import streamlit as st import json import pandas as pd import requests import os import math from openai import OpenAI def call_gpt(user_needs, shelter_services): client = OpenAI() completion = client.chat.completions.create( model="gpt-4o-mini", messages=[ {"role": "system", "content": "Given two variables 'user needs' (the ideal qualities/services of a shelter) and 'shelter services' (the services offered by a shelter), return an integer 0-10 that scores how well the 'shelter services' match the 'user needs' where 0 is the best fit and 10 is the worst fit. IMPORTANT: NO MATTER WHAT, ONLY RETURN THE INTEGER (NO EXTRA WORDS, PUNCTUATION, ETC.)"}, {"role": "user", "content": f"user_needs: {user_needs}, shelter_services: {shelter_services}"} ] ) print(completion, type(completion)) print(completion.choices[0], type(completion.choices[0])) score = completion.choices[0].message.content.strip() return int(score) def get_urgency_score(user, shelter): if user == "Today": if shelter == "Immidiate": return 0 if shelter == "High": return 0.75 if shelter == "Moderate": return 1 elif user == "In the next few days": if shelter == "Immidiate": return 0.25 if shelter == "High": return 0 if shelter == "Moderate": return 0.75 elif user == "In a week or so": if shelter == "Immidiate": return 0.75 if shelter == "High": return 0.25 if shelter == "Moderate": return 0 def get_duration_score(user, shelter): if user == "Overnight": if shelter == "Overnight": return 0 if shelter == "Temporary": return 0.5 if shelter == "Transitional": return 0.75 if shelter == "Long-Term": return 1 elif user == "A month or less": if shelter == "Overnight": return 0.5 if shelter == "Temporary": return 0 if shelter == "Transitional": return 0.25 if shelter == "Long-Term": return 0.75 elif user == "A couple of months": if shelter == "Overnight": return 0.75 if shelter == "Temporary": return 0.25 if shelter == "Transitional": return 0 if shelter == "Long-Term": return 0.5 elif user == "A year or more": if shelter == "Overnight": return 1 if shelter == "Temporary": return 0.75 if shelter == "Transitional": return 0.5 if shelter == "Long-Term": return 0 def get_zip_codes(city, state): url = f'http://api.zippopotam.us/us/{state}/{city}' response = requests.get(url) if response.status_code == 200: data = response.json() return [place['post code'] for place in data['places']] else: return [] def get_coordinates(zipcode: str, api_key: str) -> list: """ Get the coordinates (latitude and longitude) of an address using the OpenWeather Geocoding API. Parameters: zipcode (str): The zipcode to geocode. api_key (str): Your OpenWeather API key. Returns: list: A list containing the latitude and longitude of the address. """ base_url = "http://api.openweathermap.org/geo/1.0/zip" params = { 'zip': str(zipcode) + ",US", 'appid': api_key } response = requests.get(base_url, params=params) data = response.json() return [data.get('lat'), data.get('lon')] def haversine(lat1, lon1, lat2, lon2): R = 6371 # Earth radius in kilometers. Use 3956 for miles. dlat = math.radians(lat2 - lat1) dlon = math.radians(lon2 - lon1) a = math.sin(dlat / 2) ** 2 + math.cos(math.radians(lat1)) * math.cos(math.radians(lat2)) * math.sin(dlon / 2) ** 2 c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a)) distance = R * c return distance # Function to handle form submission @st.experimental_dialog("Fill out the form") def form_dialog(): city_zipcodes = { "San Francisco": get_zip_codes("San Francisco", "CA"), "Oakland": get_zip_codes("Oakland", "CA"), "Berkeley": get_zip_codes("Berkeley", "CA") } city = st.selectbox("City", list(city_zipcodes.keys())) zipcode = st.selectbox("Zipcode", ['Unsure'] + city_zipcodes[city]) sex = st.radio("Sex", ["Male", "Female", "Other"]) lgbtq = st.radio("Do you identify as LGBTQ+ (some shelters serve this community specifically)", ["No", "Yes"]) domestic_violence = st.radio("Have you experienced domestic violence (some shelters serve these individuals specifically", ["No", "Yes"]) urgency = st.radio("How quickly do you need help?", ("Today", "In the next few days", "In a week or more")) duration = st.radio("How long do you need a place to stay?", ("Overnight", "A month or less", "A couple of months", "A year or more")) needs = st.text_area("Optional - Needs (tell us what you need and how we can help)", value="") if st.button("Submit"): data = { "City": city, "Zip Code": zipcode, "Sex": sex, "LGBTQ": lgbtq, "Domestic Violence": domestic_violence, "Urgency": urgency, "Duration": duration, "Needs": needs } with open('data.json', 'w') as f: json.dump(data, f) st.session_state.form_submitted = True st.session_state.data = data st.rerun() # Initialize session state if 'form_submitted' not in st.session_state: st.session_state.form_submitted = False if 'shelter_index' not in st.session_state: st.session_state.shelter_index = 0 # Page config st.set_page_config( page_title="ShelterSearch", layout="wide", ) st.title("ShelterSearch") if not st.session_state.form_submitted: if st.button("Open Form"): form_dialog() else: with open('data.json', 'r') as f: data = json.load(f) st.json(data) shelters = pd.read_csv("database.csv") # filter city shelters = shelters[(shelters['City'] == data['City'])] # filter sex shelters = shelters[(shelters['Sex'] == data['Sex']) | (shelters['Sex'] == 'All')] # filter lgbtq if data['LGBTQ'] == 'No': shelters = shelters[(shelters['LGBTQ'] == "No")] # filter domestic violence if data['Domestic Violence'] == "No": shelters = shelters[(shelters['Domestic Violence'] == "No")] # calculate distances between zipcodes if data['Zip Code'] != "Unsure": geocoding_api_key = os.environ['OpenWeather_API_KEY'] shelters_coordinates = shelters.apply(lambda row: get_coordinates(row['Zip Code'], geocoding_api_key), axis=1).tolist() user_coordinates = get_coordinates(data['Zip Code'], geocoding_api_key) distances = [] for coordinates in shelters_coordinates: distances.append(haversine(coordinates[0], coordinates[1], user_coordinates[0], user_coordinates[1])) max = max(distances) if (max(distances) != 0) else 1 shelters['zipcode_score'] = [d / max for d in distances] # get urgency scores urgency_scores = shelters.apply(lambda row: get_urgency_score(data['Urgency'], row['Urgency']), axis=1).tolist() shelters['urgency_score'] = urgency_scores # get duration scores duration_scores = shelters.apply(lambda row: get_duration_score(data['Duration'], row['Duration']), axis=1).tolist() shelters['duration_score'] = duration_scores # services if data['Needs'] != "": openai_api_key = os.environ["OPENAI_API_KEY"] services_scores = shelters.apply(lambda row: call_gpt(data['Needs'], row['Services']), axis=1).tolist() services_scores = [s / 10 for s in integer_services_scores] shelters['services_score'] = scaled_services_scores st.table(shelters) shelters = [ {"title": "Shelter 1", "description": "This is the 1st shelter",}, {"title": "Shelter 2", "description": "This is the 2nd shelter.",}, {"title": "Shelter 3", "description": "This is the 3rd shelter.",} ] # Display the current shelter information shelter = shelters[st.session_state.shelter_index] st.write(shelter["description"]) # Create two columns col1, col2 = st.columns([1,1]) # Add buttons to each column with col1: if st.button("Previous"): if st.session_state.shelter_index > 0: st.session_state.shelter_index -= 1 st.experimental_rerun() with col2: if st.button("Next"): if st.session_state.shelter_index < len(shelters) - 1: st.session_state.shelter_index += 1 st.experimental_rerun()