refactor: move stat generation into separate class

Stats are pre-computed as well, improving performance

server/app.py

@@ -2,6 +2,7 @@ from flask import Flask, jsonify, request
 from flask_cors import CORS
 from nltk.corpus import stopwords
 from datetime import datetime
+from server.stat_gen import StatGen
 
 import nltk
 import pandas as pd
@@ -12,8 +13,7 @@ app = Flask(__name__)
 CORS(app, resources={r"/*": {"origins": "http://localhost:5173"}})
 
 # Global State
-posts_df = None
-comments_df = None
+stat_obj = None
 
 nltk.download('stopwords')
 EXCLUDE_WORDS = set(stopwords.words('english'))
@@ -33,112 +33,51 @@ def upload_data():
         return jsonify({"error": "Invalid file type. Only .jsonl files are allowed."}), 400
     
     try:
-        global posts_df, comments_df
-        posts_df = pd.read_json(post_file, lines=True)
-        comments_df = pd.read_json(comment_file, lines=True)
+        global stat_obj
+        stat_obj = StatGen(post_file, comment_file)
     except ValueError as e:
         return jsonify({"error": f"Failed to read JSONL file: {str(e)}"}), 400
     except Exception as e:
         return jsonify({"error": f"An unexpected error occurred: {str(e)}"}), 500
     
-    return jsonify({"message": "File uploaded successfully", "posts_count": len(posts_df), "comments_count": len(comments_df)}), 200
+    return jsonify({"message": "File uploaded successfully", "event_count": len(stat_obj.df)}), 200
 
 @app.route('/stats/posts_per_day', methods=['GET'])
 def posts_per_day():
-    if posts_df is None:
+    if stat_obj is None:
         return jsonify({"error": "No data uploaded"}), 400
 
     try:
-        posts_df['date'] = pd.to_datetime(posts_df['timestamp'], unit='s').dt.date
-        posts_per_day = (
-            posts_df
-            .groupby('date')
-            .size()
-            .reset_index(name='posts_count')
-        )
+        return jsonify(stat_obj.get_events_per_day().to_dict(orient='records')), 200
     except ValueError as e:
         return jsonify({"error": f"Malformed or missing data: {str(e)}"}), 400
     except Exception as e:
         return jsonify({"error": f"An unexpected error occurred: {str(e)}"}), 500
 
-    return jsonify(posts_per_day.to_dict(orient='records')), 200
-    
-@app.route('/stats/comments_per_day', methods=['GET'])
-def comments_per_day():
-    if comments_df is None:
-        return jsonify({"error": "No data uploaded"}), 400
-
-    try:
-        comments_df['date'] = pd.to_datetime(comments_df['timestamp'], unit='s').dt.date
-        comments_per_day = (
-            comments_df
-            .groupby('date')
-            .size()
-            .reset_index(name='comments_count')
-        )
-    except ValueError as e:
-        return jsonify({"error": f"Malformed or missing data: {str(e)}"}), 400
-    except Exception as e:
-        return jsonify({"error": f"An unexpected error occurred: {str(e)}"}), 500
-
-    return jsonify(comments_per_day.to_dict(orient='records')), 200
-
-@app.route("/stats/heatmap", methods=['GET'])
+@app.route("/stats/heatmap", methods=["GET"])
 def get_heatmap():
+    if stat_obj is None:
+        return jsonify({"error": "No data uploaded"}), 400
+
     try:
-        posts_df["dt"] = pd.to_datetime(posts_df["timestamp"], unit='s', utc=True)
-        posts_df["hour"] = posts_df["dt"].dt.hour
-        posts_df["weekday"] = posts_df["dt"].dt.day_name()
-
-        weekday_order = [
-        "Monday", "Tuesday", "Wednesday",
-        "Thursday", "Friday", "Saturday", "Sunday"
-        ]
-
-        posts_df["weekday"] = pd.Categorical(
-            posts_df["weekday"],
-            categories=weekday_order,
-            ordered=True
-        )
-
-        heatmap = (
-            posts_df
-            .groupby(["weekday", "hour"])
-            .size()
-            .unstack(fill_value=0)
-            .reindex(columns=range(24), fill_value=0)
-        )
+        return jsonify(stat_obj.get_heatmap().to_dict(orient="records")), 200
     except ValueError as e:
         return jsonify({"error": f"Malformed or missing data: {str(e)}"}), 400
     except Exception as e:
-        return jsonify({"error": f"An unexpected error occurred: {str(e)}"}), 500
+        return jsonify({"error": str(e)}), 500
 
-    return jsonify(heatmap.to_dict(orient="records")), 200
 
 @app.route('/stats/word_frequencies', methods=['GET'])
 def word_frequencies():
-    if posts_df is None:
+    if stat_obj is None:
         return jsonify({"error": "No data uploaded"}), 400
     
     try:
-        all_text = " ".join(posts_df['content'].fillna(''))
-        words = all_text.split()
-        word_freq = {}
-        for word in words:
-            clean_word = ''.join(c.lower() for c in word if c.isalnum())
-            if clean_word and clean_word not in EXCLUDE_WORDS:
-                word_freq[clean_word] = word_freq.get(clean_word, 0) + 1
-
-        sorted_words = sorted(word_freq.items(), key=lambda item: item[1], reverse=True)
-
-        # Get top 100 words and their frequencies and return as list of dicts
-        sorted_words = [{"word": word, "frequency": freq} for word, freq in sorted_words]
+        return jsonify(stat_obj.get_word_frequencies().to_dict(orient='records')), 200
     except ValueError as e:
         return jsonify({"error": f"Malformed or missing data: {str(e)}"}), 400
     except Exception as e:
         return jsonify({"error": f"An unexpected error occurred: {str(e)}"}), 500
 
-    return jsonify(sorted_words[:100]), 200
-
 if __name__ == "__main__":
     app.run(debug=True)