Paul filter by year

backend/.gitignore

@@ -1,5 +1,6 @@
 .env
 __pycache__/
 bluebook_env/
+bluebook_env_1/
 .coverage
 htmlcov

backend/app.py

@@ -9,6 +9,7 @@
 from pymongo.mongo_client import MongoClient
 import requests
 import xml.etree.ElementTree as ET
+import datetime
 
 COURSE_QUERY_LIMIT = 5
 SAFETY_CHECK_ENABLED = False
@@ -31,7 +32,6 @@ def load_config(app, test_config=None):
         # Load configuration from environment variables
         app.config["MONGO_URI"] = os.getenv("MONGO_URI")
 
-
 # Separate function to initialize database
 def init_database(app):
     if "MONGO_URI" in app.config:
@@ -40,7 +40,6 @@ def init_database(app):
         app.config["collection"] = db["parsed_courses"]
         # else, set to None or Mock in case of testing
 
-
 def create_app(test_config=None):
     app = Flask(__name__)
     CORS(app)
@@ -101,7 +100,14 @@ def validate_cas_ticket():
     @app.route("/api/chat", methods=["POST"])
     def chat():
         data = request.get_json()
-        user_messages = data["message"]
+        user_messages = data.get("message", None)
+
+        filter_season_codes = data.get("season_codes", None) # assume it is an array of season code
+        filter_subject = data.get("subject", None)
+        filter_areas = data.get("areas", None)
+
+        if not user_messages:
+            return jsonify({"error": "No message provided"})
 
         # remove id before sending to OpenAI
         for message in user_messages:
@@ -187,33 +193,50 @@ def chat():
 
         collection = app.config["collection"]
 
-        database_response = collection.aggregate(
-            [
-                {
-                    "$vectorSearch": {
-                        "index": "parsed_courses_title_description_index",
-                        "path": "embedding",
-                        # 'filter': {
-                        #     'rating': {
-                        #         args['operator']: args['rating']
-                        #     }
-                        # },
-                        "queryVector": query_vector,
-                        "numCandidates": 30,
-                        "limit": COURSE_QUERY_LIMIT,
-                    }
+        aggregate_pipeline = {
+            "$vectorSearch": {
+                "index": "parsed_courses_title_description_index",
+                "path": "embedding",
+                "queryVector": query_vector,
+                "numCandidates": 30,
+                "limit": COURSE_QUERY_LIMIT,
+            }
+        }
+
+        if filter_season_codes:
+            aggregate_pipeline["$vectorSearch"]["filter"] = {
+                "season_code": {
+                    "$in": filter_season_codes
                 }
-            ]
-        )
+            }
+
+        if filter_subject:
+            aggregate_pipeline["$vectorSearch"]["filter"] = {
+                "subject": {
+                    "$eq": filter_subject
+                }
+            }
+
+        if filter_areas:
+            aggregate_pipeline["$vectorSearch"]["filter"] = {
+                "areas": {
+                    "$in": filter_areas
+                }
+            }
 
+        database_response = collection.aggregate([aggregate_pipeline])
         database_response = list(database_response)
 
         recommended_courses = [
             {
+                "season_code": course["season_code"],
                 "course_code": course["course_code"],
                 "title": course["title"],
                 "description": course["description"],
                 "areas": course["areas"],
+                "sentiment_label": course["sentiment_info"]["final_label"],
+                "sentiment_score": course["sentiment_info"]["final_proportion"],
+
             }
             for course in database_response
         ]

backend/test_app.py

@@ -13,8 +13,10 @@ def client():
                 "course_code": "CPSC 150",
                 "description": "Introduction to the basic ideas of computer science (computability, algorithm, virtual machine, symbol processing system), and of several ongoing relationships between computer science and other fields, particularly philosophy of mind.",
                 "season_code": "202303",
-                "sentiment_label": "NEGATIVE",
-                "sentiment_score": 0.9444444444444444,
+                "sentiment_info": {
+                    "final_label": "NEGATIVE",
+                    "final_proportion": 0.9444444444444444,
+                },
                 "title": "Computer Science and the Modern Intellectual Agenda",
             },
         ]