server/app.py

@@ -43,6 +43,56 @@ auth_manager = AuthManager(db, bcrypt)
 
 stat_gen = StatGen()
 
+
+def _parse_datetime_filter(value):
+    if not value:
+        return None
+
+    try:
+        return datetime.datetime.fromisoformat(value)
+    except ValueError:
+        try:
+            return datetime.datetime.fromtimestamp(float(value))
+        except ValueError as err:
+            raise ValueError(
+                "Date filters must be ISO-8601 strings or Unix timestamps"
+            ) from err
+
+
+def _get_request_filters() -> dict:
+    filters = {}
+
+    search_query = request.args.get("search_query") or request.args.get("query")
+    if search_query:
+        filters["search_query"] = search_query
+
+    start_date = _parse_datetime_filter(
+        request.args.get("start_date") or request.args.get("start")
+    )
+    if start_date:
+        filters["start_date"] = start_date
+
+    end_date = _parse_datetime_filter(
+        request.args.get("end_date") or request.args.get("end")
+    )
+    if end_date:
+        filters["end_date"] = end_date
+
+    data_sources = request.args.getlist("data_sources")
+    if not data_sources:
+        data_sources = request.args.getlist("sources")
+
+    if len(data_sources) == 1 and "," in data_sources[0]:
+        data_sources = [
+            source.strip() for source in data_sources[0].split(",") if source.strip()
+        ]
+
+    if data_sources:
+        filters["data_sources"] = data_sources
+
+    return filters
+
+
 @app.route("/register", methods=["POST"])
 def register_user():
     data = request.get_json()
@@ -136,7 +186,11 @@ def upload_data():
         db.save_dataset_content(dataset_id, enriched_df)
 
         return jsonify(
-            {"message": "File uploaded successfully", "event_count": len(enriched_df), "dataset_id": dataset_id}
+            {
+                "message": "File uploaded successfully",
+                "event_count": len(enriched_df),
+                "dataset_id": dataset_id,
+            }
         ), 200
     except ValueError as e:
         return jsonify({"error": f"Failed to read JSONL file: {str(e)}"}), 400
@@ -172,7 +226,8 @@ def content_endpoint(dataset_id):
 
     dataset_content = db.get_dataset_content(dataset_id)
     try:
-        return jsonify(stat_gen.get_content_analysis(dataset_content)), 200
+        filters = _get_request_filters()
+        return jsonify(stat_gen.get_content_analysis(dataset_content, filters)), 200
     except ValueError as e:
         return jsonify({"error": f"Malformed or missing data: {str(e)}"}), 400
     except Exception as e:
@@ -192,7 +247,8 @@ def get_summary(dataset_id):
     dataset_content = db.get_dataset_content(dataset_id)
 
     try:
-        return jsonify(stat_gen.summary(dataset_content)), 200
+        filters = _get_request_filters()
+        return jsonify(stat_gen.summary(dataset_content, filters)), 200
     except ValueError as e:
         return jsonify({"error": f"Malformed or missing data: {str(e)}"}), 400
     except Exception as e:
@@ -212,7 +268,8 @@ def get_time_analysis(dataset_id):
     dataset_content = db.get_dataset_content(dataset_id)
 
     try:
-        return jsonify(stat_gen.get_time_analysis(dataset_content)), 200
+        filters = _get_request_filters()
+        return jsonify(stat_gen.get_time_analysis(dataset_content, filters)), 200
     except ValueError as e:
         return jsonify({"error": f"Malformed or missing data: {str(e)}"}), 400
     except Exception as e:
@@ -232,7 +289,8 @@ def get_user_analysis(dataset_id):
     dataset_content = db.get_dataset_content(dataset_id)
 
     try:
-        return jsonify(stat_gen.get_user_analysis(dataset_content)), 200
+        filters = _get_request_filters()
+        return jsonify(stat_gen.get_user_analysis(dataset_content, filters)), 200
     except ValueError as e:
         return jsonify({"error": f"Malformed or missing data: {str(e)}"}), 400
     except Exception as e:
@@ -252,7 +310,8 @@ def get_cultural_analysis(dataset_id):
     dataset_content = db.get_dataset_content(dataset_id)
 
     try:
-        return jsonify(stat_gen.get_cultural_analysis(dataset_content)), 200
+        filters = _get_request_filters()
+        return jsonify(stat_gen.get_cultural_analysis(dataset_content, filters)), 200
     except ValueError as e:
         return jsonify({"error": f"Malformed or missing data: {str(e)}"}), 400
     except Exception as e:
@@ -272,43 +331,15 @@ def get_interaction_analysis(dataset_id):
     dataset_content = db.get_dataset_content(dataset_id)
 
     try:
-        return jsonify(stat_gen.get_interactional_analysis(dataset_content)), 200
+        filters = _get_request_filters()
+        return jsonify(
+            stat_gen.get_interactional_analysis(dataset_content, filters)
+        ), 200
     except ValueError as e:
         return jsonify({"error": f"Malformed or missing data: {str(e)}"}), 400
     except Exception as e:
         print(traceback.format_exc())
         return jsonify({"error": f"An unexpected error occurred: {str(e)}"}), 500
 
-
-@app.route("/dataset/query", methods=["POST"])
-@jwt_required()
-def filter_query():
-    data = request.get_json()
-
-    if "query" in data:
-        stat_gen.set_search_query(data["query"])
-
-    if "start" in data:
-        start_timestamp = datetime.datetime.fromisoformat(data["start"])
-        stat_gen.set_start_date(start_timestamp)
-
-    if "end" in data:
-        end_timestamp = datetime.datetime.fromisoformat(data["end"])
-        stat_gen.set_end_date(end_timestamp)
-
-    if "sources" in data:
-        data_sources = set(data["sources"])
-        stat_gen.set_data_sources(data_sources)
-
-    return jsonify({"message": "Filters set successfully"}), 200
-
-
-@app.route("/database/query/reset", methods=["GET"])
-@jwt_required()
-def reset_dataset():
-    stat_gen.reset_filters()
-    return jsonify({"message": "Filters reset successfully"}), 200
-
-
 if __name__ == "__main__":
     app.run(debug=True)

server/stat_gen.py

@@ -39,105 +39,135 @@ class StatGen:
         self.linguistic_analysis = LinguisticAnalysis(EXCLUDE_WORDS)
         self.cultural_analysis = CulturalAnalysis()
 
-        self.search_query = ""
-        self.start_date_filter = None
-        self.end_date_filter = None
-        self.data_source_filters = set()
-
     ## Private Methods
-    def _prepare_filtered_df(self, df: pd.DataFrame) -> pd.DataFrame:
+    def _prepare_filtered_df(self, 
+                             df: pd.DataFrame, 
+                             filters: dict | None = None
+                             ) -> pd.DataFrame:
+        filters = filters or {}
         filtered_df = df.copy()
 
-        if self.search_query:
+        search_query = filters.get("search_query", None)
+        start_date_filter = filters.get("start_date", None)
+        end_date_filter = filters.get("end_date", None)
+        data_source_filter = filters.get("data_sources", None)
+
+        if search_query:
             mask = (
-                filtered_df["content"].str.contains(self.search_query, case=False, na=False)
-                | filtered_df["author"].str.contains(self.search_query, case=False, na=False).fillna(False)
-                | filtered_df["title"].str.contains(self.search_query, case=False, na=False, regex=False).fillna(False)
+                filtered_df["content"].str.contains(search_query, case=False, na=False)
+                | filtered_df["author"]
+                .str.contains(search_query, case=False, na=False)
+                .fillna(False)
+                | filtered_df["title"]
+                .str.contains(search_query, case=False, na=False, regex=False)
+                .fillna(False)
             )
             filtered_df = filtered_df[mask]
 
-        if self.start_date_filter and self.end_date_filter:
-            filtered_df = filtered_df[
-                (filtered_df["dt"] >= self.start_date_filter) & (filtered_df["dt"] <= self.end_date_filter)
-            ]
+        if start_date_filter:
+            filtered_df = filtered_df[(filtered_df["dt"] >= start_date_filter)]
 
-        if self.data_source_filters:
-            enabled_sources = [src for src, enabled in self.data_source_filters.items() if enabled]
-            if enabled_sources:
-                filtered_df = filtered_df[filtered_df["source"].isin(enabled_sources)]
+        if end_date_filter:
+            filtered_df = filtered_df[(filtered_df["dt"] <= end_date_filter)]
+
+        if data_source_filter:
+            filtered_df = filtered_df[filtered_df["source"].isin(data_source_filter)]
 
         return filtered_df
 
     ## Public Methods
 
-    def get_time_analysis(self, df: pd.DataFrame) -> dict:
+    def get_time_analysis(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
+        filtered_df = self._prepare_filtered_df(df, filters)
+
         return {
-            "events_per_day": self.temporal_analysis.posts_per_day(df),
-            "weekday_hour_heatmap": self.temporal_analysis.heatmap(df),
+            "events_per_day": self.temporal_analysis.posts_per_day(filtered_df),
+            "weekday_hour_heatmap": self.temporal_analysis.heatmap(filtered_df),
         }
 
-    def get_content_analysis(self, df: pd.DataFrame) -> dict:
+    def get_content_analysis(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
+        filtered_df = self._prepare_filtered_df(df, filters)
+
         return {
-            "word_frequencies": self.linguistic_analysis.word_frequencies(df),
-            "common_two_phrases": self.linguistic_analysis.ngrams(df),
-            "common_three_phrases": self.linguistic_analysis.ngrams(df, n=3),
-            "average_emotion_by_topic": self.emotional_analysis.avg_emotion_by_topic(df),
-            "reply_time_by_emotion": self.temporal_analysis.avg_reply_time_per_emotion(df),
+            "word_frequencies": self.linguistic_analysis.word_frequencies(filtered_df),
+            "common_two_phrases": self.linguistic_analysis.ngrams(filtered_df),
+            "common_three_phrases": self.linguistic_analysis.ngrams(filtered_df, n=3),
+            "average_emotion_by_topic": self.emotional_analysis.avg_emotion_by_topic(
+                filtered_df
+            ),
+            "reply_time_by_emotion": self.temporal_analysis.avg_reply_time_per_emotion(
+                filtered_df
+            ),
         }
 
-    def get_user_analysis(self, df: pd.DataFrame) -> dict:
+    def get_user_analysis(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
+        filtered_df = self._prepare_filtered_df(df, filters)
+
         return {
-            "top_users": self.interaction_analysis.top_users(df),
-            "users": self.interaction_analysis.per_user_analysis(df),
-            "interaction_graph": self.interaction_analysis.interaction_graph(df),
+            "top_users": self.interaction_analysis.top_users(filtered_df),
+            "users": self.interaction_analysis.per_user_analysis(filtered_df),
+            "interaction_graph": self.interaction_analysis.interaction_graph(
+                filtered_df
+            ),
         }
 
-    def get_interactional_analysis(self, df: pd.DataFrame) -> dict:
+    def get_interactional_analysis(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
+        filtered_df = self._prepare_filtered_df(df, filters)
+
         return {
-            "average_thread_depth": self.interaction_analysis.average_thread_depth(df),
-            "average_thread_length_by_emotion": self.interaction_analysis.average_thread_length_by_emotion(df),
+            "average_thread_depth": self.interaction_analysis.average_thread_depth(
+                filtered_df
+            ),
+            "average_thread_length_by_emotion": self.interaction_analysis.average_thread_length_by_emotion(
+                filtered_df
+            ),
         }
 
-    def get_cultural_analysis(self, df: pd.DataFrame) -> dict:
+    def get_cultural_analysis(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
+        filtered_df = self._prepare_filtered_df(df, filters)
+
         return {
-            "identity_markers": self.cultural_analysis.get_identity_markers(df),
-            "stance_markers": self.cultural_analysis.get_stance_markers(df),
-            "entity_salience": self.cultural_analysis.get_avg_emotions_per_entity(df),
+            "identity_markers": self.cultural_analysis.get_identity_markers(
+                filtered_df
+            ),
+            "stance_markers": self.cultural_analysis.get_stance_markers(filtered_df),
+            "entity_salience": self.cultural_analysis.get_avg_emotions_per_entity(
+                filtered_df
+            ),
         }
 
-    def summary(self, df: pd.DataFrame) -> dict:
-        total_posts = (df["type"] == "post").sum()
-        total_comments = (df["type"] == "comment").sum()
-        events_per_user = df.groupby("author").size()
+    def summary(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
+        filtered_df = self._prepare_filtered_df(df, filters)
+
+        total_posts = (filtered_df["type"] == "post").sum()
+        total_comments = (filtered_df["type"] == "comment").sum()
+        events_per_user = filtered_df.groupby("author").size()
+
+        if filtered_df.empty:
+            return {
+                "total_events": 0,
+                "total_posts": 0,
+                "total_comments": 0,
+                "unique_users": 0,
+                "comments_per_post": 0,
+                "lurker_ratio": 0,
+                "time_range": {
+                    "start": None,
+                    "end": None,
+                },
+                "sources": [],
+            }
 
         return {
-            "total_events": int(len(df)),
+            "total_events": int(len(filtered_df)),
             "total_posts": int(total_posts),
             "total_comments": int(total_comments),
             "unique_users": int(events_per_user.count()),
             "comments_per_post": round(total_comments / max(total_posts, 1), 2),
             "lurker_ratio": round((events_per_user == 1).mean(), 2),
             "time_range": {
-                "start": int(df["dt"].min().timestamp()),
-                "end": int(df["dt"].max().timestamp()),
+                "start": int(filtered_df["dt"].min().timestamp()),
+                "end": int(filtered_df["dt"].max().timestamp()),
             },
-            "sources": df["source"].dropna().unique().tolist(),
+            "sources": filtered_df["source"].dropna().unique().tolist(),
         }
-
-    def set_search_query(self, search_query: str) -> None:
-        self.search_query = search_query
-
-    def set_start_date(self, start: datetime.datetime) -> None:
-        self.start_date_filter = start
-
-    def set_end_date(self, end: datetime.datetime) -> None:
-        self.end_date_filter = end
-
-    def set_data_sources(self, data_sources: set) -> None:
-        self.data_source_filters = data_sources
-        
-    def reset_filters(self) -> None:
-        self.search_query = ""
-        self.start_date_filter = None
-        self.end_date_filter = None
-        self.data_source_filters = set()