AI Coffee Shop Streamlit App

README.md

This simple streamlit app uses the Google Maps and Places API, along with a hosted Nvidia NIM wrapper of the Llama model, to help you find coffee shops near an address.

To run

1. Install dependencies

uv pip install streamlit streamlit-folium langchain_nvidia_ai_endpoints

2. Get a Google Maps API key

3. Get a Nvidia API Key

4. Create a streamlit config file at .streamlit/config.toml with the contents of config.toml

5. streamlit run app.py

app.py

import streamlit as st
import requests
from streamlit_folium import st_folium
import folium
from langchain_nvidia_ai_endpoints import ChatNVIDIA

# Google Maps API key with Places scope
GOOGLE_MAPS_API_KEY = ""

# https://build.nvidia.com/
NVIDIA_API_KEY = ""


llm = ChatNVIDIA(api_key=NVIDIA_API_KEY, model="meta/llama-3.3-70b-instruct")


def get_google_reviews(place_id):
    """Fetches reviews for a shop using its Place ID."""
    url = f"https://maps.googleapis.com/maps/api/place/details/json?place_id={place_id}&fields=reviews,url&key={GOOGLE_MAPS_API_KEY}"
    response = requests.get(url)
    data = response.json()

    if data["status"] == "OK":
        reviews = data["result"].get("reviews", [])
        return reviews
    else:
        print("No reviews found for the given place ID.")
        return [], None


def get_coffee_shop_review_text(place_id, shop):
    reviews = get_google_reviews(place_id)
    reviews_text = [r["text"] for r in reviews]
    return f"\n\n {shop.title()} REVIEWS: \n \n".join(reviews_text)


def get_coordinates(address):
    url = f"https://maps.googleapis.com/maps/api/geocode/json?address={address}&key={GOOGLE_MAPS_API_KEY}"
    response = requests.get(url)
    data = response.json()

    if data["status"] == "OK":
        location = data["results"][0]["geometry"]["location"]
        return location["lat"], location["lng"]
    else:
        st.error("Unable to get coordinates for the provided address.")
        return None, None


def find_nearby_coffee_shops(lat, lng):
    url = f"https://maps.googleapis.com/maps/api/place/nearbysearch/json?location={lat},{lng}&radius=1500&type=cafe&key={GOOGLE_MAPS_API_KEY}"
    response = requests.get(url)
    data = response.json()

    if data["status"] == "OK":
        coffee_shops = []
        for place in data["results"][:3]:  # Limit to 3 shops
            coffee_shops.append(
                {
                    "name": place["name"],
                    "lat": place["geometry"]["location"]["lat"],
                    "lng": place["geometry"]["location"]["lng"],
                    "placeid": place["place_id"],
                }
            )
        return coffee_shops
    else:
        st.error("Unable to find nearby coffee shops.")
        return []


def ask_llm(reviews):
    prompt = f""" 
        Please recommend a coffee shop based on the following reviews. 
        
        REVIEWS

        {reviews}

        Format your response as 
        Recommendation: 
        Alternatives Considered:

        In the recommendation include a brief reason why. For each alternative include a brief reason why not.
    """
    response = llm.invoke(prompt)
    return response.content


# Streamlit App
st.set_page_config(layout="wide")
if "clicked" not in st.session_state:
    st.session_state.clicked = False


def lookup():
    st.session_state.clicked = True


st.title("Find Your Perfect Coffee Spot")
col1, col2 = st.columns(2)

with col1:
    # User Input
    address = st.text_input("Enter your address")
    st.button("Locate Coffee Shops", on_click=lookup)

    if st.session_state.clicked:
        if address:
            lat, lng = get_coordinates(address)

            if lat and lng:
                coffee_shops = find_nearby_coffee_shops(lat, lng)

                # Display map
                map_ = folium.Map(location=[lat, lng], zoom_start=15)
                folium.Marker(
                    [lat, lng], popup="Your Location", icon=folium.Icon(color="blue")
                ).add_to(map_)

            for shop in coffee_shops:
                folium.Marker(
                    [shop["lat"], shop["lng"]],
                    popup=shop["name"],
                    icon=folium.Icon(color="green"),
                ).add_to(map_)

            st_folium(map_, width=700, height=500)


with col2:
    if st.session_state.clicked:
        st.subheader("Nearby Coffee Shop Reviews & AI Recommendation")
        review_texts = [
            get_coffee_shop_review_text(shop["placeid"], shop["name"])
            for shop in coffee_shops
        ]
        review_text = "\n\n".join(review_texts)

        tab1, tab2 = st.tabs(["Recommendation", "Raw Reviews"])

        with tab1:
            st.text(ask_llm(review_text))
        with tab2:
            st.text(review_text)

    else:
        st.error("Please enter an address.")

# used for debugging        
if __name__ == "__main__":
    address = "1001 soda creek rd evergreen co 80439"
    lat, lng = get_coordinates(address)
    coffee_shops = find_nearby_coffee_shops(lat, lng)
    review_texts = [
        get_coffee_shop_review_text(shop["placeid"], shop["name"])
        for shop in coffee_shops
    ]

config.toml

[theme]
primaryColor="#2F2F2F"
backgroundColor="#8B5E3C"
secondaryBackgroundColor="#D1B29A"
textColor="#E6E6E6"
font="sans serif"