Create app.py

app.py

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+import streamlit as st
+
+st.markdown("""
+👋 Welcome to my guide on creating a streamlit application for tracking health care problems and conditions! 👨‍⚕️👩‍⚕️
+
+📜 Here's a step by step outline to get you started:
+
+Step 1: Install Streamlit 📥
+First things first, let's make sure we have Streamlit installed! Here's how:
+
+add a requirements.txt file with each library you will need including:
+streamlit
+pandas
+geopy
+folium
+
+Step 2: Create a new file 🆕
+Now that Streamlit is installed, let's create a new Python file for our application. 
+
+Here's how:
+Create a new app.py file.
+
+
+Step 3: Import the necessary libraries 📚
+The requirements.txt will be executed with pip install -r requirements.txt
+
+This will import the following libraries:
+streamlit (for building the app)
+pandas (for working with data)
+geopy (for geocoding and distance calculations)
+folium (for creating maps)
+
+Step 4: Create the user interface 🖥️
+Now let's create the user interface for our app! Here's how:
+
+Use the streamlit library to create a title for the app
+Create an input field for the user to enter their location
+Create checkboxes for the health care problems and conditions the user is interested in
+Use the streamlit library to create a button to submit the user's input
+Step 5: Retrieve and filter data 📊
+Now that we have the user's input, let's retrieve and filter the data we need. Here's how:
+
+Use the pandas library to read in a dataset of health care providers and facilities in the area
+Use the geopy library to geocode the user's location
+Calculate the distance between the user's location and each provider/facility in the dataset
+Filter the dataset based on the user's selected health care problems/conditions
+Step 6: Display the results 🌟
+Finally, let's display the results to the user! Here's how:
+
+Use the folium library to create a map with markers for each provider/facility in the filtered dataset
+Display the map to the user
+Use the streamlit library to display a table with information about each provider/facility in the filtered dataset
+🎉 And that's it! You now have a streamlit application for tracking health care problems and conditions and identifying providers and facilities in the user's area. Good luck building your app! 🚀
+
+
+""")
+
+
+import streamlit as st
+import pandas as pd
+from geopy.geocoders import Nominatim
+import folium
+
+# Set page title
+st.set_page_config(page_title="Healthcare Providers Map")
+
+# Define function to get geolocation data from user's input
+def get_location(address):
+    geolocator = Nominatim(user_agent="my_app")
+    location = geolocator.geocode(address)
+    return (location.latitude, location.longitude)
+
+# Define function to filter providers by selected health conditions
+def filter_providers(df, conditions):
+    return df[df['Conditions'].apply(lambda x: any(item for item in conditions if item in x))]
+
+# Load data
+#df = pd.read_csv('healthcare_providers.csv')
+df = pd.read_csv('minnesota_providers.csv')
+
+
+# Create UI elements
+st.title("Healthcare Providers Map")
+
+location_input = st.text_input("Enter your address to find healthcare providers in your area:")
+conditions_checkboxes = st.sidebar.multiselect("Select the health conditions you are interested in:",
+                                                ['Asthma', 'Cancer', 'Diabetes', 'Heart disease', 'High blood pressure'])
+
+if st.button("Find Providers"):
+    # Get user's location
+    user_location = get_location(location_input)
+
+    # Filter providers based on selected conditions
+    filtered_providers = filter_providers(df, conditions_checkboxes)
+
+    # Create map with markers for each provider
+    m = folium.Map(location=user_location, zoom_start=12)
+
+    for index, row in filtered_providers.iterrows():
+        folium.Marker([row['Latitude'], row['Longitude']], popup=row['Name']).add_to(m)
+
+    # Display map to user
+    folium_static(m)
+
+
+import pandas as pd
+
+# Define column names for the providers
+columns = ['Name', 'Address', 'City', 'State', 'Zipcode', 'Phone', 'Website']
+
+# Define the list of providers as a list of lists
+providers = [
+    ['Minnesota Community Care', '570 University Avenue', 'Saint Paul', 'MN', '55103', '(651) 602-7500', 'https://mncc.org/'],
+    ['Hennepin Healthcare', '701 Park Avenue', 'Minneapolis', 'MN', '55415', '(612) 873-3000', 'https://www.hennepinhealthcare.org/'],
+    ['Allina Health', '800 East 28th Street', 'Minneapolis', 'MN', '55407', '(612) 863-4000', 'https://www.allinahealth.org/'],
+    ['Fairview Health Services', '2450 Riverside Avenue', 'Minneapolis', 'MN', '55454', '(612) 672-7000', 'https://www.fairview.org/'],
+    ['HealthPartners', '8170 33rd Avenue South', 'Bloomington', 'MN', '55425', '(952) 883-6000', 'https://www.healthpartners.com/'],
+    ['Mayo Clinic Health System', '1216 Second Street Southwest', 'Rochester', 'MN', '55902', '(507) 266-7890', 'https://www.mayoclinic.org/'],
+]
+
+# Create a DataFrame from the providers list
+df = pd.DataFrame(providers, columns=columns)
+
+# Save the DataFrame as a CSV file
+df.to_csv('minnesota_providers.csv', index=False)
+
+