app.py

import streamlit as st
from time import sleep
import json
from pymongo import MongoClient
from bson import ObjectId
from openai import OpenAI
openai_client = OpenAI()
import os

# Get the restaurants based on the search and location
def get_restaurants(search, location, meters):
    try:
        uri = os.environ.get('MONGODB_ATLAS_URI')
        client = MongoClient(uri)
        db_name = 'whatscooking'
        collection_name = 'restaurants'
        restaurants_collection = client[db_name][collection_name]
        trips_collection = client[db_name]['smart_trips']
    except:
        st.error("Error Connecting to the MongoDB Atlas Cluster")
        return None, None, None, None

    try:
        newTrip, pre_agg = pre_aggregate_meters(restaurants_collection, location, meters)

        response = openai_client.embeddings.create(
            input=search,
            model="text-embedding-3-small",
            dimensions=256
        )

        vectorQuery = {
            "$vectorSearch": {
                "index": "vector_index",
                "queryVector": response.data[0].embedding,
                "path": "embedding",
                "numCandidates": 10,
                "limit": 3,
                "filter": {"searchTrip": newTrip}
            }
        }

        restaurant_docs = list(trips_collection.aggregate([vectorQuery, {"$project": {"_id": 0, "embedding": 0}}]))

        chat_response = openai_client.chat.completions.create(
            model="gpt-4o",
            messages=[
                {"role": "system", "content": "You are a helpful restaurant assistant. Answer shortly and quickly. You will get a context if the context is not relevant to the user query please address that and not provide by default the restaurants as is."},
                {"role": "user", "content": f"Find me the 2 best restaurant and why based on {search} and {restaurant_docs}. Shortly explain trades offs and why I should go to each one. You can mention the third option as a possible alternative in one sentence."}
            ]
        )

        trips_collection.delete_many({"searchTrip": newTrip})

        if len(restaurant_docs) == 0:
            return "No restaurants found", '<iframe style="background: #FFFFFF;border: none;border-radius: 2px;box-shadow: 0 2px 10px 0 rgba(70, 76, 79, .2);" width="640" height="480" src="https://charts.mongodb.com/charts-paveldev-wiumf/embed/charts?id=65c24b0c-2215-4e6f-829c-f484dfd8a90c&filter={\'restaurant_id\':\'\'}&maxDataAge=3600&theme=light&autoRefresh=true"></iframe>', str(pre_agg), str(vectorQuery)

        first_restaurant = restaurant_docs[0]['restaurant_id']
        second_restaurant = restaurant_docs[1]['restaurant_id']
        third_restaurant = restaurant_docs[2]['restaurant_id']
        restaurant_string = f"'{first_restaurant}', '{second_restaurant}', '{third_restaurant}'"

        iframe = '<iframe style="background: #FFFFFF;border: none;border-radius: 2px;box-shadow: 0 2px 10px 0 rgba(70, 76, 79, .2);" width="640" height="480" src="https://charts.mongodb.com/charts-paveldev-wiumf/embed/charts?id=65c24b0c-2215-4e6f-829c-f484dfd8a90c&filter={\'restaurant_id\':{$in:[' + restaurant_string + ']}}&maxDataAge=3600&theme=light&autoRefresh=true"></iframe>'
        client.close()
        return chat_response.choices[0].message.content, iframe, str(pre_agg), str(vectorQuery)
    except Exception as e:
        st.error(f"Your query caused an error: {e}")
        return "Your query caused an error, please retry with allowed input only ...", '<iframe style="background: #FFFFFF;border: none;border-radius: 2px;box-shadow: 0 2px 10px 0 rgba(70, 76, 79, .2);" width="640" height="480" src="https://charts.mongodb.com/charts-paveldev-wiumf/embed/charts?id=65c24b0c-2215-4e6f-829c-f484dfd8a90c&filter={\'restaurant_id\':\'\'}&maxDataAge=3600&theme=light&autoRefresh=true"></iframe>', str(pre_agg), str(vectorQuery)

def pre_aggregate_meters(restaurants_collection, location, meters):
    tripId = ObjectId()
    pre_aggregate_pipeline = [{
        "$geoNear": {
            "near": location,
            "distanceField": "distance",
            "maxDistance": meters,
            "spherical": True,
        },
    }, {
        "$addFields": {
            "searchTrip": tripId,
            "date": tripId.generation_time
        }
    }, {
        "$merge": {
            "into": "smart_trips"
        }
    }]

    result = restaurants_collection.aggregate(pre_aggregate_pipeline)
    sleep(3)
    return tripId, pre_aggregate_pipeline

st.markdown(
    """
    # MongoDB's Vector Restaurant Planner 
    Start typing below to see the results. You can search a specific cuisine for you and choose 3 predefined locations.

    The radius specifies the distance from the start search location. This space uses the dataset called [whatscooking.restaurants](https://huggingface.co/datasets/AIatMongoDB/whatscooking.restaurants)
    """
)

search = st.text_input("What type of dinner are you looking for?")
location = st.radio("Location", options=[
    {"label": "Timesquare Manhattan", "value": {"type": "Point", "coordinates": [-73.98527039999999, 40.7589099]}},
    {"label": "Westside Manhattan", "value": {"type": "Point", "coordinates": [-74.013686, 40.701975]}},
    {"label": "Downtown Manhattan", "value": {"type": "Point", "coordinates": [-74.000468, 40.720777]}}
], format_func=lambda x: x['label'])
meters = st.slider("Radius in meters", min_value=500, max_value=10000, step=5)

if st.button("Get Restaurants"):
    location_value = location['value']
    result, iframe, pre_agg, vectorQuery = get_restaurants(search, location_value, meters)
    if result:
        st.markdown(result)
        st.markdown(iframe, unsafe_allow_html=True)
        st.json(pre_agg)
        st.json(vectorQuery)