refactor: move stat generation into separate class

Stats are pre-computed as well, improving performance

server/stat_gen.py

@@ -0,0 +1,108 @@
+import pandas as pd
+import re
+import nltk
+
+from nltk.corpus import stopwords
+from collections import Counter
+
+DOMAIN_STOPWORDS = {
+    "www", "https", "http",
+    "boards", "boardsie",
+    "comment", "comments",
+    "discussion", "thread",
+    "post", "posts",
+    "would", "could", "should",
+    "like", "get", "one"
+}
+
+nltk.download('stopwords')
+EXCLUDE_WORDS = set(stopwords.words('english')) | DOMAIN_STOPWORDS
+
+class StatGen:
+    def __init__(self, posts: list, comments: list) -> None:
+        posts_df = pd.read_json(posts, lines=True)
+        comments_df = pd.read_json(comments, lines=True)
+
+        posts_df["type"] = "post"
+        posts_df["parent_id"] = None
+
+        comments_df["type"] = "comment"
+        comments_df["parent_id"] = comments_df.get("post_id")
+
+        self.df = pd.concat([posts_df, comments_df])
+        self._add_date_cols(self.df)
+
+        # Datasets
+        self.heatmap = self._generate_heatmap()
+        self.word_frequencies = self._get_word_frequencies(100)
+        self.events_per_day = self._get_events_per_day()
+
+    ## Private Methods
+    def _add_date_cols(self, df: pd.DataFrame) -> None:
+        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()
+
+    def _get_events_per_day(self) -> pd.DataFrame:
+        return (
+            self.df
+            .groupby('date')
+            .size()
+            .reset_index(name='posts_count')
+        )
+
+    def _generate_heatmap(self) -> pd.DataFrame:
+        weekday_order = [
+            "Monday", "Tuesday", "Wednesday",
+            "Thursday", "Friday", "Saturday", "Sunday"
+        ]
+
+        self.df["weekday"] = pd.Categorical(
+            self.df["weekday"],
+            categories=weekday_order,
+            ordered=True
+        )
+
+        return (
+            self.df
+            .groupby(["weekday", "hour"])
+            .size()
+            .unstack(fill_value=0)
+            .reindex(columns=range(24), fill_value=0)
+        )
+
+    def _get_word_frequencies(self, limit: int) -> pd.DataFrame:
+        texts = (
+            self.df["content"]
+            .dropna()
+            .astype(str)
+            .str.lower()
+        )
+
+        words = []
+        for text in texts:
+            tokens = re.findall(r"\b[a-z]{3,}\b", text)
+            words.extend(
+                w for w in tokens
+                if w not in EXCLUDE_WORDS
+            )
+
+        counts = Counter(words)
+
+        return (
+            pd.DataFrame(counts.items(), columns=["word", "count"])
+            .sort_values("count", ascending=False)
+            .head(limit)
+            .reset_index(drop=True)
+        )
+    
+    ## Public
+    def get_heatmap(self) -> pd.DataFrame:
+        return self.heatmap
+    
+    def get_word_frequencies(self) -> pd.DataFrame:
+        return self.word_frequencies
+    
+    def get_events_per_day(self) -> pd.DataFrame:
+        return self.events_per_day