Update lit.py

lit.py

@@ -18,104 +18,118 @@ headers = {
     )
 }
 
-# Step 1: Scrape the website to find the .ods file link
-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')
-            break
-    
-    # If the link was found, proceed to download the file
-    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)
+# 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
         
-        st.write(f"Found visa decision file: [Download Link]({file_url})")
+        # Reset index after cleaning
+        df.reset_index(drop=True, inplace=True)
         
-        # Step 2: Download the .ods file
-        file_response = requests.get(file_url, headers=headers)
+        # Convert "Application Number" to string for consistency
+        df['Application Number'] = df['Application Number'].astype(str)
         
-        if file_response.status_code == 200:
-            ods_file = BytesIO(file_response.content)
+        # Step 4: Get user input for application number using Streamlit
+        user_application_number = st.text_input("Enter your Application Number")
+        
+        # Step 5: Check if the application number exists in the DataFrame
+        if user_application_number:
+            result = df[df['Application Number'] == user_application_number]
             
-            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_application_number = st.text_input("Enter your Application Number")
+            if not result.empty:
+                decision = result.iloc[0]['Decision']
+                if decision.lower() == 'approved':
+                    st.success(f"Congratulations! Your visa application ({user_application_number}) has been Approved.")
+                elif decision.lower() == 'rejected':
+                    st.error(f"Sorry, your visa application ({user_application_number}) has been Rejected.")
+                else:
+                    st.warning(f"Your visa application ({user_application_number}) has a status of '{decision}'.")
+            else:
+                st.warning(f"No record found for Application Number: {user_application_number}.")
                 
-                # Step 5: Check if the application number exists in the DataFrame
-                if user_application_number:
-                    result = df[df['Application Number'] == user_application_number]
+                # Convert Application Numbers to integers for comparison
+                df['Application Number'] = df['Application Number'].astype(int)
+                try:
+                    user_application_number_int = int(user_application_number)
                     
-                    if not result.empty:
-                        st.success(f"Congratulations! Your visa application ({user_application_number}) has been {result.iloc[0]['Decision']}.")
-                    else:
-                        st.warning(f"No record found for Application Number: {user_application_number}.")
-                        
-                        # Convert Application Numbers to integers for comparison
-                        df['Application Number'] = df['Application Number'].astype(int)
-                        user_application_number = int(user_application_number)
-                        
-                        # Step 6: Find the nearest pre and post application numbers
-                        df_sorted = df.sort_values(by='Application Number')
-                        pre_number = df_sorted[df_sorted['Application Number'] < user_application_number].tail(1)
-                        post_number = df_sorted[df_sorted['Application Number'] > user_application_number].head(1)
-                        
-                        # Prepare the results
-                        pre_diff = user_application_number - pre_number['Application Number'].values[0] if not pre_number.empty else None
-                        post_diff = post_number['Application Number'].values[0] - user_application_number if not post_number.empty else None
-                        
-                        result_table = pd.DataFrame({
-                            "Nearest Application": ['Before', 'After'],
-                            "Application Number": [pre_number['Application Number'].values[0] if not pre_number.empty else None,
-                                                  post_number['Application Number'].values[0] if not post_number.empty else None],
-                            "Decision": [pre_number['Decision'].values[0] if not pre_number.empty else None,
-                                         post_number['Decision'].values[0] if not post_number.empty else None],
-                            "Difference": [pre_diff, post_diff]
-                        })
+                    # Step 6: Find the nearest pre and post application numbers
+                    df_sorted = df.sort_values(by='Application Number')
+                    pre_number = df_sorted[df_sorted['Application Number'] < user_application_number_int].tail(1)
+                    post_number = df_sorted[df_sorted['Application Number'] > user_application_number_int].head(1)
+                    
+                    # Prepare the results
+                    pre_diff = user_application_number_int - pre_number['Application Number'].values[0] if not pre_number.empty else None
+                    post_diff = post_number['Application Number'].values[0] - user_application_number_int if not post_number.empty else None
+                    
+                    result_table = pd.DataFrame({
+                        "Nearest Application": ['Before', 'After'],
+                        "Application Number": [pre_number['Application Number'].values[0] if not pre_number.empty else None,
+                                              post_number['Application Number'].values[0] if not post_number.empty else None],
+                        "Decision": [pre_number['Decision'].values[0] if not pre_number.empty else None,
+                                     post_number['Decision'].values[0] if not post_number.empty else None],
+                        "Difference": [pre_diff, post_diff]
+                    })
 
-                        # Step 7: Display the nearest application numbers in tabular form
-                        st.subheader("Nearest Application Numbers")
-                        st.table(result_table)
-            
-            except Exception as e:
-                st.error(f"Error reading the .ods file: {e}")
-        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}")
+                    # Step 7: Display the nearest application numbers in tabular form
+                    st.subheader("Nearest Application Numbers")
+                    st.table(result_table)
+                except ValueError:
+                    st.error("Invalid Application Number format. Please enter a numeric value.")
     
+    except Exception as e:
+        st.error(f"Error reading the .ods file: {e}")
+else:
+    st.error("No file data available.")