Top 23 Python Streamlit Projects

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• streamlit

  283 34,542 9.8 Python

  Streamlit — A faster way to build and share data apps.

  

  Project mention: Build a Route Generator App with Cloudflare Workers AI, LangChain, Streamlit, and Mapbox | dev.to | 2024-09-10

  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(""" .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 */ } """, unsafe_allow_html=True) # Footer HTML footer_html = """ "footer"> Made with ❤️ in SF🌉 with Cloudflare Workers AI ➡️ 👩🏻‍💻 "https://github.com/elizabethsiegle/bike_walk_route_map_generator">code here on GitHub """ # 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('

• Scout Monitoring

  www.scoutapm.com featured

  Free Django app performance insights with Scout Monitoring. Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.

  

• geemap

  17 3,396 9.2 Python

  A Python package for interactive geospatial analysis and visualization with Google Earth Engine.

  

• leafmap

  5 3,166 9.5 Python

  A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment

  

  Project mention: FLaNK Stack for 25 September 2023 | dev.to | 2023-09-25

• opyrator

  8 3,054 0.0 Python

  🪄 Turns your machine learning code into microservices with web API, interactive GUI, and more.

  

  Project mention: Opyrator: Turns your Python code into microservices with web API and webUI | news.ycombinator.com | 2023-10-30

• Windrecorder

  1 2,868 9.4 Python

  Windrecorder is a memory search app by records everything on your screen in small size, to let you rewind what you have seen, query through OCR text or image description, and get activity statistics. (by yuka-friends)

  

  Project mention: Windrecorder – Personal Memory Search Engine | news.ycombinator.com | 2024-05-31

• prettymapp

  6 2,313 7.3 Python

  🖼️ Create beautiful maps from OpenStreetMap data in a streamlit webapp

• DemoGPT

  31 1,672 4.0 Python

  Create 🦜️🔗 LangChain apps by just using prompts🌟 Star to support our work! | 只需使用句子即可创建 LangChain 应用程序。 给个star支持我们的工作吧！

• InfluxDB

  www.influxdata.com featured

  Purpose built for real-time analytics at any scale. InfluxDB Platform is powered by columnar analytics, optimized for cost-efficient storage, and built with open data standards.

  

• Streamlit-Authenticator

  2 1,524 8.7 Python

  A secure authentication module to manage user access in a Streamlit application.

• streamlit-webrtc

  4 1,358 8.7 Python

  Real-time video and audio streams over the network, with Streamlit.

• traingenerator

  2 1,357 0.0 Python

  🧙 A web app to generate template code for machine learning

• streamlit-aggrid

  3 1,035 8.2 Python

  Implementation of Ag-Grid component for Streamlit

• streamlit-geospatial

  8 858 5.4 Python

  A multi-page streamlit app for geospatial

  

• nlu

  25 855 8.4 Python

  1 line for thousands of State of The Art NLP models in hundreds of languages The fastest and most accurate way to solve text problems.

  

• energy-forecasting

  1 850 8.5 Python

  🌀 𝗧𝗵𝗲 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝟳-𝗦𝘁𝗲𝗽𝘀 𝗠𝗟𝗢𝗽𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 | 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟𝗘 & 𝗠𝗟𝗢𝗽𝘀 for free by designing, building and deploying an end-to-end ML batch system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 2.5 𝘩𝘰𝘶𝘳𝘴 𝘰𝘧 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 & 𝘷𝘪𝘥𝘦𝘰 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴

  Project mention: Full Stack 7-Steps MLOps Framework – Learn ML and MLOps by Building an ML System | news.ycombinator.com | 2023-09-26

• nn-template

  4 631 7.2 Python

  Generic template to bootstrap your PyTorch project.

  

• barfi

  2 629 0.0 Python

  Python Flow Based Programming environment that provides a graphical programming environment.

• learn-generative-ai

  1 629 9.7 Python

  Learn Cloud Applied Generative AI Engineering (GenEng) using OpenAI, Gemini, Streamlit, Containers, Serverless, Postgres, LangChain, Pinecone, and Next.js

  

  Project mention: Top Courses and GitHub Repositories to Learn GenerativeAI Free | dev.to | 2024-08-17

  ✅Learn-generative-ai

• streamlit-folium

  2 460 7.5 Python

  Streamlit Component for rendering Folium maps

  Project mention: Build a Route Generator App with Cloudflare Workers AI, LangChain, Streamlit, and Mapbox | dev.to | 2024-09-10

  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(""" .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 */ } """, unsafe_allow_html=True) # Footer HTML footer_html = """ "footer"> Made with ❤️ in SF🌉 with Cloudflare Workers AI ➡️ 👩🏻‍💻 "https://github.com/elizabethsiegle/bike_walk_route_map_generator">code here on GitHub """ # 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('

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