_file = fetch_ods_file()
+    if ods_file:
+        df = prepare_dataframe(ods_file)
+        if df is not None:
+            search_application(df)
+        else:
+            st.error("Failed to prepare the data.")
+    else:
+        st.error("Failed to fetch the .ods file.")
+
+if __name__ == "__main__":
+    main()