Create app.py

app.py

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+import streamlit as st  
+import pandas as pd  
+import numpy as np  
+import os  
+from pathlib import Path  
+  
+# Print the versions of the packages  
+st.write(f"Streamlit version: {st.__version__}")  
+st.write(f"Pandas version: {pd.__version__}")  
+st.write(f"Numpy version: {np.__version__}")  
+  
+# URLs for the logos  
+MAIN_LOGO_URL = "https://islamictrusthk.org/assets/images/top-logo.png"  
+SIDEBAR_LOGO_URL = "https://bot.islamictrusthk.org/assets/content_files/20240606095159123011.png"  
+  
+# Configure the page to be mobile-friendly  
+st.set_page_config(layout="centered", page_title="Restaurant Data Viewer")  
+  
+# Inject custom CSS for better mobile compatibility  
+st.markdown(  
+    """  
+    <style>  
+    /* Adjust font sizes for mobile devices */  
+    @media (max-width: 600px) {  
+        .stTextInput, .stMultiSelect {  
+            font-size: 14px;  
+        }  
+        .stDataFrame {  
+            font-size: 12px;  
+        }  
+        .stButton button {  
+            font-size: 14px;  
+        }  
+    }  
+    </style>  
+    """,  
+    unsafe_allow_html=True,  
+)  
+  
+# Directory to save uploaded files  
+UPLOAD_DIR = "uploaded_files"  
+os.makedirs(UPLOAD_DIR, exist_ok=True)  
+  
+# Function to load data  
+@st.cache_data  
+def load_data(file_path):  
+    df = pd.read_excel(file_path)  
+    df.fillna("Not Available", inplace=True)  
+    return df  
+  
+# Function to convert dataframe to CSV  
+def convert_df_to_csv(df):  
+    return df.to_csv(index=False).encode('utf-8')  
+  
+# Function to format date  
+def format_date_column(df, column):  
+    df[column] = pd.to_datetime(df[column], errors='coerce').dt.date  
+    return df  
+  
+# Function to verify required columns exist  
+def verify_columns(df, required_columns):  
+    missing_columns = [col for col in required_columns if col not in df.columns]  
+    return missing_columns  
+  
+# Function to display data in tiles  
+def display_tiles(df, cols):  
+    for i, (_, row) in enumerate(df.iterrows()):  
+        col = cols[i % len(cols)]  
+        with col:  
+            st.markdown(f"**Name:** {row['Name']}")  
+            st.markdown(f"**Cuisine:** {row['Cuisine']}")  
+            st.markdown(f"**Location:** {row['Location']}")  
+            st.markdown(f"**Restaurant Type:** {row['Restaurant Type']}")  
+            st.markdown(f"**Expiry Date:** {row['Expiry Date']}")  
+            st.markdown(f"**Website:** {row['Website']}")  
+            st.markdown(f"**Directions:** {row['Directions']}")  
+            st.markdown("---")  
+  
+# Initialize session state  
+if 'df' not in st.session_state:  
+    st.session_state.df = None  
+if 'authenticated' not in st.session_state:  
+    st.session_state.authenticated = False  
+if 'just_logged_in' not in st.session_state:  
+    st.session_state.just_logged_in = False  
+  
+# List of valid usernames and passwords  
+user_credentials = {  
+    'user1': 'password1',  
+    'user2': 'password2'  
+}  
+  
+# Authentication function  
+def authenticate(username, password):  
+    if username in user_credentials and user_credentials[username] == password:  
+        return True  
+    return False  
+  
+# Authentication block  
+if not st.session_state.authenticated:  
+    st.title("Login")  
+    username = st.text_input("Username")  
+    password = st.text_input("Password", type="password")  
+    if st.button("Login"):  
+        if authenticate(username, password):  
+            st.session_state.authenticated = True  
+            st.session_state.just_logged_in = True  
+            st.experimental_rerun()  # Refresh the app to reflect the login state  
+        else:  
+            st.error("Invalid username or password")  
+elif st.session_state.just_logged_in:  
+    st.session_state.just_logged_in = False  
+    st.experimental_rerun()  
+else:  
+    # Sidebar for logo, file upload, and file management  
+    with st.sidebar:  
+        st.image(SIDEBAR_LOGO_URL, use_column_width=True)  
+          
+        st.title("File Management")  
+          
+        # File uploader  
+        uploaded_file = st.file_uploader("Choose an Excel file", type="xlsx")  
+          
+        if uploaded_file:  
+            try:  
+                # Save the uploaded file  
+                file_path = os.path.join(UPLOAD_DIR, uploaded_file.name)  
+                with open(file_path, "wb") as f:  
+                    f.write(uploaded_file.getbuffer())  
+                      
+                st.success(f"File '{uploaded_file.name}' uploaded successfully.")  
+                  
+                df = load_data(file_path)  
+                st.session_state.df = df  
+              
+            except Exception as e:  
+                st.error(f"An error occurred: {e}")  
+          
+        # Manage existing files  
+        st.subheader("Manage Existing Files")  
+        existing_files = [f for f in os.listdir(UPLOAD_DIR) if f.endswith(".xlsx")]  
+        if existing_files:  
+            selected_file = st.selectbox("Select a file to view or delete", existing_files)  
+            if st.button("Delete Selected File"):  
+                os.remove(os.path.join(UPLOAD_DIR, selected_file))  
+                st.success(f"File '{selected_file}' deleted successfully.")  
+                st.experimental_rerun()  # Refresh the app to reflect changes  
+              
+            if st.button("Load Selected File") or st.session_state.df is None:  
+                try:  
+                    file_path = os.path.join(UPLOAD_DIR, selected_file)  
+                    df = load_data(file_path)  
+                    st.session_state.df = df  
+                except Exception as e:  
+                    st.error(f"An error occurred: {e}")  
+        else:  
+            st.info("No files available.")  
+  
+    # Display the main logo  
+    st.image(MAIN_LOGO_URL, use_column_width=True)  
+  
+    st.title("Restaurant Data Viewer")  
+  
+    # Display and filter the loaded dataframe if available  
+    if st.session_state.df is not None:  
+        df = st.session_state.df  
+          
+        # Define required columns  
+        required_columns = ['Name', 'Cuisine', 'Location', 'Restaurant Type', 'Expiry Date', 'Website', 'Directions']  
+          
+        # Verify required columns  
+        missing_columns = verify_columns(df, required_columns)  
+        if missing_columns:  
+            st.error(f"The following required columns are missing from the uploaded file: {', '.join(missing_columns)}")  
+        else:  
+            # Format the expiry date column to remove time  
+            df = format_date_column(df, 'Expiry Date')  
+              
+            # Display the dataframe  
+            st.subheader("Loaded Data")  
+            st.dataframe(df)  
+              
+            # Filter functionality  
+            st.subheader("Filters")  
+            # Use columns for a more responsive layout  
+            col1, col2, col3, col4, col5 = st.columns(5)  
+              
+            with col1:  
+                # Filter by Name  
+                name_filter = st.text_input("Name contains")  
+            with col2:  
+                # Filter by Cuisine  
+                cuisine_filter = st.multiselect("Cuisine", sorted(df['Cuisine'].unique()))  
+            with col3:  
+                # Filter by Location  
+                location_filter = st.multiselect("Location", df['Location'].unique())  
+            with col4:  
+                # Filter by Restaurant Type  
+                restaurant_type_filter = st.multiselect("Restaurant Type", df['Restaurant Type'].unique())  
+            with col5:  
+                # Filter by Expiry Date  
+                expiry_date_filter = st.date_input("Expiry Date", [])  
+              
+            # Apply filters  
+            filtered_df = df.copy()  
+              
+            if name_filter:  
+                filtered_df = filtered_df[filtered_df['Name'].str.contains(name_filter, case=False, na=False)]  
+            if cuisine_filter:  
+                filtered_df = filtered_df[filtered_df['Cuisine'].isin(cuisine_filter)]  
+            if location_filter:  
+                filtered_df = filtered_df[filtered_df['Location'].isin(location_filter)]  
+            if restaurant_type_filter:  
+                filtered_df = filtered_df[filtered_df['Restaurant Type'].isin(restaurant_type_filter)]  
+            if expiry_date_filter:  
+                if len(expiry_date_filter) == 1:  
+                    filtered_df = filtered_df[filtered_df['Expiry Date'] == expiry_date_filter[0]]  
+                elif len(expiry_date_filter) == 2:  
+                    start_date, end_date = expiry_date_filter  
+                    filtered_df = filtered_df[(filtered_df['Expiry Date'] >= start_date) & (filtered_df['Expiry Date'] <= end_date)]  
+              
+            # Display the filtered dataframe in a tile format  
+            st.subheader("Filtered Data")  
+            if not filtered_df.empty:  
+                num_cols = 3  # Number of columns for the tile layout  
+                cols = st.columns(num_cols)  
+                display_tiles(filtered_df, cols)  
+                  
+                # Download button for filtered data  
+                csv = convert_df_to_csv(filtered_df)  
+                st.download_button(  
+                    label="Download filtered data as CSV",  
+                    data=csv,  
+                    file_name='filtered_data.csv',  
+                    mime='text/csv',  
+                )  
+            else:  
+                st.info("No data matches the filter criteria.")