Spaces:
Sleeping
Sleeping
| 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(os.environ("OPENAI_API_KEY")) | |
| 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}"} | |
| ] | |
| ) | |
| 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_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 | |
| def form_dialog(): | |
| city = st.selectbox("City", ['San Francisco', 'Oakland', 'Berkeley']) | |
| zipcode = st.text_input("Zipcode") | |
| 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)") | |
| 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'] != "": | |
| # services_scores = shelters.apply(lambda row: call_gpt(data['Needs'], row['Services']), axis=1).tolist() | |
| # services_scores = [s / 10 for s in services_scores] | |
| # shelters['services_score'] = 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() |