init

app.py

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+import gradio as gr
+from time import sleep
+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_aggregate_meters(location, meters)
+
+    response = openai_client.embeddings.create(
+            input=search,
+            model="text-embedding-3-small",
+            dimensions=256
+        )
+
+    restaurant_docs = list(trips_collection.aggregate([{
+        "$vectorSearch": {
+            "index" : "vector_index",
+            "queryVector": response.data[0].embedding,
+            "path" : "embedding",
+            "numCandidates": 10,
+            "limit": 3,
+            "filter": {"searchTrip": newTrip}
+        }},
+        {"$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."}
+        ]
+        )
+
+    trips_collection.delete_many({"searchTrip": newTrip})
+
+    return chat_response.choices[0].message.content
+    
+
+def pre_aggregate_meters(location, meters):
+
+    tripId = ObjectId()
+
+    restaurants_collection.aggregate([
+        {
+            "$geoNear": {
+            "near": location,
+            "distanceField": "distance",
+            "maxDistance": meters,
+            "spherical": True,
+            },
+        },
+        {
+            "$addFields": {
+                "searchTrip" : tripId,
+                "date" : tripId.generation_time
+            }
+        },
+        {
+            "$merge": {
+                "into": "smart_trips"
+            }
+        }   
+    ]);
+
+    sleep(10)
+
+    return tripId
+
+
+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": [
+            40.701975, -74.013686
+            ]
+        }), ("park", {
+            "type": "Point",
+            "coordinates": [40.720777, -74.000468
+            ]
+        })], 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"),
+        
+    )
+    #radio.change(location_searched, loc, out)
+if __name__ == "__main__":
+    demo.launch()
+

flagged/log.csv

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+search,Location,out,output,flag,username,timestamp
+Movies,work,,I will now search: Movies!,,,2024-02-07 12:04:10.349102
+Movies,work,,I will now search: Movies!,,,2024-02-07 12:04:12.484796

requirements.tx

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+openai
+pymongo