new features

app.py

@@ -7,25 +7,36 @@ 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']
 
 
 
+## Get the restaurants based on the search and location
 def get_restaurants(search, location, meters):
 
-    newTrip, pre_agg = pre_aggregate_meters(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:
+        print("Error Connecting to the MongoDB Atlas Cluster")
+        
+
+    # Pre aggregate restaurants collection based on chosen location and radius, the output is stored into
+    # trips_collection
+    newTrip, pre_agg = pre_aggregate_meters(restaurants_collection, location, meters)
+
+    ## Get openai embeddings
     response = openai_client.embeddings.create(
             input=search,
             model="text-embedding-3-small",
             dimensions=256
         )
 
+    ## prepare the similarity search on current trip
     vectorQuery = {
         "$vectorSearch": {
             "index" : "vector_index",
@@ -35,6 +46,8 @@ def get_restaurants(search, location, meters):
             "limit": 3,
             "filter": {"searchTrip": newTrip}
         }}
+
+    ## Run the retrieved documents through a RAG system.
     restaurant_docs = list(trips_collection.aggregate([vectorQuery,
         {"$project": {"_id" : 0, "embedding": 0}}]))
 
@@ -47,10 +60,14 @@ def get_restaurants(search, location, meters):
         ]
         )
 
+    ## Removed the temporary documents
     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)
 
+    ## Build the map filter
     first_restaurant = restaurant_docs[0]['restaurant_id']
     second_restaurant = restaurant_docs[1]['restaurant_id']
     third_restaurant = restaurant_docs[2]['restaurant_id']
@@ -58,12 +75,13 @@ def get_restaurants(search, location, meters):
 
    
     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)
     
 
-def pre_aggregate_meters(location, meters):
+def pre_aggregate_meters(restaurants_collection, location, meters):
 
+    ## Do the geo location preaggregate and assign the search trip id.
     tripId = ObjectId()
     pre_aggregate_pipeline =  [{
             "$geoNear": {
@@ -87,8 +105,7 @@ def pre_aggregate_meters(location, meters):
 
     result = restaurants_collection.aggregate(pre_aggregate_pipeline);
 
-    print(trips_collection.count_documents({"searchTrip": tripId}));
-    sleep(5)
+    sleep(3)
 
     return tripId, pre_aggregate_pipeline
 
@@ -97,38 +114,46 @@ with gr.Blocks() as demo:
     gr.Markdown(
     """
     # MongoDB's Vector Restaurant planner 
-    Start typing below to see the results
+    Start typing below to see the results. You can search a specific cuisine for you and choose 3 predefined locations.
+
+    The radius specify the distance from the start search location.
     """)
-    #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>')
-#    
+
+    # Create the interface
     gr.Interface(
         get_restaurants,
-        [
-         
-            gr.Textbox(placeholder="What type of dinner are you looking for?"),
-         gr.Radio([("Timesquare Manhattan",{
-            "type": "Point",
-            "coordinates": [
-            -73.98527039999999,
-            40.7589099
-            ]
-        }), ("Westside Manhattan",{
-            "type": "Point",
-            "coordinates": [
-            -74.013686, 40.701975
-            ]
-        }), ("Downtown Manhattan", {
-            "type": "Point",
-            "coordinates": [ -74.000468,40.720777
-            ]
-        })], label="Location", info="What location you need?"),
+        [gr.Textbox(placeholder="What type of dinner are you looking for?"),
+         gr.Radio(choices=[
+                ("Timesquare Manhattan", {
+                    "type": "Point",
+                    "coordinates": [-73.98527039999999, 40.7589099]
+                }),
+                ("Westside Manhattan", {
+                    "type": "Point",
+                    "coordinates": [-74.013686, 40.701975]
+                }),
+                ("Downtown Manhattan", {
+                    "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")],
-        
+        examples=[
+            ["Laxuary italian",
+            [("Westside Manhattan", {
+                    "type": "Point",
+                    "coordinates": [-74.013686, 40.701975]
+                })]
+           , 1500]
+
+        ],
+        live=False
     )
-    #radio.change(location_searched, loc, out)
+
+    
 if __name__ == "__main__":
     demo.launch()
 

iframe.html

@@ -1 +0,0 @@
-<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:['50005104', '41166347', '41314543']}}&maxDataAge=3600&theme=light&autoRefresh=true"></iframe>