refactor: improve API wording & cleanup code

server/app.py

@@ -126,8 +126,8 @@ def get_interaction_analysis():
         print(traceback.format_exc())
         return jsonify({"error": f"An unexpected error occurred: {str(e)}"}), 500
 
-@app.route('/filter/search', methods=["POST"])
-def search_dataset():
+@app.route('/filter/query', methods=["POST"])
+def filter_query():
     if stat_obj is None:
         return jsonify({"error": "No data uploaded"}), 400
 
@@ -137,7 +137,7 @@ def search_dataset():
         return jsonify(stat_obj.df.to_dict(orient="records")), 200
     
     query = data["query"]
-    filtered_df = stat_obj.search(query)
+    filtered_df = stat_obj.filter_by_query(query)
 
     return jsonify(filtered_df), 200
 

server/stat_gen.py

@@ -16,8 +16,7 @@ DOMAIN_STOPWORDS = {
     "comment", "comments",
     "discussion", "thread",
     "post", "posts",
-    "would", "could", "should",
-    "like", "get", "one"
+    "would", "get", "one"
 }
 
 nltk.download('stopwords')
@@ -41,7 +40,10 @@ class StatGen:
         self.df.drop(columns=["post_id"], inplace=True, errors="ignore")
 
         self.nlp = NLP(self.df, "title", "content", domain_topics)
-        self._add_extra_cols(self.df)
+        self.nlp.add_emotion_cols()
+        self.nlp.add_topic_col()
+        self.nlp.add_ner_cols()
+        self._add_time_cols(self.df)
 
         self.temporal_analysis = TemporalAnalysis(self.df)
         self.emotional_analysis = EmotionalAnalysis(self.df)
@@ -52,23 +54,18 @@ class StatGen:
         self.original_df = self.df.copy(deep=True)
 
     ## Private Methods
-    def _add_extra_cols(self, df: pd.DataFrame) -> None:
-        df['timestamp'] = pd.to_numeric(self.df['timestamp'], errors='coerce')
+    def _add_time_cols(self, df: pd.DataFrame) -> None:
+        df['timestamp'] = pd.to_numeric(df['timestamp'], errors='coerce')
         df['date'] = pd.to_datetime(df['timestamp'], unit='s').dt.date
         df["dt"] = pd.to_datetime(df["timestamp"], unit="s", utc=True)
         df["hour"] = df["dt"].dt.hour
         df["weekday"] = df["dt"].dt.day_name()
-        
-        self.nlp.add_emotion_cols()
-        self.nlp.add_topic_col()
-        self.nlp.add_ner_cols()
     
     ## Public
 
-
     # topics over time
     # emotions over time
-    def get_time_analysis(self) -> pd.DataFrame:
+    def get_time_analysis(self) -> dict:
         return {
             "events_per_day": self.temporal_analysis.posts_per_day(),
             "weekday_hour_heatmap": self.temporal_analysis.heatmap()
@@ -130,7 +127,7 @@ class StatGen:
             "sources": self.df["source"].dropna().unique().tolist()
         }
         
-    def search(self, search_query: str) -> dict:
+    def filter_by_query(self, search_query: str) -> dict:
         self.df = self.df[
             self.df["content"].str.contains(search_query)
         ]