fix: resolve upstream sync conflicts and transition avatar raw emojis to fontawesome structural icons

Author: Aarushi Keshari

.DS_Store

@@ -0,0 +1,59 @@
+name: CI – Run Tests
+
+on:
+  push:
+    branches: ["main", "master"]
+  pull_request:
+    branches: ["main", "master"]
+
+jobs:
+  test:
+    name: pytest
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+          cache: "pip"
+
+      - name: Install dependencies
+        run: |
+          pip install --upgrade pip
+          pip install -r backend/requirements.txt
+          pip install pytest pytest-cov
+          pip install flask-wtf bleach Pillow beautifulsoup4 "pydantic[email]" pytest pytest-cov
+
+      - name: Run pytest
+        env:
+          GOOGLE_BOOKS_API_KEY: "ci-fake-key"
+          OPENAI_API_KEY: "ci-fake-key"
+          GROQ_API_KEY: "ci-fake-key"
+          GEMINI_API_KEY: "ci-fake-key"
+          FLASK_ENV: testing
+          PYTHONPATH: ".:backend"
+        run: |
+          python -m pytest tests/ \
+            --ignore=tests/test_llm.py \
+            --ignore=tests/test_mood_improvements.py \
+            --ignore=tests/test_mood_cache_db.py \
+            --ignore=tests/test_category_cache.py \
+            --ignore=tests/test_env_validation.py \
+            --ignore=tests/test_purchase_links.py \
+            -v --tb=short --cov=backend
+
+      - name: Install dependencies
+        run: |
+          pip install --upgrade pip
+          pip install torch --index-url https://download.pytorch.org/whl/cpu
+          pip install -r backend/requirements.txt
+          pip install flask-wtf bleach Pillow pytest pytest-cov
+
+      - name: Install dependencies
+        run: |
+          pip install --upgrade pip
+          pip install torch --index-url https://download.pytorch.org/whl/cpu
+          pip install -r backend/requirements.txt
+          pip install flask-wtf bleach Pillow beautifulsoup4 pytest pytest-cov

.github/workflows/ci.yml

@@ -6,10 +6,10 @@ mood_analysis/mood_cache.json
 *.cache
 
 # Environment variables
-.env
-.env.local
-.env.production
-.env.development
+/.env
+/.env.local
+/.env.production
+/.env.development
 
 # Sensitive local files
 *.token
@@ -32,3 +32,4 @@ instance/
 *.db
 # Virtual Environment
 venv/
+.venv/

.gitignore

@@ -14,7 +14,7 @@
 from flask_wtf.csrf import CSRFProtect, generate_csrf, CSRFError
 from sqlalchemy.exc import SQLAlchemyError
 from dotenv import load_dotenv
-from backend.spine_generator import create_spine
+from spine_generator import create_spine
 import os
 import requests
 import secrets
@@ -25,8 +25,8 @@
 import sys
 import os
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-from sanitizer import sanitize_payload
-from reader_identity.routes import reader_identity_bp
+from backend.sanitizer import sanitize_payload
+from backend.reader_identity.routes import reader_identity_bp
 
 # Load environment variables from config directory based on APP_ENV
 env = os.getenv('APP_ENV', 'development')
@@ -2405,20 +2405,9 @@ def delete_price_alert(alert_id):
 with app.app_context():
     db.create_all()
 
-@app.route('/api/books', methods=['GET'])
-def get_books():
-    query = request.args.get('q')
-    max_results = request.args.get('maxResults', 10)
-
-    API_KEY = os.getenv("GOOGLE_BOOKS_API_KEY")
-    url = f"https://www.googleapis.com/books/v1/volumes?q={query}&maxResults={max_results}&key={API_KEY}"
-
-    try:
-        response = requests.get(url)
-        data = response.json()
-        return jsonify(data)
-    except Exception as e:
-        return jsonify({"error": "Failed to fetch books"}), 500
+# NOTE: Book search is performed directly from the frontend using the Google Books API.
+# The old backend proxy endpoint /api/books has been removed to avoid unnecessary
+# load on the backend server.
 
 if __name__ == '__main__':
     server_config = app_config.server

backend/app.py

@@ -166,7 +166,9 @@ def from_env(cls) -> 'GoogleOAuthConfig':
             redirect_uri=os.getenv('GOOGLE_OAUTH_REDIRECT_URI'),
             frontend_redirect_url=os.getenv('FRONTEND_URL', 'http://127.0.0.1:5500/frontend/pages/library.html'),
             scope=os.getenv('GOOGLE_OAUTH_SCOPE', 'openid email profile')
-          
+        )
+    
+@dataclass
 class EmailConfig:
     """Email service configuration (e.g., SendGrid, Mailgun)."""
     api_key: Optional[str]

backend/config.py

@@ -1,8 +1,6 @@
 """
 Handles unsupervised reader clustering.
 """
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.cluster import KMeans
 """
 Uses TF-IDF and KMeans clustering for grouping readers.
 """
@@ -11,29 +9,39 @@
 class ClusterEngine:
 
     def __init__(self):
-
-        self.vectorizer = TfidfVectorizer(
-            stop_words="english"
-        )
+        self.vectorizer = None
 
     def cluster_reviews(self, reviews):
-
-        vectors = self.vectorizer.fit_transform(
-            reviews
-        )
-
-        cluster_count = min(
-            len(reviews),
-            4
-        )
-
-        model = KMeans(
-            n_clusters=cluster_count,
-            random_state=42
-        )
-
-        clusters = model.fit_predict(
-            vectors
-        )
-
-        return clusters.tolist()
+        if not reviews:
+            return [-1]
+
+        try:
+            from sklearn.cluster import KMeans
+            if self.vectorizer is None:
+                from sklearn.feature_extraction.text import TfidfVectorizer
+                self.vectorizer = TfidfVectorizer(
+                    stop_words="english"
+                )
+            vectors = self.vectorizer.fit_transform(
+                reviews
+            )
+
+            cluster_count = min(
+                len(reviews),
+                4
+            )
+            if cluster_count == 0:
+                return [-1]
+
+            model = KMeans(
+                n_clusters=cluster_count,
+                random_state=42
+            )
+
+            clusters = model.fit_predict(
+                vectors
+            )
+
+            return clusters.tolist()
+        except Exception:
+            return [-1]

backend/reader_identity/cluster_engine.py

@@ -1,8 +1,6 @@
 """
 Handles semantic similarity using sentence embeddings.
 """
-from sentence_transformers import SentenceTransformer
-from sklearn.metrics.pairwise import cosine_similarity
 
 """
 Generates embeddings and compares semantic similarity.
@@ -12,16 +10,18 @@
 class EmbeddingEngine:
 
     def __init__(self):
-
-        self.model = SentenceTransformer(
-            "all-MiniLM-L6-v2"
-        )
+        self.model = None
 
     def get_embedding(self, text):
-
+        if self.model is None:
+            from sentence_transformers import SentenceTransformer
+            self.model = SentenceTransformer(
+                "all-MiniLM-L6-v2"
+            )
         return self.model.encode([text])
 
     def compare_texts(self, text1, text2):
+        from sklearn.metrics.pairwise import cosine_similarity
 
         embedding1 = self.get_embedding(text1)
 

backend/reader_identity/embedding_engine.py

@@ -14,50 +14,9 @@
     "/api/v1/reader-archetype",
     methods=["POST"]
 )
-
 def reader_archetype():
-
     try:
-
         data = request.get_json()
-
-        if not data:
-
-            return jsonify({
-                "success": False,
-                "error": "Missing JSON body"
-            }), 400
-
-        genres = data.get("genres", [])
-
-        reviews = data.get("reviews", [])
-
-        if not reviews:
-
-            return jsonify({
-                "success": False,
-                "error": "Reviews are required"
-            }), 400
-
-        result = generator.generate_profile(
-            genres,
-            reviews
-        )
-
-        return jsonify(result)
-
-    except Exception as e:
-
-        return jsonify({
-            "success": False,
-            "error": str(e)
-        }), 500
-def reader_archetype():
-
-    try:
-
-        data = request.get_json()
-
         if not data:
             return jsonify({
                 "success": False,
@@ -83,11 +42,9 @@ def reader_archetype():
             genres,
             reviews
         )
-
         return jsonify(result), 200
 
     except Exception as e:
-
         return jsonify({
             "success": False,
             "error": str(e)

backend/reader_identity/routes.py

@@ -2,15 +2,14 @@
 Handles sentiment analysis for reader reviews.
 """
 
-from textblob import TextBlob
-
 """
 Performs sentiment-based emotional profiling.
 """
 
 class SentimentEngine:
 
     def analyze_reviews(self, reviews):
+        from textblob import TextBlob
 
         combined_text = " ".join(reviews)