feat: add manual classification with AI topic suggestions in question entry v2

- Introduced nvidia_prompts.py with specialized NEET syllabus prompts for Biology, Chemistry, Physics, and Mathematics.
- Added /get_topic_suggestions route to fetch AI-powered chapter suggestions using NVIDIA NIM.
- Implemented manual classification wizard in question_entry_v2.html with range selection and topic entry modals.
- Added /classified/update_single route for updating individual question metadata.

classifier_routes.py

@@ -8,12 +8,169 @@ from processing import resize_image_if_needed, call_nim_ocr_api
 from gemini_classifier import classify_questions_with_gemini
 from gemma_classifier import GemmaClassifier
 from nova_classifier import classify_questions_with_nova
+import requests
+from nvidia_prompts import BIOLOGY_PROMPT_TEMPLATE, CHEMISTRY_PROMPT_TEMPLATE, PHYSICS_PROMPT_TEMPLATE, MATHEMATICS_PROMPT_TEMPLATE
 
 classifier_bp = Blueprint('classifier_bp', __name__)
 
 # Instantiate classifiers
 gemma_classifier = GemmaClassifier()
 
+def get_nvidia_prompt(subject, input_questions):
+    if subject.lower() == 'biology':
+        return BIOLOGY_PROMPT_TEMPLATE.format(input_questions=input_questions)
+    elif subject.lower() == 'chemistry':
+        return CHEMISTRY_PROMPT_TEMPLATE.format(input_questions=input_questions)
+    elif subject.lower() == 'physics':
+        return PHYSICS_PROMPT_TEMPLATE.format(input_questions=input_questions)
+    elif subject.lower() == 'mathematics':
+        return MATHEMATICS_PROMPT_TEMPLATE.format(input_questions=input_questions)
+    return None
+
+@classifier_bp.route('/get_topic_suggestions', methods=['POST'])
+@login_required
+def get_topic_suggestions():
+    data = request.json
+    question_text = data.get('question_text')
+    image_id = data.get('image_id')
+    subject = data.get('subject')
+    
+    if not subject:
+        return jsonify({'error': 'Subject is required'}), 400
+        
+    # If text is missing but we have image_id, try to get from DB or run OCR
+    if not question_text and image_id:
+        try:
+            conn = get_db_connection()
+            # Check DB first
+            row = conn.execute('SELECT question_text, processed_filename, session_id FROM questions q JOIN images i ON q.image_id = i.id WHERE i.id = ?', (image_id,)).fetchone()
+            
+            if row:
+                if row['question_text']:
+                    question_text = row['question_text']
+                else:
+                    # Run OCR
+                    processed_filename = row['processed_filename']
+                    session_id = row['session_id']
+                    if processed_filename:
+                        image_path = os.path.join(current_app.config['PROCESSED_FOLDER'], processed_filename)
+                        if os.path.exists(image_path):
+                            image_bytes = resize_image_if_needed(image_path)
+                            ocr_result = call_nim_ocr_api(image_bytes)
+                            
+                            if ocr_result.get('data') and ocr_result['data'][0].get('text_detections'):
+                                question_text = " ".join(item['text_prediction']['text'] for item in ocr_result['data'][0]['text_detections'])
+                                # Save back to DB
+                                conn.execute('UPDATE questions SET question_text = ? WHERE image_id = ?', (question_text, image_id))
+                                conn.commit()
+            conn.close()
+        except Exception as e:
+            current_app.logger.error(f"Error fetching/OCRing text for image {image_id}: {e}")
+            return jsonify({'error': f"OCR failed: {str(e)}"}), 500
+
+    if not question_text:
+        return jsonify({'error': 'Could not obtain question text (OCR failed or no text found).'}), 400
+
+    # Prepare prompt
+    # The prompt expects "Input Questions: [Insert ...]". 
+    # We will format the single question as "1. {text}" to match the pattern somewhat, 
+    # though the prompt handles raw text too.
+    input_formatted = f"1. {question_text}"
+    prompt_content = get_nvidia_prompt(subject, input_formatted)
+    
+    if not prompt_content:
+        return jsonify({'error': f'Unsupported subject: {subject}'}), 400
+
+    # Call NVIDIA API
+    nvidia_api_key = os.environ.get('NVIDIA_API_KEY')
+    if not nvidia_api_key:
+         return jsonify({'error': 'NVIDIA_API_KEY not set'}), 500
+
+    invoke_url = 'https://integrate.api.nvidia.com/v1/chat/completions'
+    headers = {
+        'Authorization': f'Bearer {nvidia_api_key}',
+        'Accept': 'application/json',
+        'Content-Type': 'application/json'
+    }
+    
+    payload = {
+        "model": "nvidia/nemotron-3-nano-30b-a3b",
+        "messages": [
+            {
+                "content": prompt_content,
+                "role": "user"
+            }
+        ],
+        "temperature": 0.1, # Low temp for deterministic classification
+        "top_p": 1,
+        "max_tokens": 1024,
+        "stream": False
+    }
+
+    try:
+        response = requests.post(invoke_url, headers=headers, json=payload, timeout=30)
+        response.raise_for_status()
+        result = response.json()
+        
+        content = result['choices'][0]['message']['content']
+        
+        # Parse JSON from content (it might be wrapped in markdown code blocks)
+        if "```json" in content:
+            content = content.split("```json")[1].split("```")[0].strip()
+        elif "```" in content:
+             content = content.split("```")[1].split("```")[0].strip()
+             
+        data = json.loads(content)
+        
+        # Extract the chapter title
+        chapter_title = "Unclassified"
+        if data.get('data') and len(data['data']) > 0:
+            chapter_title = data['data'][0].get('chapter_title', 'Unclassified')
+            
+        return jsonify({'success': True, 'chapter_title': chapter_title, 'full_response': data})
+
+    except Exception as e:
+        current_app.logger.error(f"NVIDIA API Error: {e}")
+        return jsonify({'error': str(e)}), 500
+
+@classifier_bp.route('/classified/update_single', methods=['POST'])
+@login_required
+def update_question_classification_single():
+    data = request.json
+    image_id = data.get('image_id')
+    subject = data.get('subject')
+    chapter = data.get('chapter')
+
+    if not image_id:
+        return jsonify({'error': 'Image ID is required'}), 400
+
+    try:
+        conn = get_db_connection()
+        # Security: Check ownership via session -> images
+        image_owner = conn.execute("""
+            SELECT s.user_id 
+            FROM images i 
+            JOIN sessions s ON i.session_id = s.id 
+            WHERE i.id = ?
+        """, (image_id,)).fetchone()
+
+        if not image_owner or image_owner['user_id'] != current_user.id:
+            conn.close()
+            return jsonify({'error': 'Unauthorized'}), 403
+
+        conn.execute(
+            'UPDATE questions SET subject = ?, chapter = ? WHERE image_id = ?',
+            (subject, chapter, image_id)
+        )
+        conn.commit()
+        conn.close()
+        return jsonify({'success': True})
+    except Exception as e:
+        current_app.logger.error(f"Error updating question classification for image {image_id}: {e}")
+        return jsonify({'error': str(e)}), 500
+
+
+
 @classifier_bp.route('/classified/edit')
 @login_required
 def edit_classified_questions():

nvidia_prompts.py

@@ -0,0 +1,276 @@
+
+BIOLOGY_PROMPT_TEMPLATE = """**System Role:** You are a specialized Biology question classifier for NEET exams. Your task is to map questions to their corresponding chapter from the NCERT Biology syllabus.
+
+**Syllabus Chapters (Use these exact titles):**
+
+**Class XI & XII Combined List**
+
+1. The Living World
+2. Biological Classification
+3. Plant Kingdom
+4. Animal Kingdom
+5. Morphology of Flowering Plants
+6. Anatomy of Flowering Plants
+7. Structural Organisation in Animals
+8. Cell: The Unit of Life
+9. Biomolecules
+10. Cell Cycle and Cell Division
+11. Photosynthesis in Higher Plants
+12. Respiration in Plants
+13. Plant Growth and Development
+14. Breathing and Exchange of Gases
+15. Body Fluids and Circulation
+16. Excretory Products and their Elimination
+17. Locomotion and Movement
+18. Neural Control and Coordination
+19. Chemical Coordination and Integration
+20. Sexual Reproduction in Flowering Plants
+21. Human Reproduction
+22. Reproductive Health
+23. Principles of Inheritance and Variation
+24. Molecular Basis of Inheritance
+25. Evolution
+26. Health and Disease
+27. Improvement in Food Production
+28. Microbes in Human Welfare
+29. Biotechnology - Principles and Processes
+30. Biotechnology and Its Applications
+31. Organisms and Populations
+32. Ecosystem
+33. Biodiversity and Its Conservation
+
+**Classification Guidelines:**
+
+1. **Scope**: Analyze the input question. If the question is NOT related to Biology, mark the chapter as 'Unclassified'.
+2. **Mapping**: Identify the single most relevant chapter from the list above.
+3. **Multi-Chapter**: If a question explicitly spans 2 distinct chapters, include both.
+
+**Output JSON Schema:**
+
+```json
+{
+  "data": [
+    {
+      "index": <integer matching question number>,
+      "subject": "Biology",
+      "chapter_index": <chapter number or 0>,
+      "chapter_title": "<exact chapter title from list or 'Unclassified'>",
+      "original_question_text": "<complete original question>",
+      "confidence": <0.0 to 1.0>
+    }
+  ],
+  "success": [true]
+}
+
+```
+
+**Input Questions:**
+{input_questions}
+"""
+
+CHEMISTRY_PROMPT_TEMPLATE = """**System Role:** You are a specialized Chemistry question classifier for NEET/JEE exams. Your task is to map questions to their corresponding chapter from the NCERT Chemistry
+syllabus.
+
+**Syllabus Chapters (Use these exact titles):**
+
+**Class XI**
+
+1. Some Basic Concepts of Chemistry
+2. Structure of Atom
+3. Classification of Elements and Periodicity in Properties
+4. Chemical Bonding and Molecular Structure
+5. States of Matter: Gases and Liquids
+6. Thermodynamics
+7. Equilibrium
+8. Redox Reactions
+9. Hydrogen
+10. The s-Block Elements
+11. The p-Block Elements (Group 13 and 14)
+12. Organic Chemistry – Some Basic Principles and Techniques (GOC)
+13. Hydrocarbons
+14. Environmental Chemistry
+
+**Class XII**
+
+1. The Solid State
+2. Solutions
+3. Electrochemistry
+4. Chemical Kinetics
+5. Surface Chemistry
+6. General Principles and Processes of Isolation of Elements (Metallurgy)
+7. The p-Block Elements (Group 15 to 18)
+8. The d- and f- Block Elements
+9. Coordination Compounds
+10. Haloalkanes and Haloarenes
+11. Alcohols, Phenols and Ethers
+12. Aldehydes, Ketones and Carboxylic Acids
+13. Amines
+14. Biomolecules
+15. Polymers
+16. Chemistry in Everyday Life
+
+**Classification Guidelines:**
+
+1. **Scope**: Analyze the input question. If the question is NOT related to Chemistry, mark the chapter as 'Unclassified'.
+2. **Mapping**: Identify the single most relevant chapter from the list above.
+3. **Multi-Chapter**: If a question explicitly spans 2 distinct chapters, include both.
+
+**Output JSON Schema:**
+
+```json
+{
+  "data": [
+    {
+      "index": <integer matching question number>,
+      "subject": "Chemistry",
+      "chapter_index": <chapter number or 0>,
+      "chapter_title": "<exact chapter title from list or 'Unclassified'>",
+      "original_question_text": "<complete original question>",
+      "confidence": <0.0 to 1.0>
+    }
+  ],
+  "success": [true]
+}
+
+```
+
+**Input Questions:**
+{input_questions}
+"""
+
+PHYSICS_PROMPT_TEMPLATE = """**System Role:** You are a specialized Physics question classifier for NEET/JEE exams. Your task is to map questions to their corresponding chapter from the NCERT Physics syllabus.
+
+**Syllabus Chapters (Use these exact titles):**
+
+**Class XI**
+
+1. Units and Measurements
+2. Motion in a Straight Line
+3. Motion in a Plane
+4. Laws of Motion
+5. Work, Energy and Power
+6. System of Particles and Rotational Motion
+7. Gravitation
+8. Mechanical Properties of Solids
+9. Mechanical Properties of Fluids
+10. Thermal Properties of Matter
+11. Thermodynamics
+12. Kinetic Theory
+13. Oscillations
+14. Waves
+
+**Class XII**
+
+1. Electric Charges and Fields
+2. Electrostatic Potential and Capacitance
+3. Current Electricity
+4. Moving Charges and Magnetism
+5. Magnetism and Matter
+6. Electromagnetic Induction
+7. Alternating Current
+8. Electromagnetic Waves
+9. Ray Optics and Optical Instruments
+10. Wave Optics
+11. Dual Nature of Radiation and Matter
+12. Atoms
+13. Nuclei
+14. Semiconductor Electronics: Materials, Devices and Simple Circuits
+15. Communication Systems
+
+**Classification Guidelines:**
+
+1. **Scope**: Analyze the input question. If the question is NOT related to Physics, mark the chapter as 'Unclassified'.
+2. **Mapping**: Identify the single most relevant chapter from the list above.
+3. **Multi-Chapter**: If a question explicitly spans 2 distinct chapters, include both.
+
+**Output JSON Schema:**
+
+```json
+{
+  "data": [
+    {
+      "index": <integer matching question number>,
+      "subject": "Physics",
+      "chapter_index": <chapter number or 0>,
+      "chapter_title": "<exact chapter title from list or 'Unclassified'>",
+      "original_question_text": "<complete original question>",
+      "confidence": <0.0 to 1.0>
+    }
+  ],
+  "success": [true]
+}
+
+```
+
+**Input Questions:**
+{input_questions}
+"""
+
+MATHEMATICS_PROMPT_TEMPLATE = """**System Role:** You are a specialized Mathematics question classifier for JEE exams. Your task is to map questions to their corresponding chapter from the NCERT Mathematics
+syllabus.
+
+**Syllabus Chapters (Use these exact titles):**
+
+**Class XI**
+
+1. Sets
+2. Relations and Functions
+3. Trigonometric Functions
+4. Principle of Mathematical Induction
+5. Complex Numbers and Quadratic Equations
+6. Linear Inequalities
+7. Permutations and Combinations
+8. Binomial Theorem
+9. Sequences and Series
+10. Straight Lines
+11. Conic Sections
+12. Introduction to Three Dimensional Geometry
+13. Limits and Derivatives
+14. Mathematical Reasoning
+15. Statistics
+16. Probability
+
+**Class XII**
+
+1. Relations and Functions
+2. Inverse Trigonometric Functions
+3. Matrices
+4. Determinants
+5. Continuity and Differentiability
+6. Application of Derivatives
+7. Integrals
+8. Application of Integrals
+9. Differential Equations
+10. Vector Algebra
+11. Three Dimensional Geometry
+12. Linear Programming
+13. Probability
+
+**Classification Guidelines:**
+
+1. **Scope**: Analyze the input question. If the question is NOT related to Mathematics, mark the chapter as 'Unclassified'.
+2. **Mapping**: Identify the single most relevant chapter from the list above.
+3. **Multi-Chapter**: If a question explicitly spans 2 distinct chapters, include both.
+
+**Output JSON Schema:**
+
+```json
+{
+  "data": [
+    {
+      "index": <integer matching question number>,
+      "subject": "Mathematics",
+      "chapter_index": <chapter number or 0>,
+      "chapter_title": "<exact chapter title from list or 'Unclassified'>",
+      "original_question_text": "<complete original question>",
+      "confidence": <0.0 to 1.0>
+    }
+  ],
+  "success": [true]
+}
+
+```
+
+**Input Questions:**
+{input_questions}
+"""

templates/question_entry_v2.html

@@ -82,6 +82,9 @@
                         <span class="spinner-border spinner-border-sm d-none" role="status" aria-hidden="true"></span>
                         <span class="extract-text">Extract & Classify All</span>
                     </button>
+                    <button type="button" class="btn btn-warning ms-2" data-bs-toggle="modal" data-bs-target="#manualClassificationModal">
+                        <i class="bi bi-list-check"></i> Manual Classification
+                    </button>
                 </div>
                 {% endif %}
 
@@ -270,6 +273,75 @@
         </div>
     </div>
 </div>
+
+<!-- Modal 1: Range & Subject -->
+<div class="modal fade" id="manualClassificationModal" tabindex="-1">
+    <div class="modal-dialog">
+        <div class="modal-content bg-dark text-white">
+            <div class="modal-header">
+                <h5 class="modal-title">Manual Classification Setup</h5>
+                <button type="button" class="btn-close btn-close-white" data-bs-dismiss="modal"></button>
+            </div>
+            <div class="modal-body">
+                <div class="mb-3">
+                    <label class="form-label">Select Subject</label>
+                    <select id="manual_subject" class="form-select bg-dark text-white border-secondary">
+                        <option value="Biology">Biology</option>
+                        <option value="Chemistry">Chemistry</option>
+                        <option value="Physics">Physics</option>
+                        <option value="Mathematics">Mathematics</option>
+                    </select>
+                </div>
+                <div class="mb-3">
+                    <label class="form-label">Question Range (e.g., "1-5, 8, 10-12")</label>
+                    <input type="text" id="manual_range" class="form-control bg-dark text-white border-secondary" placeholder="Leave empty for all questions">
+                    <small class="text-muted">Enter ranges like 1-5 or comma separated values.</small>
+                </div>
+            </div>
+            <div class="modal-footer">
+                <button type="button" class="btn btn-primary" onclick="startManualClassification()">Start Classification</button>
+            </div>
+        </div>
+    </div>
+</div>
+
+<!-- Modal 2: Topic Wizard -->
+<div class="modal fade" id="topicSelectionModal" tabindex="-1" data-bs-backdrop="static">
+    <div class="modal-dialog modal-lg">
+        <div class="modal-content bg-dark text-white">
+            <div class="modal-header">
+                <h5 class="modal-title">Classify Question <span id="topic_q_num"></span></h5>
+                <button type="button" class="btn-close btn-close-white" data-bs-dismiss="modal"></button>
+            </div>
+            <div class="modal-body">
+                <div class="text-center mb-3">
+                    <img id="topic_q_img" src="" class="img-fluid rounded" style="max-height: 200px; object-fit: contain;">
+                </div>
+                <div class="mb-3">
+                    <label class="form-label">Subject: <span id="topic_subject_display" class="fw-bold text-info"></span></label>
+                </div>
+                <div class="mb-3">
+                    <label class="form-label">Chapter / Topic</label>
+                    <div class="input-group">
+                        <input type="text" id="topic_input" class="form-control bg-dark text-white border-secondary" placeholder="Enter topic...">
+                        <button class="btn btn-outline-info" type="button" id="btn_get_suggestion" onclick="getAiSuggestion()">
+                            <span class="spinner-border spinner-border-sm d-none" role="status"></span>
+                            ✨ Get AI Suggestion
+                        </button>
+                    </div>
+                </div>
+                <div id="ai_suggestion_result" class="alert alert-info d-none mt-2"></div>
+            </div>
+            <div class="modal-footer d-flex justify-content-between">
+                <button type="button" class="btn btn-secondary" onclick="prevTopicQuestion()">Previous</button>
+                <div>
+                    <span id="topic_progress" class="me-3 text-muted"></span>
+                    <button type="button" class="btn btn-primary" onclick="nextTopicQuestion()">Next</button>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
 {% endblock %}
 
 {% block scripts %}
@@ -902,6 +974,183 @@
         }
     }
 
+    // --- Manual Classification Logic ---
+    let manualQuestionsList = [];
+    let currentManualIndex = 0;
+    let manualSubject = "";
+
+    function parseRange(rangeStr, maxVal) {
+        const indices = new Set();
+        const parts = rangeStr.split(',');
+        for (let part of parts) {
+            part = part.trim();
+            if (!part) continue;
+            if (part.includes('-')) {
+                const [start, end] = part.split('-').map(Number);
+                if (!isNaN(start) && !isNaN(end)) {
+                    for (let i = start; i <= end; i++) indices.add(i);
+                }
+            } else {
+                const num = Number(part);
+                if (!isNaN(num)) indices.add(num);
+            }
+        }
+        // Filter valid 1-based indices and convert to 0-based
+        return Array.from(indices).filter(i => i >= 1 && i <= maxVal).map(i => i - 1).sort((a, b) => a - b);
+    }
+
+    function startManualClassification() {
+        manualSubject = document.getElementById('manual_subject').value;
+        const rangeStr = document.getElementById('manual_range').value;
+        
+        if (rangeStr.trim() === "") {
+             manualQuestionsList = Array.from({length: numImages}, (_, i) => i);
+        } else {
+             manualQuestionsList = parseRange(rangeStr, numImages);
+        }
+
+        if (manualQuestionsList.length === 0) {
+            alert("No valid questions selected in range.");
+            return;
+        }
+
+        currentManualIndex = 0;
+        const modal1 = bootstrap.Modal.getInstance(document.getElementById('manualClassificationModal'));
+        modal1.hide();
+        
+        const modal2 = new bootstrap.Modal(document.getElementById('topicSelectionModal'));
+        modal2.show();
+        
+        loadTopicQuestion();
+    }
+
+    function loadTopicQuestion() {
+        const globalIndex = manualQuestionsList[currentManualIndex];
+        // Find the fieldset for this index
+        const fieldset = document.querySelector(`fieldset[data-question-index="${globalIndex}"]`);
+        const imageId = fieldset.querySelector('input[name^="image_id_"]').value;
+        const qNum = fieldset.querySelector('input[name^="question_number_"]').value;
+        const img = fieldset.querySelector('img');
+        const imgUrl = img ? img.src : '';
+        
+        // Populate Modal
+        document.getElementById('topic_q_num').innerText = qNum;
+        const modalImg = document.getElementById('topic_q_img');
+        if (imgUrl) {
+            modalImg.src = imgUrl;
+            modalImg.style.display = 'inline-block';
+        } else {
+            modalImg.style.display = 'none';
+        }
+
+        document.getElementById('topic_subject_display').innerText = manualSubject;
+        
+        // Get existing chapter if any
+        const chapterSpan = document.getElementById(`chapter_${imageId}`);
+        const currentChapter = chapterSpan ? chapterSpan.innerText : '';
+        document.getElementById('topic_input').value = (currentChapter && currentChapter !== 'N/A' && currentChapter !== 'Unclassified') ? currentChapter : '';
+        
+        document.getElementById('topic_progress').innerText = `${currentManualIndex + 1} / ${manualQuestionsList.length}`;
+        const resultDiv = document.getElementById('ai_suggestion_result');
+        resultDiv.classList.add('d-none');
+        resultDiv.innerText = '';
+        
+        // Reset button state
+        const btn = document.getElementById('btn_get_suggestion');
+        btn.disabled = false;
+        btn.innerHTML = `<span class="spinner-border spinner-border-sm d-none" role="status"></span> ✨ Get AI Suggestion`;
+    }
+
+    async function nextTopicQuestion() {
+        // Save current
+        await saveCurrentTopic();
+        
+        if (currentManualIndex < manualQuestionsList.length - 1) {
+            currentManualIndex++;
+            loadTopicQuestion();
+        } else {
+            // Finish
+            const modal2 = bootstrap.Modal.getInstance(document.getElementById('topicSelectionModal'));
+            modal2.hide();
+            showStatus("Manual classification completed for selected range.", "success");
+        }
+    }
+
+    function prevTopicQuestion() {
+         if (currentManualIndex > 0) {
+            currentManualIndex--;
+            loadTopicQuestion();
+        }
+    }
+
+    async function saveCurrentTopic() {
+        const globalIndex = manualQuestionsList[currentManualIndex];
+        const fieldset = document.querySelector(`fieldset[data-question-index="${globalIndex}"]`);
+        const imageId = fieldset.querySelector('input[name^="image_id_"]').value;
+        const topic = document.getElementById('topic_input').value;
+
+        // Update UI immediately
+        const subjectSpan = document.getElementById(`subject_${imageId}`);
+        if(subjectSpan) subjectSpan.innerText = manualSubject;
+        
+        const chapterSpan = document.getElementById(`chapter_${imageId}`);
+        if(chapterSpan) chapterSpan.innerText = topic || 'Unclassified';
+
+        // Send to backend
+        try {
+            await fetch('/classified/update_single', {
+                method: 'POST',
+                headers: { 'Content-Type': 'application/json' },
+                body: JSON.stringify({
+                    image_id: imageId,
+                    subject: manualSubject,
+                    chapter: topic
+                })
+            });
+        } catch (e) {
+            console.error("Failed to save topic", e);
+        }
+    }
+
+    async function getAiSuggestion() {
+        const globalIndex = manualQuestionsList[currentManualIndex];
+        const fieldset = document.querySelector(`fieldset[data-question-index="${globalIndex}"]`);
+        const imageId = fieldset.querySelector('input[name^="image_id_"]').value;
+        const btn = document.getElementById('btn_get_suggestion');
+        const resultDiv = document.getElementById('ai_suggestion_result');
+        
+        btn.disabled = true;
+        btn.querySelector('.spinner-border').classList.remove('d-none');
+
+        try {
+            const response = await fetch('/get_topic_suggestions', {
+                method: 'POST',
+                headers: { 'Content-Type': 'application/json' },
+                body: JSON.stringify({
+                    image_id: imageId,
+                    subject: manualSubject
+                })
+            });
+            const data = await response.json();
+            
+            if (data.success) {
+                document.getElementById('topic_input').value = data.chapter_title;
+                resultDiv.innerText = `AI Suggestion: ${data.chapter_title}`;
+                resultDiv.classList.remove('d-none', 'alert-danger');
+                resultDiv.classList.add('alert-info');
+            } else {
+                throw new Error(data.error);
+            }
+        } catch (e) {
+            resultDiv.innerText = `Error: ${e.message}`;
+            resultDiv.classList.remove('d-none', 'alert-info');
+            resultDiv.classList.add('alert-danger');
+        } finally {
+            btn.disabled = false;
+            btn.querySelector('.spinner-border').classList.add('d-none');
+        }
+    }
+
     document.addEventListener('DOMContentLoaded', () => {
         initializeTomSelect(); // Initialize Tom Select here
         loadSettings();