In this tutorial, you will learn how to create a web app that generates optimized tourist routes using Cloudflare Workers AI, Mapbox, LangChain, and Streamlit. The app allows users to select landmarks in a chosen city and get the shortest route between them, solving the Traveling Salesman Problem.
Set Up Your Dev Environment and Environment Variables
On the command line, make a new folder called tsp.py. Create a virtual environment by running
python3 -m venv venv
source venv/bin/activate
Next, install the required libraries with
pip install python-dotenv geopy langchain langchain-core langchain-community markdown pandas requests streamlit streamlit-searchbox folium streamlit-folium
You need a Mapbox API token--you can get one here and a Cloudflare Workers AI token, which you get by clicking AI on the lefthand side of your dashboard, followed by clicking the blue Use REST API button then the blue Create a Workers AI API Token button.
You can find your Cloudflare Account SID on that same page or in the dashboard URL--it's the string of characters following dash.cloudflare.com/.
Make a .env file and replace those values below:
MAPBOX_TOKEN=
CLOUDFLARE_API_TOKEN=
CLOUDFLARE_ACCOUNT_ID=
The app uses environment variables for secure access to APIs like Mapbox and Cloudflare Workers AI. They are accessed in Python code using dotenv and os (and would be replaced by lines like mapbox_token = st.secrets["MAPBOX_TOKEN"] if you were to deploy to Streamlit.
At the top, include the following import statements and variables to reference the environment variables:
from dotenv import load_dotenv
import folium
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms.cloudflare_workersai import CloudflareWorkersAI
import os
import requests
import streamlit as st
from streamlit_folium import folium_static
load_dotenv()
mapbox_token = os.getenv('MAPBOX_TOKEN')
cf_account_id = os.getenv('CLOUDFLARE_ACCOUNT_ID')
cf_api_token = os.getenv('CLOUDFLARE_API_TOKEN')
Create User Interface with Streamlit
The UI is created using Streamlit. Add the title and description with the code below:
st.title('Route Me🚴♀️🚶♀️➡️🏃♀️')
st.markdown("""
This app uses Cloudflare Workers AI, LangChain, and Mapbox to solve the Traveling Salesman problem.
1. Enter a city🏙️
2. Pick landmarks🌁🗽
3. Generate the shortest path.
4. Explore! 🗺️
""")
We also add a footer for aesthetic and branding purposes:
footer_html = """
<div class="footer">
Made with ❤️ in SF🌉 with Cloudflare Workers AI ➡️ 👩🏻💻
<a href="https://github.com/your-repo">code here on GitHub</a>
</div>
"""
st.markdown(footer_html, unsafe_allow_html=True)
Fetch Cities and Landmarks
We use Mapbox's searchbox API to fetch information about the input city and corresponding landmark information. The app provides a list of cities and landmarks according to the user input.
The following code searches for cities:
def find_city(city_inp: str):
url = "https://api.mapbox.com/search/searchbox/v1/suggest"
params = {"q": city_inp, "access_token": mapbox_token, "types": "place"}
res = requests.get(url, params=params)
# Extract suggestions
suggestions = res.json().get('suggestions', [])
return [(f"{s['name']}, {s['place_formatted']}", s['mapbox_id']) for s in suggestions]
This function retrieves details about the input city:
def retrieve_city(city_id: str):
url = f"https://api.mapbox.com/search/searchbox/v1/retrieve/{city_id}"
params = {"access_token": mapbox_token}
res = requests.get(url, params=params)
return res.json().get('features', [])[0] # Get first result
This function retrieves landmarks:
def retrieve_landmark(name: str, proximity: str):
url = "https://api.mapbox.com/search/searchbox/v1/forward"
params = {
"q": name,
"access_token": mapbox_token,
"proximity": proximity,
'types': 'poi'
}
res = requests.get(url, params=params)
return res.json()['features'][0]
Use Cloudflare Workers AI and LangChain for Landmarks
We use Cloudflare Workers AI to generate a list of landmarks. This is done through LangChain’s prompt chain.
def lmchain():
llm = CloudflareWorkersAI(account_id=cf_account_id, api_token=cf_api_token, model='@cf/meta/llama-3.1-8b-instruct')
prompt = PromptTemplate(
template="Return a list of 7 landmarks in {city}.",
input_variables=["city"]
)
return LLMChain(llm=llm, prompt=prompt)
Traveling Salesman Problem and Optimized Routes
Ultimately, this app solves the Traveling Salesman problem by utilizing Mapbox's optimized-trips API. It calculates the shortest route between landmarks selected by the user.
def travelingsalesman(landmarks_df):
coordinates = ";".join([f"{row['longitude']},{row['latitude']}" for _, row in landmarks_df.iterrows()])
url = f"https://api.mapbox.com/optimized-trips/v1/mapbox/cycling/{coordinates}"
params = {"access_token": mapbox_token, "geometries": "geojson"}
res = requests.get(url, params=params)
return res.json()
Display the Map
We use Folium to generate the map and display it in the Streamlit app with the streamlit-folium library.
def create_route_map(landmarks, optimized_coords):
m = folium.Map(location=[landmarks['latitude'].mean(), landmarks['longitude'].mean()], zoom_start=12)
folium.PolyLine(optimized_coords, color='red').add_to(m)
return folium_static(m)
Generate Textual Route Description
The LLM generates a textual description of the optimized bike/walk/run route:
def make_route(city, landmarks):
chain = lmchain()
return chain.run({"city": city, "landmarks": "\n".join(landmarks['Name'])})
Download Map and Route Options
The app allows users to download the route map and description:
st.download_button(label='Download Route Map', data=map_to_html(st.session_state.route_map), file_name='route_map.html')
st.download_button(label='Download Route Description', data=route_html, file_name='route_description.html')
The complete Python file should look like this:
from dotenv import load_dotenv
from geopy import distance
import io
from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain.output_parsers import CommaSeparatedListOutputParser
from langchain_community.llms.cloudflare_workersai import CloudflareWorkersAI
import markdown
import os
import pandas as pd
import requests
import streamlit as st
from streamlit_searchbox import st_searchbox
from typing import List
import uuid
import folium
from folium.plugins import Draw
from streamlit_folium import folium_static
# Display the title and description
st.title('Route Me🚴♀️🚶♀️➡️🏃♀️')
st.markdown("""
<style>
.footer {
position: fixed;
bottom: 0;
left: 0;
width: 100%;
background-color: #f1f1f1;
text-align: center;
padding: 10px 0;
color: #000;
font-size: 14px;
box-shadow: 0 -2px 5px rgba(0,0,0,0.1);
}
.content {
padding-bottom: 150px; /* Adjust this value to create space between content and footer */
}
</style>
""", unsafe_allow_html=True)
# Footer HTML
footer_html = """
<div class="footer">
Made with ❤️ in SF🌉 with Cloudflare Workers AI ➡️ 👩🏻💻 <a href="https://github.com/elizabethsiegle/bike_walk_route_map_generator">code here on GitHub</a>
</div>
"""
# Inject the footer into the app
st.markdown(footer_html, unsafe_allow_html=True)
# Define markdown content directly
markdown_content = """
This app uses [Cloudflare Workers AI](https://developers.cloudflare.com/workers-ai/), [LangChain](https://langchain.dev/), landmark/city data from Mapbox, [Folium](https://python-visualization.github.io/folium/latest/) for visualizing maps and routes, and [Streamlit](https://streamlit.io/)/[Streamlit Folium](https://folium.streamlit.app/) to tackle the Traveling Salesman problem!
1. Enter a city🏙️ you wish to visit
-> get a few must-visit landmarks in your chosen city
2. Pick the landmarks🌁🗽 you want to visit.
3. Generate the shortest path between these landmarks.
4. Explore! 🗺️
"""
st.markdown(markdown_content)
# Load environment variables
load_dotenv()
mapbox_token = st.secrets["MAPBOX_TOKEN"] # os.environ.get('MAPBOX_TOKEN')
cf_account_id = st.secrets["CLOUDFLARE_ACCOUNT_ID"]# os.environ.get('CLOUDFLARE_ACCOUNT_ID')
cf_api_token = st.secrets["CLOUDFLARE_API_TOKEN"] # os.environ.get('CLOUDFLARE_API_TOKEN')
token = str(uuid.uuid4())
# find cities
def find_city(city_inp: str) -> List[tuple]:
if len(city_inp) < 3:
return []
# searchbox api = return list of suggestions for city
url = "https://api.mapbox.com/search/searchbox/v1/suggest"
params = {"q": city_inp, "access_token": mapbox_token, "session_token": token, "types": "place"}
res = requests.get(url, params=params)
if res.status_code != 200:
return []
try:
suggestions = res.json().get('suggestions', [])
results = []
for s in suggestions:
print(f"s[name] {s['name']} s[place_formatted] {s['place_formatted']}")
results.append((f"{s['name']}, {s['place_formatted']}", s['mapbox_id']))
return results
except Exception as e:
st.error(f"Error fetching city suggestions: {e}")
return []
# Function to retrieve city details
@st.cache_data
def retrieve_city(id):
url = f"https://api.mapbox.com/search/searchbox/v1/retrieve/{id}"
params = {"access_token": mapbox_token,"session_token": token}
res = requests.get(url, params=params)
if res.status_code != 200:
return []
try:
features = res.json().get('features', [])
if not features:
st.warning("No features returned for the city.")
return []
return features[0]
except Exception as e:
st.error(f"An error occurred: {e}")
return []
# Function to retrieve landmark details
@st.cache_data
def retrieve_landmark(name, proximity):
mapbox_url = "https://api.mapbox.com/search/searchbox/v1/forward"
params = {"access_token": mapbox_token, "q": name, "proximity": proximity, 'types': 'poi', 'poi_category': 'tourist_attraction,museum,monument,historic,park,church,place_of_worship'}
res = requests.get(mapbox_url, params=params)
print(f'res.json {res.json()}')
if res.status_code != 200:
return []
try:
return res.json()['features'][0]
except Exception as e:
print(f"Error retrieving landmark: {e}")
return []
# find city w/ searchbox
city_id = st_searchbox(find_city, key="city")
# Function to make landmark chain
@st.cache_resource
def lmchain():
outp_parser = CommaSeparatedListOutputParser()
form_instructions = outp_parser.get_format_instructions()
llm = CloudflareWorkersAI(account_id=cf_account_id, api_token=cf_api_token, model='@cf/meta/llama-3.1-8b-instruct',)
prompt = PromptTemplate(
template="""Return a comma-separated list of the 7 best landmarks in {city}. Only return the list. {form_instructions}""",
input_variables=["city"],
partial_variables={"form_instructions": form_instructions},
)
chain = LLMChain(llm=llm, prompt=prompt, output_parser=outp_parser)
return chain
# Function to get landmark locations
@st.cache_data
def get_landmarks(landmarks, long_city, lat_city):
data = []
for lm in landmarks:
features = retrieve_landmark(lm, f"{long_city},{lat_city}")
if not features:
continue
coor = features['geometry']['coordinates']
long, lat = coor
dist = distance.distance((lat_city, long_city), (lat, long)).km
if dist <= 7:
data.append([lm, long, lat, True])
return pd.DataFrame(data=data, columns=['Name', 'longitude', 'latitude', 'Include'])
@st.cache_data
def travelingsalesman(chosen_landmarks):
profile = "mapbox/cycling"
coordinates = ";".join([f"{row['longitude']},{row['latitude']}" for _, row in chosen_landmarks.iterrows()])
# optimized trips API -> optimized route to hit all landmarks
url = f"https://api.mapbox.com/optimized-trips/v1/{profile}/{coordinates}"
params = {"access_token": mapbox_token, "geometries": "geojson"} # Request GeoJSON format
res = requests.get(url, params=params)
if res.status_code != 200:
st.error(f"Error: API request failed with status code {res.status_code}")
return None, []
try:
json_response = res.json()
if 'trips' not in json_response or not json_response['trips']:
st.error("Error: No trips found in the API response")
return None, []
trip = json_response['trips'][0]
if 'geometry' not in trip:
st.error("Error: No geometry found in the trip data")
return None, []
geometry = trip['geometry']
if isinstance(geometry, dict) and 'coordinates' in geometry:
optimized_coords = [(coord[1], coord[0]) for coord in geometry['coordinates']]
return json_response, optimized_coords
else:
st.error("Error: Unexpected geometry format in the API response")
return None, []
except Exception as e:
st.error(f"Error in travelingsalesman: {str(e)}")
st.write("JSON Response:", json_response) # Debug: Print the JSON response
return None, []
def create_route_map(landmarks, optimized_coords):
# Create a map centered on the mean of all coordinates
all_lats = landmarks['latitude'].tolist() + [coord[0] for coord in optimized_coords]
all_lons = landmarks['longitude'].tolist() + [coord[1] for coord in optimized_coords]
center_lat = sum(all_lats) / len(all_lats)
center_lon = sum(all_lons) / len(all_lons)
m = folium.Map(location=[center_lat, center_lon], zoom_start=12)
Draw(export=True).add_to(m)
# Add markers for each landmark
for _, row in landmarks.iterrows():
folium.Marker(
[row['latitude'], row['longitude']],
popup=row['Name']
).add_to(m)
# Add the optimized route line if coordinates are available
if optimized_coords:
folium.PolyLine(
optimized_coords,
weight=6,
color='red',
opacity=0.8
).add_to(m)
else:
st.warning("No optimized route available. Displaying landmarks only.")
# Fit the map to the bounds of all coordinates
sw = min(all_lats), min(all_lons)
ne = max(all_lats), max(all_lons)
m.fit_bounds([sw, ne])
return m
# Function to create a bike route
@st.cache_data
def make_route(city, landmarks, _llm):
prompt = PromptTemplate(
template="""You are an experienced tour guide in {city}. You love telling more about landmarks in a short way. Create a bike route for {city} in markdown, using headings with ##, passing by the following landmarks: {landmarks}. End with the introduction of the next landmark {end}, as if it was the next destination, but don't discuss it.""",
input_variables=["landmarks", "city", "end"]
)
chain = LLMChain(llm=_llm, prompt=prompt)
landmarks_string = "\n".join([f"{row['Name']}" for _, row in landmarks.iloc[:5, :].iterrows()])
# Handle case where there are fewer than 6 landmarks
if len(landmarks) < 6:
part_one = chain.run({'city': city, 'landmarks': landmarks_string, 'end': ""})
return part_one
else:
part_one = chain.run({'city': city, 'landmarks': landmarks_string, 'end': landmarks.iloc[5]['Name']})
prompt = PromptTemplate(
template="""You are an experienced tour guide in {city}. You love telling more about landmarks in a short way. Create a bike route for {city} in markdown, using headings with ##, passing by the following landmarks: {landmarks}. Start your explanation with 'Continuing from {previous}'.""",
input_variables=["landmarks", "city", "previous"]
)
chain = LLMChain(llm=_llm, prompt=prompt)
landmarks_string = "\n".join([f"{row['Name']}" for _, row in landmarks.iloc[5:, :].iterrows()])
part_two = chain.run({'city': city, 'landmarks': landmarks_string, 'previous': landmarks.iloc[4]['Name']})
return part_one + " " + part_two
# Function to convert markdown data to HTML
@st.cache_data
def to_html(data, filename='route'):
return markdown.markdown(data)
# New function to convert Folium map to HTML string
def map_to_html(map_object):
map_html = io.BytesIO()
map_object.save(map_html, close_file=False)
return map_html.getvalue().decode()
if 'route' not in st.session_state:
st.session_state.route = None
# Function to generate a bike route
def gen_route(city, stops):
route = make_route(city['properties']['full_address'], stops, CloudflareWorkersAI(account_id=cf_account_id, api_token=cf_api_token))
st.session_state.route = route
def create_route_map(landmarks, optimized_coords):
# Create a map centered on the first landmark
m = folium.Map(location=[landmarks.iloc[0]['latitude'], landmarks.iloc[0]['longitude']], zoom_start=12)
# Add markers for each landmark
for _, row in landmarks.iterrows():
folium.Marker(
[row['latitude'], row['longitude']],
popup=row['Name'],
tooltip=row['Name']
).add_to(m)
# Add the optimized route line if coordinates are available
if optimized_coords:
folium.PolyLine(
optimized_coords,
weight=2,
color='red',
opacity=0.8
).add_to(m)
else:
st.warning("No optimized route available. Displaying landmarks only.")
return m
# Update the main app logic
if city_id:
city = retrieve_city(city_id)
if city:
coords = city['geometry']['coordinates']
long, lat = coords
landmarks = lmchain().run({"city": city['properties']['full_address']})
if 'landmark_locations' not in st.session_state:
st.session_state.landmark_locations = get_landmarks(landmarks, long, lat)
user_inp = st.data_editor(
st.session_state.landmark_locations,
hide_index=True,
disabled=('Name', 'longitude', 'latitude'),
column_config={'longitude': None, 'latitude': None},
key='user_input',
use_container_width=True
)
st.session_state.landmark_locations.update(user_inp)
selected_landmarks = st.session_state.landmark_locations[st.session_state.landmark_locations['Include']]
output, optimized_coords = travelingsalesman(selected_landmarks)
if output is not None and optimized_coords:
dist = output['trips'][0]['distance']
conv_fac = 0.000621371
miles = dist * conv_fac
st.write(f"Total distance: {miles:.3f} mi")
waypoints = [wp['waypoint_index'] for wp in output['waypoints']]
stops = selected_landmarks.iloc[waypoints, :]
# Store the route map in session state
st.session_state.route_map = create_route_map(stops, optimized_coords)
# Display the route map
folium_static(st.session_state.route_map)
st.button('Generate route!', on_click=lambda: gen_route(city, stops))
else:
st.error("Unable to generate the optimized route. Please try again or select different landmarks.")
# Show generated route and offer map download
if 'route' in st.session_state and st.session_state.route:
route = st.session_state.route
st.markdown(route)
# Offer route map download if available
if 'route_map' in st.session_state:
map_html = map_to_html(st.session_state.route_map)
st.download_button(
label='Download Route Map',
data=map_html,
file_name='route_map.html',
mime='text/html'
)
# Offer route description download
route_html = to_html(route)
st.download_button(
label='Download the route description!',
data=route_html,
file_name='cf-workers-ai-tourist-route.html',
mime='text/html'
)
st.markdown('</div>', unsafe_allow_html=True)
# Footer HTML
footer_html = """
<div class="footer">
Made with ❤️ in SF🌉
</div>
"""
The [complete code can be found here on GitHub](https://github.com/elizabethsiegle/bike_walk_route_map_generator).
# Inject the footer into the app
st.markdown(footer_html, unsafe_allow_html=True)
Lastly, run the file with streamlit run tsp.py!
app gif
So much fun is to be had for developers with Mapbox, LangChain, Cloudflare, and Streamlit. Let me know online what you're building!
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