Merge pull request #853: Optimize backend imports and wire up Reader Identity UI

Author: devanshi14malhotra

backend/reader_identity/cluster_engine.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/embedding_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/routes.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/sentiment_engine.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)
 

frontend/js/app.js

@@ -3205,6 +3205,204 @@ document.addEventListener('DOMContentLoaded', async () => {
 
         loadExtendedStats();
 
+        // =====================================================================
+        // READER IDENTITY LOGIC
+        // Fetches reviews, determines archetype/mood/cluster, and renders states.
+        // =====================================================================
+        const renderErrorIdentityState = () => {
+            const identityContent = document.getElementById('reader-identity-content');
+            if (!identityContent) return;
+
+            identityContent.innerHTML = `
+                <div class="error-state" style="padding: 1.5rem; text-align: center; color: var(--text-muted); background: rgba(229, 57, 53, 0.05); border: 1px solid rgba(229, 57, 53, 0.2); border-radius: 10px;">
+                    <i class="fa-solid fa-triangle-exclamation" style="font-size: 2rem; color: #e53935; margin-bottom: 1rem; display: block;"></i>
+                    <p style="margin-bottom: 1rem;">Failed to load Reader Identity profile.</p>
+                    <button id="retry-identity-btn" class="action-btn-secondary" style="font-size: 0.8rem; padding: 4px 10px;">Retry</button>
+                </div>
+            `;
+
+            const retryBtn = document.getElementById('retry-identity-btn');
+            if (retryBtn) {
+                retryBtn.addEventListener('click', () => {
+                    loadReaderIdentity();
+                });
+            }
+        };
+
+        const fetchAndRenderArchetype = async (genres, reviewsList) => {
+            const identityContent = document.getElementById('reader-identity-content');
+            if (!identityContent) return;
+
+            const token = SafeStorage.get('bibliodrift_token');
+            const response = await fetch(`${MOOD_API_BASE}/reader-archetype`, {
+                method: 'POST',
+                headers: {
+                    'Authorization': `Bearer ${token}`,
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({ genres, reviews: reviewsList })
+            });
+
+            if (!response.ok) {
+                throw new Error(`Failed to fetch archetype (Status: ${response.status})`);
+            }
+
+            const data = await response.json();
+            if (!data.success || !data.reader_profile) {
+                throw new Error('API returned unsuccessful profile generation');
+            }
+
+            const profile = data.reader_profile;
+            const archetype = profile.archetype || "Unknown Reader";
+            const mood = profile.reader_mood || "Balanced Analytical Reader";
+            const sentimentScore = typeof profile.sentiment_score === 'number' ? profile.sentiment_score.toFixed(2) : '0.00';
+            const cluster = typeof profile.reader_cluster === 'number' ? (profile.reader_cluster === -1 ? 'Unclassified' : `Group #${profile.reader_cluster}`) : 'Unclassified';
+
+            const archetypeDescriptions = {
+                "Deep Thinker": "Drawn to philosophy, existential questions, and reflective/psychological themes.",
+                "Emotional Reader": "Connects deeply with romance, relationships, emotional journeys, and human feelings.",
+                "Dark Reader": "Enjoys crime, violence, psychological thrillers, horror, and mystery.",
+                "Adventurous Reader": "Seeks sci-fi, fantasy, action-packed adventures, and exploration."
+            };
+            const desc = archetypeDescriptions[archetype] || "Based on your current reading habits and preferences.";
+
+            identityContent.innerHTML = `
+                <div class="reader-identity-grid" style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 1.5rem; margin-top: 1rem; text-align: left;">
+                    <div class="identity-card" style="background: rgba(255, 255, 255, 0.03); padding: 1.2rem; border-radius: 10px; border: 1px solid rgba(255, 255, 255, 0.05);">
+                        <div style="font-size: 0.8rem; text-transform: uppercase; color: var(--text-muted); margin-bottom: 0.5rem; letter-spacing: 0.5px;">Reader Archetype</div>
+                        <div style="font-size: 1.3rem; font-weight: 600; color: var(--accent-gold); display: flex; align-items: center; gap: 8px;">
+                            <i class="fa-solid fa-brain" style="font-size: 1.3rem; margin: 0; color: var(--accent-gold);"></i> 
+                            <span id="identity-archetype">${archetype}</span>
+                        </div>
+                        <div style="font-size: 0.85rem; color: var(--text-muted); margin-top: 0.5rem;" id="identity-archetype-desc">
+                            ${desc}
+                        </div>
+                    </div>
+                    
+                    <div class="identity-card" style="background: rgba(255, 255, 255, 0.03); padding: 1.2rem; border-radius: 10px; border: 1px solid rgba(255, 255, 255, 0.05);">
+                        <div style="font-size: 0.8rem; text-transform: uppercase; color: var(--text-muted); margin-bottom: 0.5rem; letter-spacing: 0.5px;">Reader Mood</div>
+                        <div style="font-size: 1.3rem; font-weight: 600; color: var(--accent-gold); display: flex; align-items: center; gap: 8px;">
+                            <i class="fa-solid fa-masks-theater" style="font-size: 1.3rem; margin: 0; color: var(--accent-gold);"></i> 
+                            <span id="identity-mood">${mood}</span>
+                        </div>
+                        <div style="font-size: 0.85rem; color: var(--text-muted); margin-top: 0.5rem;">
+                            Sentiment Score: <strong id="identity-sentiment">${sentimentScore}</strong>
+                        </div>
+                    </div>
+
+                    <div class="identity-card" style="background: rgba(255, 255, 255, 0.03); padding: 1.2rem; border-radius: 10px; border: 1px solid rgba(255, 255, 255, 0.05);">
+                        <div style="font-size: 0.8rem; text-transform: uppercase; color: var(--text-muted); margin-bottom: 0.5rem; letter-spacing: 0.5px;">Reader Group</div>
+                        <div style="font-size: 1.3rem; font-weight: 600; color: var(--accent-gold); display: flex; align-items: center; gap: 8px;">
+                            <i class="fa-solid fa-people-group" style="font-size: 1.3rem; margin: 0; color: var(--accent-gold);"></i> 
+                            <span id="identity-cluster">${cluster}</span>
+                        </div>
+                        <div style="font-size: 0.85rem; color: var(--text-muted); margin-top: 0.5rem;">
+                            Based on review text analysis.
+                        </div>
+                    </div>
+                </div>
+            `;
+        };
+
+        const renderEmptyIdentityState = (genres) => {
+            const identityContent = document.getElementById('reader-identity-content');
+            if (!identityContent) return;
+
+            identityContent.innerHTML = `
+                <div class="empty-state" style="padding: 1rem 0; text-align: center; color: var(--text-muted);">
+                    <i class="fa-solid fa-circle-info" style="font-size: 2rem; color: var(--accent-gold); margin-bottom: 1rem; display: block;"></i>
+                    <p style="margin-bottom: 1.5rem;">We couldn't find any reviews in your profile yet. Add reviews to your finished books to unlock your reader identity!</p>
+                    <div style="max-width: 450px; margin: 0 auto; background: rgba(255, 255, 255, 0.02); padding: 1.5rem; border-radius: 10px; border: 1px solid rgba(255, 255, 255, 0.05); text-align: left;">
+                        <h4 style="color: var(--text-main); margin-bottom: 0.5rem;">Or try a quick test right now:</h4>
+                        <p style="font-size: 0.85rem; margin-bottom: 1rem;">Write a brief summary of the kinds of books you love reading (e.g. "I love deep space adventures with complex characters"):</p>
+                        <textarea id="onboarding-review-text" placeholder="I love exploring dark mystery novels and fast-paced thrillers..." style="width: 100%; height: 80px; padding: 10px; border-radius: 8px; border: 1px solid var(--border-color); background: var(--card-bg); color: var(--text-color); margin-bottom: 1rem; font-family: inherit; font-size: 0.9rem; resize: none;"></textarea>
+                        <button id="submit-onboarding-btn" class="action-btn-primary" style="font-size: 0.85rem; padding: 6px 15px;">Analyze Mood & Archetype</button>
+                    </div>
+                </div>
+            `;
+
+            const submitBtn = document.getElementById('submit-onboarding-btn');
+            if (submitBtn) {
+                submitBtn.addEventListener('click', async () => {
+                    const textInput = document.getElementById('onboarding-review-text').value.trim();
+                    if (!textInput) return;
+
+                    identityContent.innerHTML = `
+                        <div class="loading-state" style="padding: 2rem 0; text-align: center; color: var(--text-muted);">
+                            <i class="fa-solid fa-spinner fa-spin" style="font-size: 2rem; color: var(--accent-gold); margin-bottom: 1rem; display: block;"></i>
+                            <p>Analyzing your custom input...</p>
+                        </div>
+                    `;
+
+                    try {
+                        await fetchAndRenderArchetype(genres, [textInput]);
+                    } catch (error) {
+                        console.error('Error analyzing custom onboarding input:', error);
+                        renderErrorIdentityState();
+                    }
+                });
+            }
+        };
+
+        const loadReaderIdentity = async () => {
+            const identityContent = document.getElementById('reader-identity-content');
+            if (!identityContent) return;
+
+            identityContent.innerHTML = `
+                <div class="loading-state" style="padding: 2rem 0; text-align: center; color: var(--text-muted);">
+                    <i class="fa-solid fa-spinner fa-spin" style="font-size: 2rem; color: var(--accent-gold); margin-bottom: 1rem; display: block;"></i>
+                    <p>Analyzing your reading profile and reviews...</p>
+                </div>
+            `;
+
+            const token = SafeStorage.get('bibliodrift_token');
+            if (!token) {
+                identityContent.innerHTML = `
+                    <div class="error-state" style="padding: 1.5rem; text-align: center; color: var(--text-muted);">
+                        <p>Please log in to view your Reader Identity.</p>
+                    </div>
+                `;
+                return;
+            }
+
+            try {
+                // 1. Fetch user reviews
+                const reviewsResponse = await fetch(`${MOOD_API_BASE}/users/${user.id}/reviews`, {
+                    headers: { 'Authorization': `Bearer ${token}` }
+                });
+
+                if (!reviewsResponse.ok) {
+                    throw new Error(`Failed to fetch reviews (Status: ${reviewsResponse.status})`);
+                }
+
+                const reviewsData = await reviewsResponse.json();
+                const reviewsList = (reviewsData.reviews || []).map(r => r.review_text).filter(Boolean);
+
+                // Derive genres
+                const allBooks = [
+                    ...(libManager.library.current || []),
+                    ...(libManager.library.want || []),
+                    ...(libManager.library.finished || [])
+                ];
+                const genres = Array.from(new Set(
+                    allBooks.flatMap(book => book.volumeInfo?.categories || [])
+                ));
+
+                // 2. Render empty/onboarding or loaded state
+                if (reviewsList.length === 0) {
+                    renderEmptyIdentityState(genres);
+                } else {
+                    await fetchAndRenderArchetype(genres, reviewsList);
+                }
+            } catch (error) {
+                console.error('Error loading reader identity:', error);
+                renderErrorIdentityState();
+            }
+        };
+
+        // Initialize reader identity loading
+        loadReaderIdentity();
+
         // Progress Bar Calculation
         const barFinished = document.getElementById('bar-finished');
         const barCurrent = document.getElementById('bar-current');