Update app.py

app.py

@@ -1,10 +1,10 @@
 import streamlit as st
 
-
 # Dish allergen recognition part
 # load the necessary libraries
 import matplotlib.pyplot as plt
 import seaborn
+
 # set the style of the plots
 plt.rcParams["figure.figsize"] = (15, 10)
 plt.rcParams["figure.dpi"] = 125
@@ -13,14 +13,15 @@ plt.rcParams['font.family'] = ['sans-serif']
 plt.rcParams['font.sans-serif'] = ['DejaVu Sans']
 plt.style.use('ggplot')
 seaborn.set_style("whitegrid", {'axes.grid': False})
-plt.rcParams['image.cmap'] = 'gray' # grayscale looks better
-from pathlib import Path # Path management
+plt.rcParams['image.cmap'] = 'gray'  # grayscale looks better
+from pathlib import Path  # Path management
 import numpy as np
 import pandas as pd
 import os
 from skimage.io import imread as imread
 from skimage.util import montage
 from PIL import Image
+
 montage_rgb = lambda x: np.stack([montage(x[:, :, :, i]) for i in range(x.shape[3])], -1)
 from skimage.color import label2rgb
 
@@ -32,7 +33,7 @@ mapping_file = Path('clean_list.json')
 alleg_df = pd.read_json(mapping_file)
 
 # add the image path into the mapping dataset
-alleg_df['image_path'] = alleg_df['image_path'].map(lambda x: image_dir / 'new' / x) 
+alleg_df['image_path'] = alleg_df['image_path'].map(lambda x: image_dir / 'new' / x)
 print(alleg_df['image_path'].map(lambda x: x.exists()).value_counts())
 allergens = alleg_df.columns[3:].tolist()
 alleg_df.sample(2)
@@ -42,7 +43,7 @@ from pathlib import Path
 # load the color features
 color_file = Path('color_features.json')
 color_feat_df = pd.read_json(color_file)
-color_feat_df['image_path'] = color_feat_df['image_path'].map(lambda x: image_dir / 'new' / x) 
+color_feat_df['image_path'] = color_feat_df['image_path'].map(lambda x: image_dir / 'new' / x)
 
 # create a dictionary of color features
 color_feat_dict = {c_row['image_path']: c_row['color_features'] for _, c_row in color_feat_df.iterrows()}
@@ -51,21 +52,21 @@ alleg_df['color_features'] = alleg_df['image_path'].map(color_feat_dict.get)
 alleg_df.sample(2)
 
 # calculate the correlation matrix of the allergens and draw the heatmap
-co_all = np.corrcoef(np.stack(alleg_df[allergens].applymap(lambda x: 1 if x>0 else 0).values, 0).T)
+co_all = np.corrcoef(np.stack(alleg_df[allergens].applymap(lambda x: 1 if x > 0 else 0).values, 0).T)
 fig, ax1 = plt.subplots(1, 1, figsize=(10, 10))
 seaborn.heatmap(co_all, annot=True, fmt='2.1%', ax=ax1, cmap='RdBu', vmin=-1, vmax=1)
 ax1.set_xticklabels(allergens, rotation=90)
 ax1.set_yticklabels(allergens);
 
-
 # package the allergens together
-alleg_df['allergy_vec'] = alleg_df[allergens].applymap(lambda x: 1 if x>0 else 0).values.tolist()
+alleg_df['allergy_vec'] = alleg_df[allergens].applymap(lambda x: 1 if x > 0 else 0).values.tolist()
 
 # split the dataset into training and validation sets
 from sklearn.model_selection import train_test_split
-train_df, valid_df = train_test_split(alleg_df.drop(columns='ingredients_list'), 
-                                      test_size=0.1, 
-                                      random_state=2019, 
+
+train_df, valid_df = train_test_split(alleg_df.drop(columns='ingredients_list'),
+                                      test_size=0.1,
+                                      random_state=2019,
                                       stratify=alleg_df['allergy_vec'].map(lambda x: x[0:3]))
 
 # reset the index of the training and validation datasets
@@ -76,26 +77,25 @@ valid_df.reset_index(inplace=True)
 print(train_df.shape[0], 'training images')
 print(valid_df.shape[0], 'validation images')
 
-
 # there are 8687 training images and 966 validation images
 
 # The np.stack() function is used to stack the data in the DataFrame into a NumPy array.
 train_x_vec = np.stack(train_df['color_features'].values, 0)
 train_y_vec = np.stack(train_df['allergy_vec'], 0)
 
-
 # load the necessary libraries for the model
 
 from sklearn.pipeline import make_pipeline
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.preprocessing import RobustScaler  # RobustScaler
 # create a pipeline for the model, it will make the model first scale the data and then use the Random Forest Regressor to train the model
-#rf_pipe = make_pipeline(RobustScaler(), RandomForestRegressor(n_estimators=15))
-#rf_pipe.fit(train_x_vec, train_y_vec)
+# rf_pipe = make_pipeline(RobustScaler(), RandomForestRegressor(n_estimators=15))
+# rf_pipe.fit(train_x_vec, train_y_vec)
 # But in this file, In order to shorten the running time of the app, we saved the trained model file in advance, and read the trained model directly here
 
 
 import joblib
+
 # read trained model result
 loaded_rf_pipe = joblib.load('rf_model.joblib')
 
@@ -107,11 +107,12 @@ from sklearn.metrics import roc_auc_score, roc_curve, accuracy_score
 import matplotlib.pyplot as plt
 from imageio import imread
 
+
 @st.cache_data
 def show_model_results(_in_model, picture_number=None):
     # Ensure 'valid_df', 'valid_x_vec', 'valid_y_vec', 'train_df', 'train_x_vec', 'train_y_vec', 'allergens' are defined
     # stack the data in the DataFrame into a NumPy array.
-    
+
     valid_x_vec = np.stack(valid_df['color_features'].values, 0)
     valid_y_vec = np.stack(valid_df['allergy_vec'], 0)
 
@@ -131,9 +132,9 @@ def show_model_results(_in_model, picture_number=None):
         auc = roc_auc_score(y_vec[:, i], valid_pred[:, i])
         acc = accuracy_score(y_vec[:, i], valid_pred[:, i] > 0.5)
         ax1.plot(tpr, fpr, '.-', label='{}: AUC {:0.2f}, Accuracy: {:2.0%}'.format(c_allergen, auc, acc))
-        all_rows += [{'allegen': c_allergen, 
-                      'prediction': valid_pred[j, i], 
-                      'class': 'Positive' if y_vec[j, i] > 0.5 else 'Negative'} 
+        all_rows += [{'allegen': c_allergen,
+                      'prediction': valid_pred[j, i],
+                      'class': 'Positive' if y_vec[j, i] > 0.5 else 'Negative'}
                      for j in range(valid_pred.shape[0])]
 
     # Plotting the predictions of the model versus the true labels
@@ -148,13 +149,12 @@ def show_model_results(_in_model, picture_number=None):
     d_ax.set_title('Overall')
     d_ax.legend()
 
-
     # Plotting the predictions of the model versus the true labels for the test picture
-    
+
     ax1.legend()
     for (_, c_row), (c_ax, d_ax) in zip(
-    valid_df.iloc[valid_num:valid_num+1].iterrows(), 
-    m_axs[1:]):
+            valid_df.iloc[valid_num:valid_num + 1].iterrows(),
+            m_axs[1:]):
         c_ax.imshow(imread(c_row['image_path']))
         c_ax.set_title(c_row['title'])
         c_ax.axis('off')
@@ -166,18 +166,20 @@ def show_model_results(_in_model, picture_number=None):
         d_ax.set_yticklabels(allergens, rotation=0)
         d_ax.set_xlim(0, 1)
         d_ax.legend()
-    
+
     # add the current figures into Streamlit page
     st.pyplot(fig)
     return st.write("Completed")
 
+
 # Get the image paths
 image_paths = valid_df['image_path'].tolist()
 
 
 # Streamlit app
 def recognition():
-    st.write(f'<span style="font-size:20px;">We use the images from the test set as examples.</span>', unsafe_allow_html=True)
+    st.write(f'<span style="font-size:20px;">We use the images from the test set as examples.</span>',
+             unsafe_allow_html=True)
 
     image_paths = valid_df['image_path'].tolist()
 
@@ -192,46 +194,47 @@ def recognition():
             image = imread(image_paths[index])
             axes[i, j].imshow(image)
             axes[i, j].axis('off')
-            axes[i, j].set_title(f'Image {index+1}')
+            axes[i, j].set_title(f'Image {index + 1}')
 
     st.pyplot(fig)
 
-    num_images = 10 
+    num_images = 10
 
     # User interaction to select image
-    st.write(f'<span style="font-size:20px;">Enter the image number you want to analyze.</span>', unsafe_allow_html=True)
+    st.write(f'<span style="font-size:20px;">Enter the image number you want to analyze.</span>',
+             unsafe_allow_html=True)
 
     # User input
     choice = st.number_input(f"Range (1-{num_images}): ", min_value=1, max_value=num_images)
-    
+
     # Show model results
     if st.button('Show Results'):
-        show_model_results(loaded_rf_pipe, choice-1)
-
-
+        show_model_results(loaded_rf_pipe, choice - 1)
 
 
 # Menu-allergen database part
 # load the libraries we need
 import streamlit as st
 import pandas as pd
-import plotly.express as px # Streamlined plotting and interactive visualization
+import plotly.express as px  # Streamlined plotting and interactive visualization
+
 # input our dataset
 
 # Create a text element and let customers know the data is loading.
 data = pd.read_csv('data.csv')
 
+
 # set the function to filter the allgergens customers may have
 # The mask is created by negating the result of checking whether the 'Allergens' column in the data contains any of the allergens joined by '|'.
 # | (pipe symbol) is used for "or" logic when joining the allergens that means the union of selected allergons will be filtered.
 def filter_products(allergens):
     # The ~ in front of it negates the result. This is for deleting the food data containing the allergens
-    mask = ~data['Allergens'].str.contains('|'.join(allergens), case=False, na=False) # case=False shows not considering case, and na=False handles missing values appropriately.
+    mask = ~data['Allergens'].str.contains('|'.join(allergens), case=False,
+                                           na=False)  # case=False shows not considering case, and na=False handles missing values appropriately.
     filtered_data = data[mask]
     return filtered_data
 
 
-
 import streamlit as st
 import folium
 from streamlit_folium import st_folium
@@ -242,72 +245,76 @@ from dotenv import load_dotenv
 
 import streamlit as st
 
-
-
-
-
-
-
-page = st.sidebar.radio("Page", ["Home Page", "Menu-allergen database", "Dish_Allergen_Recognition", "Hospital Navigator"])
+page = st.sidebar.radio("Page",
+                        ["Home Page", "Menu-allergen database", "Dish_Allergen_Recognition", "Hospital Navigator"])
 
 if page == "Home Page":
     st.title("TrallergyFree")
     st.image('logo.jpg')
-    st.write("The purpose of the TrallergyFree app is to provide a reliable and convenient solution for individuals with food allergies, ensuring their safety and peace of mind in various dining situations. The app aims to assist users in identifying allergens in dishes, particularly when they are unable to communicate effectively about their dietary restrictions, such as when traveling abroad. By leveraging advanced image recognition technology, TrallergyFree empowers users to make informed decisions about their food choices, thereby reducing the risk of allergic reactions and enhancing their overall dining experience.")
-    
+    st.write(
+        "The purpose of the TrallergyFree app is to provide a reliable and convenient solution for individuals with food allergies, ensuring their safety and peace of mind in various dining situations. The app aims to assist users in identifying allergens in dishes, particularly when they are unable to communicate effectively about their dietary restrictions, such as when traveling abroad. By leveraging advanced image recognition technology, TrallergyFree empowers users to make informed decisions about their food choices, thereby reducing the risk of allergic reactions and enhancing their overall dining experience.")
+
 
 elif page == "Menu-allergen database":
     st.title("Menu-allergen database")
     # st.multiselect is a widget in the streamlit library. It is used to create a multiple selection dropdown menu.
     # It allows users to select multiple options from a given list of choices which are collected by unique
 
-    allergens = st.multiselect("What foods are you allergic to?", 
-      [
-        "Wheat",
-        "Nuts",
-        "Sesame",
-        "Soy",
-        "Mustard",
-        "Celery",
-        "Dairy",
-        "Egg",
-        'Others',
-      ]
-    )
-    
+    allergens = st.multiselect("What foods are you allergic to?",
+                               [
+                                   "Wheat",
+                                   "Nuts",
+                                   "Sesame",
+                                   "Soy",
+                                   "Mustard",
+                                   "Celery",
+                                   "Dairy",
+                                   "Egg",
+                                   'Others',
+                               ]
+                               )
+
     # When customers select one or more allergens, our interactive page will recommend the dishes that don't contain the allergens
     if allergens:
         # Filter and display the recommendations
-        recommended_products = filter_products(allergens) 
-        if not recommended_products.empty: # case when there is food not containing the selected allergens
+        recommended_products = filter_products(allergens)
+        if not recommended_products.empty:  # case when there is food not containing the selected allergens
             # Group products by type and create a tab for each type
             st.write("Below is a list of allergen-free products. Please make your selection✅")
-            #st.write("🔸You can choose the food you want to eat and select it in the box⬜🔸")
-            product_types = recommended_products['type'].unique() # de-weighted product types for choosing
-            tabs = st.tabs([str(pt) for pt in product_types]) # show tabs function
-    
+            # st.write("🔸You can choose the food you want to eat and select it in the box⬜🔸")
+            product_types = recommended_products['type'].unique()  # de-weighted product types for choosing
+            tabs = st.tabs([str(pt) for pt in product_types])  # show tabs function
+
             # create an empty dataframe for all selected products
             all_selected_products = pd.DataFrame()
-            for i, tab in enumerate(tabs): # interactive function with tabs for products
+            for i, tab in enumerate(tabs):  # interactive function with tabs for products
                 with tab:
                     type_specific_products = recommended_products[recommended_products['type'] == product_types[i]]
-                    type_specific_products['selected'] = False # This adds a 'selected' column and initializes it to False.
+                    type_specific_products[
+                        'selected'] = False  # This adds a 'selected' column and initializes it to False.
                     for j, row in type_specific_products.iterrows():
                         # create columns and display the product name and allergens
-                        cols = st.columns([1, 1, 1]) 
+                        cols = st.columns([1, 1, 1])
                         cols[0].write(row['Products'])
                         cols[1].write(row['Allergens'])
-                        selected = cols[2].checkbox("Select", key=f"select_{i}_{j}") # set a checkbox for selection. The key is set uniquely for each row and tab
-                        type_specific_products.at[j, 'selected'] = selected # Updates the 'selected' value in the type-specific product based on the checkbox state
+                        selected = cols[2].checkbox("Select",
+                                                    key=f"select_{i}_{j}")  # set a checkbox for selection. The key is set uniquely for each row and tab
+                        type_specific_products.at[
+                            j, 'selected'] = selected  # Updates the 'selected' value in the type-specific product based on the checkbox state
                     selected_products = type_specific_products[type_specific_products['selected']]
-                    all_selected_products = pd.concat([all_selected_products, selected_products]) # row concatenation, the axis in pd.concat is 0 in default
-    
-            if not all_selected_products.empty: # case when customers select products
+                    all_selected_products = pd.concat([all_selected_products,
+                                                       selected_products])  # row concatenation, the axis in pd.concat is 0 in default
+            #display the nutritional information of selected products 
+            if not all_selected_products.empty:  # case when customers select products
                 st.divider()
                 st.write("Below is the nutritional information for the selected products:")
-                st.dataframe(all_selected_products[['type', 'Products', 'Allergens', 'Total Energy (Kcal)', 'Carbohydrate (g)', 'Protein (g)', 'Total Fat (g)', 'Sodium (g)', 'Sugar (g)']])
-                nutritional_totals = all_selected_products[['Total Energy (Kcal)', 'Carbohydrate (g)', 'Protein (g)', 'Total Fat (g)', 'Sodium (g)', 'Sugar (g)']].sum()
-                
+                st.dataframe(all_selected_products[
+                                 ['type', 'Products', 'Allergens', 'Total Energy (Kcal)', 'Carbohydrate (g)',
+                                  'Protein (g)', 'Total Fat (g)', 'Sodium (g)', 'Sugar (g)']])
+                nutritional_totals = all_selected_products[
+                    ['Total Energy (Kcal)', 'Carbohydrate (g)', 'Protein (g)', 'Total Fat (g)', 'Sodium (g)',
+                     'Sugar (g)']].sum()
+
                 # Display metrics for nutritional totals in a 3x2 grid
                 st.divider()
                 st.write("Below are the nutritional totals for the selected products:")
@@ -321,47 +328,50 @@ elif page == "Menu-allergen database":
                 with col3:
                     st.metric("Protein", f"{nutritional_totals['Protein (g)']} g")
                     st.metric("Sugar", f"{nutritional_totals['Sugar (g)']} g")
-                
+
                 st.divider()
                 st.write("Below are the nutrient proportions of the selected products:")
-                # Create pie charts for each nutritional metric in 3x2 grid
+                # Create pie charts for each nutritional metric in 2x3 grid
                 # If they are multi selections, each product will contribute proportion in the pie chart
-                metrics = ['Total Energy (Kcal)', 'Carbohydrate (g)', 'Protein (g)', 'Total Fat (g)', 'Sodium (g)', 'Sugar (g)']
+                metrics = ['Total Energy (Kcal)', 'Carbohydrate (g)', 'Protein (g)', 'Total Fat (g)', 'Sodium (g)',
+                           'Sugar (g)']
                 for i in range(0, len(metrics), 2):
                     cols = st.columns(2)  # Define three columns
                     for j in range(2):
                         if i + j < len(metrics):  # Check if the current index is within the metrics array
                             with cols[j]:
                                 metric = metrics[i + j]
-                                fig = px.pie(all_selected_products, values=metric, names='Products', title=f'Proportion of {metric}', 
-                                            width=300, height=300)  # Adjust width and height if necessary
+                                fig = px.pie(all_selected_products, values=metric, names='Products',
+                                             title=f'Proportion of {metric}',
+                                             width=300, height=300)  # Adjust width and height if necessary
                                 st.plotly_chart(fig)
             else:
-                st.write("No selected products in this category.") # case when no product is selected
+                st.write("No selected products in this category.")  # case when no product is selected
         else:
-            st.write("No products found without the specified allergens.") # case when there is no food without selected allergens
-    
+            st.write(
+                "No products found without the specified allergens.")  # case when there is no food without selected allergens
+
 elif page == "Dish_Allergen_Recognition":
     st.title('Dish Allergen Recognition')
     if __name__ == "__main__":
         recognition()
-elif page == "Hospital Navigator":
-    st.title("Hospital Navigator")
+elif page == "Hospital Navigator":
+    st.title("Hospital Navigator")
     # Selection box for campus
     campus = st.selectbox(
         'Select the campus:',
         ('Yanqihu Campus', 'Zhongguancun Campus')
     )
-    
+    # Yanqihu campus part
     if campus == 'Yanqihu Campus':
         st.header('Hospitals and pharmacies within 2km of Yanqihu Campus')
-    
+
         # User's location for Yanqihu Campus
         latitude = 40.407521
         longitude = 116.68452
-    
+        # Create a map centered at the user's location
         hospital_map = folium.Map(location=[latitude, longitude], zoom_start=12)
-    
+
         # Add a marker for the user's location
         folium.Marker(
             location=[latitude, longitude],
@@ -369,7 +379,7 @@ elif page == "Hospital Navigator":
             tooltip="User's Location",
             icon=folium.Icon(color='black', icon="info-sign")  # Using built-in icon
         ).add_to(hospital_map)
-    
+        #import the dataset of hospitals nearby yanqihu campus
         df_hospitals = pd.read_excel('yanqihu_hospitals.xlsx', engine='openpyxl')
         st.write("The table below shows hospitals within 2km of you.🏥")
         st.dataframe(df_hospitals)
@@ -382,7 +392,7 @@ elif page == "Hospital Navigator":
                 tooltip=row['name'],
                 icon=folium.Icon(color='blue', icon="info-sign")  # Using built-in icon
             ).add_to(hospital_map)
-    
+        #import the dataset of pharmacies nearby yanqihu campus
         df_pharmacies = pd.read_excel('yanqihu_pharmacies.xlsx', engine='openpyxl')
         st.write("The table below shows pharmacies within 2km of you.💊")
         st.dataframe(df_pharmacies)
@@ -395,19 +405,19 @@ elif page == "Hospital Navigator":
                 tooltip=row['name'],
                 icon=folium.Icon(color='purple', icon="info-sign")  # Using built-in icon
             ).add_to(hospital_map)
-    
+
         # Display the map in Streamlit
         st_folium(hospital_map, width=725, height=500)
-    
+
     elif campus == 'Zhongguancun Campus':
         st.header('Hospitals and pharmacies within 2km of Zhongguancun Campus')
-    
+
         # User's location for Zhongguancun Campus
         latitude = 39.979402
         longitude = 116.3134541
-    
+        # Create a map centered at the user's location
         hospital_map = folium.Map(location=[latitude, longitude], zoom_start=14)
-    
+
         # Add a marker for the user's location
         folium.Marker(
             location=[latitude, longitude],
@@ -415,7 +425,7 @@ elif page == "Hospital Navigator":
             tooltip="User's Location",
             icon=folium.Icon(color='black', icon="info-sign")  # Using built-in icon
         ).add_to(hospital_map)
-    
+        #import the dataset of hospitals nearby zhongguancun campus
         df_hospitals = pd.read_excel('zhongguancun_hospitals.xlsx', engine='openpyxl')
         st.write("The table below shows hospitals within 2km of you.🏥")
         st.dataframe(df_hospitals)
@@ -428,7 +438,7 @@ elif page == "Hospital Navigator":
                 tooltip=row['name'],
                 icon=folium.Icon(color='blue', icon="info-sign")  # Using built-in icon
             ).add_to(hospital_map)
-    
+        #import the dataset of pharmacies nearby zhongguancun campus
         df_pharmacies = pd.read_excel('zhongguancun_pharmacies.xlsx', engine='openpyxl')
         st.write("The table below shows pharmacies within 2km of you.💊")
         st.dataframe(df_pharmacies)
@@ -441,38 +451,33 @@ elif page == "Hospital Navigator":
                 tooltip=row['name'],
                 icon=folium.Icon(color='purple', icon="info-sign")  # Using built-in icon
             ).add_to(hospital_map)
-    
+
         # Display the map in Streamlit
         st_folium(hospital_map, width=725, height=500)
-    
+
     # Explanation of color keys
     st.divider()
     st.write("Color Key:\n- Black🖤: Your current location📍\n- Blue💙: Hospitals🏥\n- Purple💜: Pharmacies💊")
-    
-    st.divider()   
-    
-    
+
+    st.divider()
+
     # Streamlit page title
     st.title('Enter Your Location')
-    
+
     # Allow users to input latitude and longitude
     latitude = st.number_input('Enter your latitude:(eg.39.918058)', value=0.0, format="%.6f")
     longitude = st.number_input('Enter your longitude:(eg.116.397026)', value=0.0, format="%.6f")
-    
+
     # Display the inputted location information if valid
     if latitude and longitude:
         st.write(f"Your entered location is at latitude {latitude} and longitude {longitude}")
-    
+
     # Load environment variables
     load_dotenv()
     api_key = os.getenv('api_key')  # Your Amap API key
-    
-    # User's location
-    #latitude = 39.993015
-    #longitude = 116.473168
-    
+    # Create a map centered at the user's location
     hospital_map = folium.Map(location=[latitude, longitude], zoom_start=14)
-    
+
     # Add a marker for the user's location
     folium.Marker(
         location=[latitude, longitude],
@@ -480,21 +485,21 @@ elif page == "Hospital Navigator":
         tooltip="User's Location",
         icon=folium.Icon(color='black')
     ).add_to(hospital_map)
-    
+
     # Hospital API URL
     url_hospital = f'https://restapi.amap.com/v3/place/around?key={api_key}&location={longitude},{latitude}&types=090100&radius=2000&offset=20&page=1&extensions=all'
     # Pharmacy API URL
     url_pharmacy = f'https://restapi.amap.com/v3/place/around?key={api_key}&location={longitude},{latitude}&types=090601&radius=2000&offset=20&page=1&extensions=all'
-    
+
     try:
         # Make API request for hospitals
         response_hospital = requests.get(url_hospital)
         response_pharmacy = requests.get(url_pharmacy)  # API request for pharmacies
-        
+
         if response_hospital.status_code == 200 and response_pharmacy.status_code == 200:
             data_hospital = response_hospital.json()
             data_pharmacy = response_pharmacy.json()
-            
+
             # Process hospital data
             if 'pois' in data_hospital:
                 df_hospitals = pd.DataFrame(data_hospital['pois'])
@@ -511,7 +516,7 @@ elif page == "Hospital Navigator":
                             tooltip=row['name'],
                             icon=folium.Icon(color='blue')
                         ).add_to(hospital_map)
-    
+
             # Process pharmacy data
             if 'pois' in data_pharmacy:
                 df_pharmacies = pd.DataFrame(data_pharmacy['pois'])
@@ -528,11 +533,11 @@ elif page == "Hospital Navigator":
                             tooltip=row['name'],
                             icon=folium.Icon(color='purple')  # Different color for pharmacies
                         ).add_to(hospital_map)
-    
+
             # Display the map in Streamlit
             st.title('Nearby Hospitals and Pharmacies')
             st_folium(hospital_map, width=725, height=500)
-            st.write("Color Key:\n- Black🖤: Your current location📍\n- Blue💙: Hospitals🏥\n- Purple💜: Pharmacies💊")       
+            st.write("Color Key:\n- Black🖤: Your current location📍\n- Blue💙: Hospitals🏥\n- Purple💜: Pharmacies💊")
         else:
             st.error("Failed to retrieve data. Check the status codes.")
     except Exception as e: