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import gradio as gr
from time import sleep
import json
from pymongo import MongoClient
from bson import ObjectId
from openai import OpenAI
openai_client = OpenAI()
import os
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']
def get_restaurants(search, location, meters):
newTrip, pre_agg = pre_aggregate_meters(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-3.5-turbo",
messages=[
{"role": "system", "content": "You are a helpful restaurant assistant."},
{ "role": "user", "content": f"Find me the 2 best restaurant and why based on {search} and {restaurant_docs}. explain trades offs and why I should go to each one. You can mention the third option as a possible alternative."}
]
)
trips_collection.delete_many({"searchTrip": newTrip})
first_restaurant = restaurant_docs[0]['restaurant_id']
second_restaurant = restaurant_docs[1]['restaurant_id']
third_restaurant = restaurant_docs[2]['restaurant_id']
if (first_restaurant or second_restaurant or third_restaurant) is None:
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>'
else:
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\':{$in:[' + restaurant_string + ']}}&maxDataAge=3600&theme=light&autoRefresh=true"></iframe>'
return chat_response.choices[0].message.content, iframe,str(pre_agg), str(vectorQuery)
def pre_aggregate_meters(location, meters):
tripId = ObjectId()
pre_aggregate_pipeline = [{
"$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);
print(trips_collection.count_documents({"searchTrip": tripId}));
sleep(1)
return tripId, pre_aggregate_pipeline
with gr.Blocks() as demo:
gr.Markdown(
"""
# MongoDB's Vector Restaurant planner
Start typing below to see the results
""")
#gr.HTML(value='<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&maxDataAge=3600&theme=light&autoRefresh=true"></iframe>')
#
gr.Interface(
get_restaurants,
[
gr.Textbox(placeholder="What type of dinner are you looking for?"),
gr.Radio([("work",{
"type": "Point",
"coordinates": [
-73.98527039999999,
40.7589099
]
}), ("home",{
"type": "Point",
"coordinates": [
-74.013686, 40.701975
]
}), ("park", {
"type": "Point",
"coordinates": [ -74.000468,40.720777
]
})], label="Location", info="What location you need?"),
gr.Slider(minimum=500, maximum=10000, randomize=False, step=5, label="Radius in meters")],
[gr.Textbox(label="MongoDB Vector Recommendations", placeholder="Results will be displayed here"), "html",
gr.Code(label="Pre-aggregate pipeline",language="json" ),
gr.Code(label="Vector Query", language="json")],
)
#radio.change(location_searched, loc, out)
if __name__ == "__main__":
demo.launch()
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