Update visa_checker_with_download.py

visa_checker_with_download.py

@@ -4,6 +4,7 @@ import bisect
 import requests
 from io import BytesIO
 from bs4 import BeautifulSoup
+from fpdf import FPDF
 
 # ------------------------------------------------------------------------------------
 # Step 1: Load Data (Fetch and Prepare the DataFrame)
@@ -106,7 +107,83 @@ def binary_search_nearest(df, target):
     return before, after
 
 # ------------------------------------------------------------------------------------
-# Step 3: Search Application Status
+# Step 3: Export DataFrame to PDF
+# ------------------------------------------------------------------------------------
+
+def export_to_pdf(df, file_name):
+    """
+    Converts the cleaned DataFrame to a PDF file.
+
+    Args:
+        df: The DataFrame to be converted to PDF.
+        file_name: The name of the PDF file to create.
+
+    Returns:
+        A BytesIO object containing the PDF file.
+    """
+    pdf = FPDF()
+    pdf.set_auto_page_break(auto=True, margin=15)
+    pdf.add_page()
+    pdf.set_font("Arial", size=12)
+
+    # Title
+    pdf.set_font("Arial", style="B", size=16)
+    pdf.cell(0, 10, "Visa Decisions Data", ln=True, align="C")
+    pdf.ln(10)
+
+    # Table Header
+    pdf.set_font("Arial", style="B", size=12)
+    for col in df.columns:
+        pdf.cell(50, 10, col, border=1)
+    pdf.ln()
+
+    # Table Data
+    pdf.set_font("Arial", size=12)
+    for _, row in df.iterrows():
+        for cell in row:
+            pdf.cell(50, 10, str(cell), border=1)
+        pdf.ln()
+
+    pdf_output = BytesIO()
+    pdf.output(pdf_output)
+    pdf_output.seek(0)
+
+    return pdf_output
+
+# ------------------------------------------------------------------------------------
+# Step 4: Allow User to Download Cleaned DataFrame
+# ------------------------------------------------------------------------------------
+
+def download_dataframe(df, file_name):
+    """
+    Provides a download link for the cleaned DataFrame as CSV or PDF.
+
+    Args:
+        df: The cleaned DataFrame to download.
+        file_name: The name of the original file for naming convention.
+    """
+    cleaned_file_name_csv = f"{file_name.replace('.ods', '.csv')}"
+    csv_data = df.to_csv(index=False)
+
+    st.sidebar.download_button(
+        label="Download Cleaned Data as CSV",
+        data=csv_data,
+        file_name=cleaned_file_name_csv,
+        mime="text/csv"
+    )
+
+    cleaned_file_name_pdf = f"{file_name.replace('.ods', '.pdf')}"
+    pdf_data = export_to_pdf(df, cleaned_file_name_pdf)
+
+    st.sidebar.download_button(
+        label="Download Cleaned Data as PDF",
+        data=pdf_data,
+        file_name=cleaned_file_name_pdf,
+        mime="application/pdf"
+    )
+
+# ------------------------------------------------------------------------------------
+# Step 5: Search Application Status
 # ------------------------------------------------------------------------------------
 
 def search_application(df):
@@ -120,23 +197,11 @@ def search_application(df):
 
     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)
+        if not user_input.isdigit() or len(user_input) < 8:
+            st.warning("Please enter at least 8 digits for your VISA application number.")
+            return
+
+        application_number = int(user_input)
 
         # Search for the application number in the DataFrame
         result = df[df["Application Number"] == application_number]
@@ -151,8 +216,6 @@ def search_application(df):
                 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({
@@ -174,27 +237,6 @@ def search_application(df):
             else:
                 st.info("No nearest application numbers found.")
 
-# ------------------------------------------------------------------------------------
-# Step 4: Allow User to Download Cleaned DataFrame
-# ------------------------------------------------------------------------------------
-
-def download_dataframe(df, file_name):
-    """
-    Provides a download link for the cleaned DataFrame.
-
-    Args:
-        df: The cleaned DataFrame to download.
-        file_name: The name of the original file for naming convention.
-    """
-    cleaned_file_name = f"cleaned_{file_name.replace('.ods', '.csv')}"
-    csv_data = df.to_csv(index=False)
-    st.download_button(
-        label="Download Cleaned Data",
-        data=csv_data,
-        file_name=cleaned_file_name,
-        mime="text/csv"
-    )
-
 # ------------------------------------------------------------------------------------
 # Main Streamlit Application Logic
 # ------------------------------------------------------------------------------------