feat: update schema to include posts and comments

db/database.py

@@ -0,0 +1,52 @@
+import os
+import psycopg2
+from psycopg2.extras import RealDictCursor
+
+
+class PostgresConnector:
+    """
+    Simple PostgreSQL connector (single connection).
+    """
+
+    def __init__(self):
+        self.connection = psycopg2.connect(
+            host=os.getenv("POSTGRES_HOST", "localhost"),
+            port=os.getenv("POSTGRES_PORT", 5432),
+            user=os.getenv("POSTGRES_USER", "postgres"),
+            password=os.getenv("POSTGRES_PASSWORD", "postgres"),
+            database=os.getenv("POSTGRES_DB", "postgres"),
+        )
+        self.connection.autocommit = False
+
+    def execute(self, query, params=None, fetch=False):
+        with self.connection.cursor(cursor_factory=RealDictCursor) as cursor:
+            cursor.execute(query, params)
+            if fetch:
+                return cursor.fetchall()
+            self.connection.commit()
+
+    def executemany(self, query, param_list):
+        with self.connection.cursor(cursor_factory=RealDictCursor) as cursor:
+            cursor.executemany(query, param_list)
+            self.connection.commit()
+
+    def save_user(self, username, email, password_hash):
+        query = """
+            INSERT INTO users (username, email, password_hash)
+            VALUES (%s, %s, %s)
+        """
+        self.execute(query, (username, email, password_hash))
+
+    def get_user_by_username(self, username) -> dict:
+        query = "SELECT id, username, email, password_hash FROM users WHERE username = %s"
+        result = self.execute(query, (username,), fetch=True)
+        return result[0] if result else None
+    
+    def get_user_by_email(self, email) -> dict:
+        query = "SELECT id, username, email, password_hash FROM users WHERE email = %s"
+        result = self.execute(query, (email,), fetch=True)
+        return result[0] if result else None
+
+    def close(self):
+        if self.connection:
+            self.connection.close()

db/schema.sql

@@ -0,0 +1,35 @@
+CREATE TABLE users (
+    id SERIAL PRIMARY KEY,
+    username VARCHAR(255) NOT NULL UNIQUE,
+    email VARCHAR(255) NOT NULL UNIQUE,
+    password_hash VARCHAR(255) NOT NULL,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+CREATE TABLE has_access (
+    id SERIAL PRIMARY KEY,
+    user_id INTEGER NOT NULL,
+    post_id INTEGER NOT NULL,
+    FOREIGN KEY (user_id) REFERENCES users(id) ON DELETE CASCADE,
+    FOREIGN KEY (post_id) REFERENCES posts(id) ON DELETE CASCADE
+);
+
+CREATE TABLE posts (
+    id SERIAL PRIMARY KEY,
+    author VARCHAR(255) NOT NULL,
+    title VARCHAR(255) NOT NULL,
+    content TEXT NOT NULL,
+    created_at TIMESTAMP NOT NULL,
+    source VARCHAR(255) NOT NULL
+);
+
+CREATE TABLE comments (
+    id SERIAL PRIMARY KEY,
+    post_id INTEGER NOT NULL,
+    author VARCHAR(255) NOT NULL,
+    content TEXT NOT NULL,
+    created_at TIMESTAMP NOT NULL,
+    reply_to VARCHAR(255),
+    source VARCHAR(255) NOT NULL,
+    FOREIGN KEY (post_id) REFERENCES posts(id) ON DELETE CASCADE
+);

docker-compose.yml

@@ -0,0 +1,15 @@
+services:
+  postgres:
+    image: postgres:16
+    container_name: postgres_db
+    restart: unless-stopped
+    env_file:
+      - .env
+    ports:
+      - "5432:5432"
+    volumes:
+      - ./db/postgres_vol:/var/lib/postgresql/data
+      - ./db/schema.sql:/docker-entrypoint-initdb.d/schema.sql
+
+volumes:
+  postgres_data:

frontend/src/components/UserStats.tsx

@@ -34,7 +34,7 @@ function ApiToGraphData(apiData: InteractionGraph) {
 }
 
 
-const InteractionStats = (props: { data: UserAnalysisResponse }) => {
+const UserStats = (props: { data: UserAnalysisResponse }) => {
   const graphData = ApiToGraphData(props.data.interaction_graph);
 
   return (
@@ -44,7 +44,7 @@ const InteractionStats = (props: { data: UserAnalysisResponse }) => {
             This graph visualizes interactions between users based on comments and replies. 
             Nodes represent users, and edges represent interactions (e.g., comments or replies) between them.
         </p>
-        <div style={{ height: "600px", border: "1px solid #ccc", borderRadius: 8, marginTop: 16 }}>
+        <div>
             <ForceGraph3D
                 graphData={graphData}
                 nodeAutoColorBy="id"
@@ -58,4 +58,4 @@ const InteractionStats = (props: { data: UserAnalysisResponse }) => {
   );
 }
 
-export default InteractionStats;
+export default UserStats;

frontend/src/pages/Stats.tsx

@@ -3,7 +3,7 @@ import axios from "axios";
 import StatsStyling from "../styles/stats_styling";
 import SummaryStats from "../components/SummaryStats";
 import EmotionalStats from "../components/EmotionalStats";
-import InteractionStats from "../components/InteractionStats";
+import InteractionStats from "../components/UserStats";
 
 import { 
   type SummaryResponse, 

requirements.txt

@@ -1,10 +1,12 @@
 beautifulsoup4==4.14.3
-Flask==3.1.2
+Flask==3.1.3
 flask_cors==6.0.2
 google_api_python_client==2.188.0
-keybert==0.9.0
 nltk==3.9.2
-pandas==3.0.0
+numpy==2.4.2
+pandas==3.0.1
+psycopg2==2.9.11
+psycopg2_binary==2.9.11
 python-dotenv==1.2.1
 Requests==2.32.5
 sentence_transformers==5.2.2

server/analysis/interactional.py

@@ -124,3 +124,85 @@ class InteractionAnalysis:
             interactions[a][b] = interactions[a].get(b, 0) + 1
 
         return interactions
+    
+    def average_thread_depth(self):
+        depths = []
+        id_to_reply = self.df.set_index("id")["reply_to"].to_dict()
+        for _, row in self.df.iterrows():
+            depth = 0
+            current_id = row["id"]
+
+            while True:
+                reply_to = id_to_reply.get(current_id)
+                if pd.isna(reply_to) or reply_to == "":
+                    break
+
+                depth += 1
+                current_id = reply_to
+
+            depths.append(depth)
+
+        if not depths:
+            return 0
+        
+        return round(sum(depths) / len(depths), 2)
+    
+    def average_thread_length_by_emotion(self):
+        emotion_exclusions = {"emotion_neutral", "emotion_surprise"}
+
+        emotion_cols = [
+            c for c in self.df.columns
+            if c.startswith("emotion_") and c not in emotion_exclusions
+        ]
+
+        id_to_reply = self.df.set_index("id")["reply_to"].to_dict()
+        length_cache = {}
+
+        def thread_length_from(start_id):
+            if start_id in length_cache:
+                return length_cache[start_id]
+
+            seen = set()
+            length = 1
+            current = start_id
+
+            while True:
+                if current in seen:
+                    # infinite loop shouldn't happen, but just in case
+                    break
+                seen.add(current)
+
+                reply_to = id_to_reply.get(current)
+
+                if reply_to is None or (isinstance(reply_to, float) and pd.isna(reply_to)) or reply_to == "":
+                    break
+
+                length += 1
+                current = reply_to
+
+                if current in length_cache:
+                    length += (length_cache[current] - 1)
+                    break
+
+            length_cache[start_id] = length
+            return length
+
+        emotion_to_lengths = {}
+
+        # Fill NaNs in emotion cols to avoid max() issues
+        emo_df = self.df[["id"] + emotion_cols].copy()
+        emo_df[emotion_cols] = emo_df[emotion_cols].fillna(0)
+
+        for _, row in emo_df.iterrows():
+            msg_id = row["id"]
+            length = thread_length_from(msg_id)
+
+            emotions = {c: row[c] for c in emotion_cols}
+            dominant = max(emotions, key=emotions.get)
+
+            emotion_to_lengths.setdefault(dominant, []).append(length)
+
+        return {
+            emotion: round(sum(lengths) / len(lengths), 2)
+            for emotion, lengths in emotion_to_lengths.items()
+        }

server/analysis/linguistic.py

@@ -9,6 +9,10 @@ class LinguisticAnalysis:
         self.df = df
         self.word_exclusions = word_exclusions
 
+    def _tokenize(self, text: str):
+        tokens = re.findall(r"\b[a-z]{3,}\b", text)
+        return [t for t in tokens if t not in self.word_exclusions]
+
     def _clean_text(self, text: str) -> str:
         text = re.sub(r"http\S+", "", text)        # remove URLs
         text = re.sub(r"www\S+", "", text)
@@ -66,3 +70,44 @@ class LinguisticAnalysis:
             .head(limit)
             .to_dict(orient="records")
         )
+        
+    def identity_markers(self):
+        df = self.df.copy()
+        df["content"] = df["content"].fillna("").astype(str).str.lower()
+
+        in_group_words = {"we", "us", "our", "ourselves"}
+        out_group_words = {"they", "them", "their", "themselves"}
+
+        emotion_exclusions = [
+            "emotion_neutral",
+            "emotion_surprise"
+        ]
+
+        emotion_cols = [
+            col for col in self.df.columns
+            if col.startswith("emotion_") and col not in emotion_exclusions
+        ]
+        in_count = 0
+        out_count = 0
+        in_emotions = {e: 0 for e in emotion_cols}
+        out_emotions = {e: 0 for e in emotion_cols}
+        total = 0
+
+        for post in df:
+            text = post["content"]
+            tokens = re.findall(r"\b[a-z]{2,}\b", text)
+            total += len(tokens)
+            in_count += sum(t in in_group_words for t in tokens)
+            out_count += sum(t in out_group_words for t in tokens)
+
+            emotions = post[emotion_cols]
+            print(emotions)
+
+            
+
+        return {
+            "in_group_usage": in_count,
+            "out_group_usage": out_count,
+            "in_group_ratio": round(in_count / max(total, 1), 5),
+            "out_group_ratio": round(out_count / max(total, 1), 5),
+        }

server/app.py

@@ -1,21 +1,102 @@
+import os
+
+from dotenv import load_dotenv
 from flask import Flask, jsonify, request
 from flask_cors import CORS
+from flask_bcrypt import Bcrypt
+from flask_jwt_extended import (
+    JWTManager,
+    create_access_token,
+    jwt_required,
+    get_jwt_identity
+)
+
 from server.stat_gen import StatGen
+from db.database import PostgresConnector
+from server.auth import AuthManager
 
 import pandas as pd
 import traceback
 import json
 
 app = Flask(__name__)
+db = PostgresConnector()
 
-# Allow for CORS from localhost:5173
+# Env Variables
-CORS(app, resources={r"/*": {"origins": "http://localhost:5173"}})
+load_dotenv()
+frontend_url = os.getenv("FRONTEND_URL", "http://localhost:5173")
+jwt_secret_key = os.getenv("JWT_SECRET_KEY", "super-secret-change-this")
+jwt_access_token_expires = int(os.getenv("JWT_ACCESS_TOKEN_EXPIRES", 1200)) # Default to 20 minutes
+
+# Flask Configuration
+CORS(app, resources={r"/*": {"origins": frontend_url}})
+app.config["JWT_SECRET_KEY"] = jwt_secret_key
+app.config["JWT_ACCESS_TOKEN_EXPIRES"] = jwt_access_token_expires 
+
+bcrypt = Bcrypt(app)
+jwt = JWTManager(app)
+auth_manager = AuthManager(db, bcrypt)
 
 # Global State
-posts_df = pd.read_json('small.jsonl', lines=True)
+# posts_df = pd.read_json('small.jsonl', lines=True)
-with open("topic_buckets.json", "r", encoding="utf-8") as f:
+# with open("topic_buckets.json", "r", encoding="utf-8") as f:
-    domain_topics = json.load(f)
+#     domain_topics = json.load(f)
-stat_obj = StatGen(posts_df, domain_topics)
+# stat_obj = StatGen(posts_df, domain_topics)
+stat_obj = None
+
+@app.route('/register', methods=['POST'])
+def register_user():
+    data = request.get_json()
+
+    if not data or "username" not in data or "email" not in data or "password" not in data: 
+        return jsonify({"error": "Missing username, email, or password"}), 400
+    
+    username = data["username"]
+    email = data["email"]
+    password = data["password"]
+
+    try:
+        auth_manager.register_user(username, email, password)
+    except ValueError as e:
+        return jsonify({"error": str(e)}), 400
+    except Exception as e:
+        print(traceback.format_exc())
+        return jsonify({"error": f"An unexpected error occurred: {str(e)}"}), 500
+
+    print(f"Registered new user: {username}")
+    return jsonify({"message": f"User '{username}' registered successfully"}), 200
+
+@app.route('/login', methods=['POST'])
+def login_user():
+    data = request.get_json()
+
+    if not data or "username" not in data or "password" not in data:
+        return jsonify({"error": "Missing username or password"}), 400
+    
+    username = data["username"]
+    password = data["password"]
+
+    try:
+        user = auth_manager.authenticate_user(username, password)
+        if user:
+            access_token = create_access_token(identity=str(user['id']))
+            return jsonify({"access_token": access_token}), 200
+        else:
+            return jsonify({"error": "Invalid username or password"}), 401
+    except Exception as e:
+        print(traceback.format_exc())
+        return jsonify({"error": f"An unexpected error occurred: {str(e)}"}), 500
+    
+@app.route("/profile", methods=["GET"])
+@jwt_required()
+def profile():
+    current_user = get_jwt_identity()
+
+    return jsonify(
+        message="Access granted",
+        user=auth_manager.get_user_by_id(current_user)
+    ), 200
+
 
 @app.route('/upload', methods=['POST'])
 def upload_data():
@@ -55,7 +136,7 @@ def word_frequencies():
         return jsonify({"error": "No data uploaded"}), 400
     
     try:
-        return jsonify(stat_obj.content_analysis()), 200
+        return jsonify(stat_obj.get_content_analysis()), 200
     except ValueError as e:
         return jsonify({"error": f"Malformed or missing data: {str(e)}"}), 400
     except Exception as e:
@@ -80,7 +161,7 @@ def get_time_analysis():
         return jsonify({"error": "No data uploaded"}), 400
     
     try:
-        return jsonify(stat_obj.time_analysis()), 200
+        return jsonify(stat_obj.get_time_analysis()), 200
     except ValueError as e:
         return jsonify({"error": f"Malformed or missing data: {str(e)}"}), 400
     except Exception as e:
@@ -93,7 +174,33 @@ def get_user_analysis():
         return jsonify({"error": "No data uploaded"}), 400
     
     try:
-        return jsonify(stat_obj.user_analysis()), 200
+        return jsonify(stat_obj.get_user_analysis()), 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("/stats/cultural", methods=["GET"])
+def get_cultural_analysis():
+    if stat_obj is None:
+        return jsonify({"error": "No data uploaded"}), 400
+    
+    try:
+        return jsonify(stat_obj.get_cultural_analysis()), 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("/stats/interaction", methods=["GET"])
+def get_interaction_analysis():
+    if stat_obj is None:
+        return jsonify({"error": "No data uploaded"}), 400
+    
+    try:
+        return jsonify(stat_obj.get_interactional_analysis()), 200
     except ValueError as e:
         return jsonify({"error": f"Malformed or missing data: {str(e)}"}), 400
     except Exception as e:

server/auth.py

@@ -0,0 +1,29 @@
+from db.database import PostgresConnector
+from flask_bcrypt import Bcrypt
+
+class AuthManager:
+    def __init__(self, db: PostgresConnector, bcrypt: Bcrypt):
+        self.db = db
+        self.bcrypt = bcrypt
+
+    def register_user(self, username, email, password):
+        hashed_password = self.bcrypt.generate_password_hash(password).decode("utf-8")
+
+        if self.db.get_user_by_email(email):
+            raise ValueError("Email already registered")
+        
+        if self.db.get_user_by_username(username):
+            raise ValueError("Username already taken")
+
+        self.db.save_user(username, email, hashed_password)
+
+    def authenticate_user(self, username, password):
+        user = self.db.get_user_by_username(username)
+        if user and self.bcrypt.check_password_hash(user['password_hash'], password):
+            return user
+        return None
+    
+    def get_user_by_id(self, user_id):
+        query = "SELECT id, username, email FROM users WHERE id = %s"
+        result = self.db.execute(query, (user_id,), fetch=True)
+        return result[0] if result else None

server/stat_gen.py

@@ -62,13 +62,18 @@ class StatGen:
         self.nlp.add_ner_cols()
     
     ## Public
-    def time_analysis(self) -> pd.DataFrame:
+
+
+    # topics over time
+    # emotions over time
+    def get_time_analysis(self) -> pd.DataFrame:
         return {
             "events_per_day": self.temporal_analysis.posts_per_day(),
             "weekday_hour_heatmap": self.temporal_analysis.heatmap()
         }
 
-    def content_analysis(self) -> dict:
+    # average topic duration
+    def get_content_analysis(self) -> dict:
         return {
             "word_frequencies": self.linguistic_analysis.word_frequencies(),
             "common_two_phrases": self.linguistic_analysis.ngrams(),
@@ -77,13 +82,31 @@ class StatGen:
             "reply_time_by_emotion": self.temporal_analysis.avg_reply_time_per_emotion()
         }
     
-    def user_analysis(self) -> dict:
+    # average emotion per user
+    # average chain length
+    def get_user_analysis(self) -> dict:
         return {
             "top_users": self.interaction_analysis.top_users(),
             "users": self.interaction_analysis.per_user_analysis(),
             "interaction_graph": self.interaction_analysis.interaction_graph()
         }
     
+    # average / max thread depth
+    # high engagment threads based on volume
+
+    def get_interactional_analysis(self) -> dict:
+        return {
+            "average_thread_depth": self.interaction_analysis.average_thread_depth(),
+            "average_thread_length_by_emotion": self.interaction_analysis.average_thread_length_by_emotion()
+        }
+    
+    # detect community jargon
+    # in-group and out-group linguistic markers
+    def get_cultural_analysis(self) -> dict:
+        return {
+            "identity_markers": self.linguistic_analysis.identity_markers()
+        }
+    
     def summary(self) -> dict:
         total_posts = (self.df["type"] == "post").sum()
         total_comments = (self.df["type"] == "comment").sum()