Update app.py

app.py

@@ -1,107 +1,99 @@
 # --------------------------------------------------------------
 # IGCSE Science Platform – Question Practice & Deep Marking
-# Models: Gemini 2.5 (Primary) → Cohere → Z.ai → MiniMax (Fallbacks)
+# AI: Gemini 2.5 Pro (Primary) → Cohere Command-R+ (Fallback)
+# Hugging Face Spaces compatible
 # --------------------------------------------------------------
 
-
 import os
 import json
-import re
 import time
-from datetime import datetime
+import random
 import gradio as gr
 
-# ---------- 1. Configure ALL AI Systems ----------
-try:
-    import google.generativeai as genai
-    genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
-    gemini_model = genai.GenerativeModel('gemini-2.5-pro')
-    print("✅ Gemini AI initialized (PRIMARY)")
-except Exception as e:
-    print(f"❌ Gemini: {e}")
-    gemini_model = None
-
-try:
-    import cohere
-    cohere_client = cohere.Client(os.getenv("COHERE_API_KEY"))
-    print("✅ Cohere initialized (SECONDARY)")
-except Exception as e:
-    print(f"❌ Cohere: {e}")
-    cohere_client = None
-
-try:
-    from huggingface_hub import InferenceClient
-    zai_client = InferenceClient(provider="novita", api_key=os.environ.get("HF_TOKEN"))
-    print("✅ Z.ai GLM-4.6 initialized (TERTIARY)")
-except Exception as e:
-    print(f"❌ Z.ai: {e}")
-    zai_client = None
-
-try:
-    minimax_client = InferenceClient(provider="novita", api_key=os.environ.get("HF_TOKEN"))
-    print("✅ MiniMax initialized (FINAL FALLBACK)")
-except Exception as e:
-    print(f"❌ MiniMax: {e}")
-    minimax_client = None
-
-
-# ---------- 2. Unified AI Fallback ----------
+# ---------- 1. LAZY AI Initialization ----------
+# Deferred to first use — prevents OOM crash on HF Spaces startup
+
+_gemini_model = None
+_cohere_client = None
+_ai_initialized = False
+
+
+def _init_ai():
+    global _gemini_model, _cohere_client, _ai_initialized
+    if _ai_initialized:
+        return
+
+    try:
+        import google.generativeai as genai
+        key = os.getenv("GEMINI_API_KEY", "")
+        if key:
+            genai.configure(api_key=key)
+            _gemini_model = genai.GenerativeModel("gemini-2.5-pro")
+            print("✅ Gemini 2.5 Pro ready (PRIMARY)")
+        else:
+            print("⚠ GEMINI_API_KEY not set — skipping Gemini")
+    except Exception as e:
+        print(f"❌ Gemini init failed: {e}")
+
+    try:
+        import cohere
+        key = os.getenv("COHERE_API_KEY", "")
+        if key:
+            _cohere_client = cohere.Client(key)
+            print("✅ Cohere Command-R+ ready (FALLBACK)")
+        else:
+            print("⚠ COHERE_API_KEY not set — skipping Cohere")
+    except Exception as e:
+        print(f"❌ Cohere init failed: {e}")
+
+    _ai_initialized = True
+
+
+# ---------- 2. Unified AI Call ----------
 def ask_ai(prompt, temperature=0.7, max_retries=2):
+    _init_ai()
     last_error = None
 
-    if gemini_model:
+    # Primary: Gemini 2.5 Pro
+    if _gemini_model:
         for attempt in range(max_retries):
             try:
-                resp = gemini_model.generate_content(
+                import google.generativeai as genai
+                resp = _gemini_model.generate_content(
                     prompt,
                     generation_config=genai.types.GenerationConfig(temperature=temperature)
                 )
                 return resp.text, "gemini"
             except Exception as e:
                 last_error = e
+                print(f"⚠ Gemini attempt {attempt + 1} failed: {e}")
                 if attempt < max_retries - 1:
                     time.sleep(1)
 
-    if cohere_client:
-        for attempt in range(max_retries):
-            try:
-                resp = cohere_client.chat(model="command-r-plus-08-2024", message=prompt, temperature=temperature)
-                return resp.text, "cohere"
-            except Exception as e:
-                last_error = e
-                if attempt < max_retries - 1:
-                    time.sleep(1)
-
-    if zai_client:
+    # Fallback: Cohere Command-R+
+    if _cohere_client:
         for attempt in range(max_retries):
             try:
-                comp = zai_client.chat.completions.create(
-                    model="zai-org/GLM-4.6",
-                    messages=[{"role": "user", "content": prompt}],
+                resp = _cohere_client.chat(
+                    model="command-r-plus-08-2024",
+                    message=prompt,
                     temperature=temperature
                 )
-                return comp.choices[0].message.content, "zai"
+                return resp.text, "cohere"
             except Exception as e:
                 last_error = e
+                print(f"⚠ Cohere attempt {attempt + 1} failed: {e}")
                 if attempt < max_retries - 1:
                     time.sleep(1)
 
-    if minimax_client:
-        try:
-            comp = minimax_client.chat.completions.create(
-                model="MiniMaxAI/MiniMax-M2",
-                messages=[{"role": "user", "content": prompt}],
-                temperature=temperature
-            )
-            return comp.choices[0].message.content, "minimax"
-        except Exception as e:
-            last_error = e
-
-    return f"❌ All AI services failed. Last error: {str(last_error)}", "error"
+    return (
+        "❌ No AI service available. Add GEMINI_API_KEY or COHERE_API_KEY in Space Secrets.",
+        "error"
+    )
 
 
 # ---------- 3. Topic Lists ----------
-chemistry_topics = [
+CHEMISTRY_TOPICS = [
     "States of Matter", "Atoms, Elements & Compounds", "Mixtures & Separation Techniques",
     "Atomic Structure", "Electronic Configuration", "Periodic Table",
     "Chemical Bonding: Ionic", "Chemical Bonding: Covalent", "Chemical Bonding: Metallic",
@@ -124,7 +116,7 @@ chemistry_topics = [
     "Laboratory Safety", "Experimental Techniques", "Analysis & Evaluation"
 ]
 
-biology_topics = [
+BIOLOGY_TOPICS = [
     "Cell Structure & Function", "Specialised Cells", "Microscopy",
     "Cell Division: Mitosis", "Cell Division: Meiosis", "Stem Cells",
     "Diffusion", "Osmosis", "Active Transport",
@@ -154,17 +146,16 @@ biology_topics = [
     "Biological Techniques", "Field Studies"
 ]
 
-# ---------- 4. Question Types per Subject ----------
 QUESTION_TYPES = {
     "Chemistry": [
-        "Multiple Choice", "Short Answer – Describe", "Short Answer – Explain",
-        "Calculation – Moles / Mass", "Calculation – Concentration",
-        "Calculation – Percentage Yield", "Extended Response",
+        "Multiple Choice", "Short Answer - Describe", "Short Answer - Explain",
+        "Calculation - Moles / Mass", "Calculation - Concentration",
+        "Calculation - Percentage Yield", "Extended Response",
         "Data Interpretation", "Practical / Experiment", "Equation Writing",
         "Evaluation / Suggest Improvements"
     ],
     "Biology": [
-        "Multiple Choice", "Short Answer – Describe", "Short Answer – Explain",
+        "Multiple Choice", "Short Answer - Describe", "Short Answer - Explain",
         "Extended Response", "Data Interpretation", "Graph / Table Analysis",
         "Practical / Experiment", "Evaluation / Suggest Improvements",
         "Applying Knowledge to Novel Scenarios", "Genetic Diagrams",
@@ -172,15 +163,16 @@ QUESTION_TYPES = {
     ]
 }
 
-# ---------- 5. Question Generation ----------
+
+# ---------- 4. Question Generation ----------
 def generate_question(subject, topic, q_type, difficulty, num_marks):
     if not topic:
-        return "⚠ Please select a topic first.", "", "", ""
+        return "Please select a topic first.", "", "", ""
 
     difficulty_desc = {
-        "Foundation": "Test basic recall and simple application. Clear, direct language. 1–3 mark questions.",
-        "Core": "Test understanding and application. Require structured explanations. 3–5 mark questions.",
-        "Extended": "Test analysis, evaluation, and synthesis. Multi-step reasoning, novel contexts. 5–8 mark questions."
+        "Foundation": "Test basic recall and simple application. Clear, direct language.",
+        "Core": "Test understanding and application. Require structured explanations.",
+        "Extended": "Test analysis, evaluation, and synthesis. Multi-step reasoning, novel contexts."
     }
 
     prompt = f"""You are an expert IGCSE {subject} examiner for Cambridge International Education.
@@ -188,33 +180,32 @@ def generate_question(subject, topic, q_type, difficulty, num_marks):
 Create ONE authentic IGCSE {subject} exam question with these specifications:
 - Topic: {topic}
 - Question Type: {q_type}
-- Difficulty: {difficulty} — {difficulty_desc[difficulty]}
+- Difficulty: {difficulty} - {difficulty_desc[difficulty]}
 - Total Marks: {num_marks}
 
 REQUIREMENTS:
 - Use authentic IGCSE command words (describe, explain, suggest, calculate, evaluate, state, outline, compare, deduce, predict)
-- The question must test genuine UNDERSTANDING of the topic, not just memorisation
+- Test genuine UNDERSTANDING, not just memorisation
 - Include realistic context, data, or scenarios where appropriate
-- For calculation questions, provide all necessary data (molar masses, formulae, values)
-- For data/graph questions, describe a realistic dataset or table in text form
-- Be precise — every mark should test something specific
+- For calculations: provide all necessary data (molar masses, formulae, values)
+- For data/graph questions: describe a realistic dataset in text form
+- Every mark should test something specific
 
-Return ONLY a valid JSON object (no markdown, no code fences):
+Return ONLY a valid JSON object, no markdown, no code fences:
 {{
-  "question_text": "The full question including any context, data tables (as text), or scenario. Use \\n for new lines.",
+  "question_text": "Full question with context. Use \\n for new lines.",
   "marks": {num_marks},
-  "command_word": "the primary command word used",
+  "command_word": "primary command word used",
   "question_type": "{q_type}",
   "topic": "{topic}",
   "difficulty": "{difficulty}",
   "mark_scheme": [
     "Point 1 (1 mark): exact wording of acceptable answer",
-    "Point 2 (1 mark): ...",
-    "... one string per mark point"
+    "Point 2 (1 mark): ..."
   ],
-  "model_answer": "A complete, full-mark answer written as a student would write it",
-  "examiner_notes": "What the examiner is looking for; common pitfalls; what differentiates strong answers from weak ones",
-  "key_concepts_tested": ["concept1", "concept2", "..."]
+  "model_answer": "A complete full-mark answer written as a student would write it",
+  "examiner_notes": "What the examiner looks for; common pitfalls; what separates strong from weak answers",
+  "key_concepts_tested": ["concept1", "concept2"]
 }}"""
 
     resp, source = ask_ai(prompt, temperature=0.4)
@@ -223,49 +214,41 @@ Return ONLY a valid JSON object (no markdown, no code fences):
         clean = resp.replace("```json", "").replace("```", "").strip()
         data = json.loads(clean)
 
-        q_display = f"""**[{data['marks']} marks] | {data['question_type']} | {data['difficulty']} | Command word: {data['command_word'].upper()}**
+        q_display = (
+            f"**[{data['marks']} marks] | {data['question_type']} | "
+            f"{data['difficulty']} | Command word: {data['command_word'].upper()}**\n\n---\n\n"
+            f"{data['question_text']}"
+        )
 
----
-
-{data['question_text']}"""
-
-        mark_scheme_text = "\n".join([f"• {pt}" for pt in data.get("mark_scheme", [])])
-        examiner_notes = data.get("examiner_notes", "")
+        mark_scheme_text = "\n".join([f"- {pt}" for pt in data.get("mark_scheme", [])])
         key_concepts = ", ".join(data.get("key_concepts_tested", []))
-
-        scheme_display = f"""**Mark Scheme — {data['marks']} marks**
-
-{mark_scheme_text}
-
----
-
-**Examiner Notes:**
-{examiner_notes}
-
-**Key Concepts Tested:** {key_concepts}"""
+        scheme_display = (
+            f"**Mark Scheme - {data['marks']} marks**\n\n{mark_scheme_text}\n\n---\n\n"
+            f"**Examiner Notes:**\n{data.get('examiner_notes', '')}\n\n"
+            f"**Key Concepts Tested:** {key_concepts}"
+        )
 
         model_ans = data.get("model_answer", "")
-
-        source_tag = f"\n\n_AI Source: {source.title()}_" if source != "gemini" else ""
+        source_tag = f"\n\n_Generated by {source.title()}_" if source != "gemini" else ""
 
         return q_display, scheme_display, model_ans, source_tag
 
-    except Exception as e:
-        return f"⚠ Could not parse question. Raw response:\n\n{resp}", "", "", ""
+    except Exception:
+        return f"Could not parse question. Raw response:\n\n{resp}", "", "", ""
 
 
-# ---------- 6. Deep Marking ----------
-def mark_answer(question_display, scheme_display, student_answer, subject, show_model):
+# ---------- 5. Deep Marking ----------
+def mark_answer(question_display, scheme_display, student_answer, subject, model_ans):
     if not student_answer.strip():
-        return "⚠ Please write your answer before submitting for marking."
+        return "Please write your answer before submitting for marking."
     if not question_display or "select a topic" in question_display.lower():
-        return "⚠ Please generate a question first."
+        return "Please generate a question first."
 
-    model_section = f"\n\nModel answer for reference:\n{show_model}" if show_model else ""
+    model_section = f"\n\nModel answer for reference:\n{model_ans}" if model_ans else ""
 
     prompt = f"""You are a highly experienced IGCSE {subject} examiner providing DETAILED FORMATIVE FEEDBACK.
 
-QUESTION & MARK SCHEME:
+QUESTION AND MARK SCHEME:
 {question_display}
 
 {scheme_display}
@@ -276,53 +259,46 @@ STUDENT'S ANSWER:
 
 ---
 
-Your job is to mark this answer with MAXIMUM DIAGNOSTIC DETAIL. Go through the student's answer sentence by sentence and:
-
+Mark this answer with MAXIMUM DIAGNOSTIC DETAIL. Go sentence by sentence and:
 1. Award marks explicitly against the mark scheme
-2. Identify EVERY error — factual, conceptual, terminological, or structural
+2. Identify EVERY error - factual, conceptual, terminological, or structural
 3. Explain WHY each error is wrong scientifically
 4. Identify gaps where expected points are missing
-5. Recognise what the student understood well (credit genuine understanding)
+5. Recognise genuine understanding and credit it
 6. Give specific, actionable improvement advice
 
-Return ONLY a valid JSON object (no markdown, no code fences):
+Return ONLY a valid JSON object, no markdown, no code fences:
 {{
-  "marks_awarded": 3,
-  "marks_total": {5},
-  "percentage": 60,
-  "grade_band": "Developing / Secure / Proficient / Excellent",
+  "marks_awarded": 0,
+  "marks_total": 5,
+  "percentage": 0,
+  "grade_band": "Developing",
   "overall_verdict": "1-2 sentence summary of performance",
   "mark_by_mark_breakdown": [
     {{
       "mark_point": "what the mark scheme required",
       "awarded": true,
-      "student_wrote": "what the student actually wrote (quote or paraphrase)",
+      "student_wrote": "what the student actually wrote",
       "verdict": "Correct / Partially correct / Incorrect / Missing",
-      "explanation": "Why this earned/lost the mark; what was missing or wrong"
+      "explanation": "Why this earned/lost the mark"
     }}
   ],
   "errors_in_detail": [
     {{
-      "error": "Exact quote or paraphrase of the student's error",
+      "error": "Quote or paraphrase of the student error",
       "error_type": "Factual error / Misconception / Vague language / Missing detail / Wrong terminology / Incomplete explanation",
-      "why_wrong": "Scientific explanation of why this is wrong or insufficient",
-      "correct_version": "How it should have been written/what should have been said",
-      "how_to_fix": "Specific advice to avoid this mistake in future"
+      "why_wrong": "Scientific explanation of why this is wrong",
+      "correct_version": "How it should have been written",
+      "how_to_fix": "Specific advice to avoid this in future"
     }}
   ],
-  "missing_points": [
-    "Key point the student should have included but didn't"
-  ],
-  "strengths": [
-    "Specific things the student did well"
-  ],
-  "improvement_plan": [
-    "Specific, numbered action item to improve on this topic"
-  ],
-  "conceptual_gaps": "Description of any underlying conceptual misunderstanding revealed by the answer",
-  "terminology_issues": "Any scientific terminology used incorrectly or imprecisely",
-  "exam_technique_feedback": "Advice on structure, command word response, use of scientific language, answer length",
-  "recommended_focus": "The single most important thing this student should study/practise next"
+  "missing_points": ["Key point the student should have included"],
+  "strengths": ["Specific things the student did well"],
+  "improvement_plan": ["Action item 1", "Action item 2"],
+  "conceptual_gaps": "Any underlying conceptual misunderstanding revealed",
+  "terminology_issues": "Scientific terminology used incorrectly or imprecisely",
+  "exam_technique_feedback": "Advice on structure, command word response, scientific language, answer length",
+  "recommended_focus": "The single most important thing to study next"
 }}"""
 
     resp, source = ask_ai(prompt, temperature=0.2)
@@ -334,134 +310,167 @@ Return ONLY a valid JSON object (no markdown, no code fences):
         marks_awarded = fb.get("marks_awarded", 0)
         marks_total = fb.get("marks_total", 1)
         pct = fb.get("percentage", round(marks_awarded / marks_total * 100))
-        band = fb.get("grade_band", "—")
+        band = fb.get("grade_band", "")
 
-        # Score bar
         filled = int(pct / 10)
         bar = "█" * filled + "░" * (10 - filled)
         score_color = "🔴" if pct < 40 else "🟡" if pct < 70 else "🟢"
 
-        output = f"""{score_color} **{marks_awarded}/{marks_total} marks ({pct}%) — {band}**
-`{bar}` 
+        out = (
+            f"{score_color} **{marks_awarded}/{marks_total} marks ({pct}%) - {band}**\n"
+            f"`{bar}`\n\n"
+            f"_{fb.get('overall_verdict', '')}_\n\n---\n\n"
+            f"## Mark-by-Mark Breakdown\n\n"
+        )
 
-_{fb.get('overall_verdict', '')}_
-
----
-
-## 📋 Mark-by-Mark Breakdown
-
-"""
         for i, mp in enumerate(fb.get("mark_by_mark_breakdown", []), 1):
             icon = "✅" if mp.get("awarded") else "❌"
-            verdict = mp.get("verdict", "")
-            output += f"""**Mark {i}** {icon} _{verdict}_
-- **Required:** {mp.get('mark_point', '')}
-- **Student wrote:** _{mp.get('student_wrote', '—')}_
-- **Examiner:** {mp.get('explanation', '')}
-
-"""
+            out += (
+                f"**Mark {i}** {icon} _{mp.get('verdict', '')}_\n"
+                f"- **Required:** {mp.get('mark_point', '')}\n"
+                f"- **Student wrote:** _{mp.get('student_wrote', '')}_\n"
+                f"- **Examiner:** {mp.get('explanation', '')}\n\n"
+            )
 
         if fb.get("errors_in_detail"):
-            output += "---\n\n## 🔍 Errors — In Detail\n\n"
+            out += "---\n\n## Errors In Detail\n\n"
             for err in fb["errors_in_detail"]:
-                output += f"""**Error type:** `{err.get('error_type', '')}`
-> _{err.get('error', '')}_
-
-**Why it's wrong:** {err.get('why_wrong', '')}
-
-**Correct version:** {err.get('correct_version', '')}
-
-**How to fix it:** {err.get('how_to_fix', '')}
-
-"""
+                out += (
+                    f"**Error type:** `{err.get('error_type', '')}`\n"
+                    f"> _{err.get('error', '')}_\n\n"
+                    f"**Why it's wrong:** {err.get('why_wrong', '')}\n\n"
+                    f"**Correct version:** {err.get('correct_version', '')}\n\n"
+                    f"**How to fix it:** {err.get('how_to_fix', '')}\n\n"
+                )
 
         if fb.get("missing_points"):
-            output += "---\n\n## ⚠️ Missing Points\n\n"
+            out += "---\n\n## Missing Points\n\n"
             for mp in fb["missing_points"]:
-                output += f"- {mp}\n"
+                out += f"- {mp}\n"
 
         if fb.get("strengths"):
-            output += "\n---\n\n## ✅ What You Did Well\n\n"
+            out += "\n---\n\n## What You Did Well\n\n"
             for s in fb["strengths"]:
-                output += f"- {s}\n"
+                out += f"- {s}\n"
 
-        output += "\n---\n\n## 📈 Improvement Plan\n\n"
+        out += "\n---\n\n## Improvement Plan\n\n"
         for i, step in enumerate(fb.get("improvement_plan", []), 1):
-            output += f"{i}. {step}\n"
+            out += f"{i}. {step}\n"
 
         if fb.get("conceptual_gaps"):
-            output += f"\n---\n\n## 🧠 Conceptual Gap Identified\n\n{fb['conceptual_gaps']}\n"
+            out += f"\n---\n\n## Conceptual Gap Identified\n\n{fb['conceptual_gaps']}\n"
 
         if fb.get("terminology_issues"):
-            output += f"\n---\n\n## 📝 Terminology Issues\n\n{fb['terminology_issues']}\n"
+            out += f"\n---\n\n## Terminology Issues\n\n{fb['terminology_issues']}\n"
 
-        output += f"\n---\n\n## 🎯 Exam Technique\n\n{fb.get('exam_technique_feedback', '')}\n"
-        output += f"\n---\n\n## 🔮 Most Important Next Step\n\n**{fb.get('recommended_focus', '')}**\n"
+        out += f"\n---\n\n## Exam Technique\n\n{fb.get('exam_technique_feedback', '')}\n"
+        out += f"\n---\n\n## Most Important Next Step\n\n**{fb.get('recommended_focus', '')}**\n"
 
         if source != "gemini":
-            output += f"\n\n_Marked by {source.title()}_"
+            out += f"\n\n_Marked by {source.title()}_"
 
-        return output
+        return out
 
-    except Exception as e:
-        return f"⚠ Could not parse marking. Raw response:\n\n{resp}"
+    except Exception:
+        return f"Could not parse marking feedback. Raw response:\n\n{resp}"
+
+
+# ---------- 6. Topic Drill ----------
+def generate_drill(subject, topic):
+    if not topic:
+        return "Select a topic first!"
+
+    prompt = f"""Generate 10 rapid-fire IGCSE {subject} questions on: "{topic}"
+
+Mix of:
+- 3 x simple recall (1 mark each)
+- 4 x application/explanation (2 marks each)
+- 3 x analysis/evaluation (3 marks each)
 
+Return ONLY a valid JSON array, no markdown:
+[
+  {{
+    "q_num": 1,
+    "question": "question text",
+    "marks": 1,
+    "type": "Recall",
+    "answer": "concise model answer",
+    "key_point": "the single most important thing to include"
+  }}
+]"""
+
+    resp, source = ask_ai(prompt, temperature=0.4)
+
+    try:
+        clean = resp.replace("```json", "").replace("```", "").strip()
+        qs = json.loads(clean)
+
+        out = f"## 10-Question Drill: {topic}\n\n**Total: 17 marks | {subject}**\n\n---\n\n"
+        for q in qs:
+            label = f"[{q['marks']} mark{'s' if q['marks'] > 1 else ''}]"
+            out += f"**Q{q['q_num']}** {label} _{q['type']}_\n{q['question']}\n\n"
+
+        out += "---\n\n### Model Answers\n\n"
+        for q in qs:
+            out += f"**Q{q['q_num']}** ({q['marks']} marks): {q['answer']}\n_Key point: {q['key_point']}_\n\n"
+
+        if source != "gemini":
+            out += f"\n_Generated by {source.title()}_"
+        return out
 
-# ---------- 7. Quiz Mode (5 questions, then score) ----------
+    except Exception:
+        return resp
+
+
+# ---------- 7. Quiz State ----------
 quiz_state = {}
 
+
 def start_quiz(subject, topic, difficulty):
     if not topic:
-        return "⚠ Select a topic first.", gr.update(visible=False), gr.update(visible=False), "", "", ""
+        return "Select a topic first.", gr.update(visible=False), gr.update(visible=False), "", "1 / 5", ""
 
     quiz_state.clear()
-    quiz_state["subject"] = subject
-    quiz_state["topic"] = topic
-    quiz_state["difficulty"] = difficulty
-    quiz_state["questions"] = []
-    quiz_state["schemes"] = []
-    quiz_state["answers"] = []
-    quiz_state["current"] = 0
-    quiz_state["total"] = 5
-
-    q_types = QUESTION_TYPES[subject]
-    selected_types = [q_types[i % len(q_types)] for i in range(5)]
-    quiz_state["q_types"] = selected_types
-
-    # Generate first question
-    q, scheme, model_ans, _ = generate_question(subject, topic, selected_types[0], difficulty, 4)
+    quiz_state.update({
+        "subject": subject, "topic": topic, "difficulty": difficulty,
+        "questions": [], "schemes": [], "model_answers": [], "answers": [],
+        "current": 0, "total": 5,
+        "q_types": [QUESTION_TYPES[subject][i % len(QUESTION_TYPES[subject])] for i in range(5)]
+    })
+
+    q, scheme, model_ans, _ = generate_question(
+        subject, topic, quiz_state["q_types"][0], difficulty, 4
+    )
     quiz_state["questions"].append(q)
     quiz_state["schemes"].append(scheme)
-    quiz_state["model_answers"] = [model_ans]
+    quiz_state["model_answers"].append(model_ans)
 
     return (
-        f"**Quiz started! Question 1 of 5** — {topic} | {difficulty}",
+        f"**Quiz started! Question 1 of 5** - {topic} | {difficulty}",
         gr.update(visible=True),
         gr.update(visible=False),
-        q,
-        "1 / 5",
-        ""
+        q, "1 / 5", ""
     )
 
+
 def quiz_next(student_answer):
     idx = quiz_state.get("current", 0)
     if not student_answer.strip():
-        return "⚠ Write your answer first!", quiz_state["questions"][idx], f"{idx+1} / 5", ""
+        return quiz_state["questions"][idx], quiz_state["schemes"][idx], f"{idx + 1} / 5", ""
 
     quiz_state["answers"].append(student_answer)
     quiz_state["current"] = idx + 1
 
     if quiz_state["current"] >= quiz_state["total"]:
-        # Show results
-        return quiz_state["questions"][idx], quiz_state["schemes"][idx], f"{quiz_state['total']} / {quiz_state['total']}", "done"
-
-    # Generate next question
-    subject = quiz_state["subject"]
-    topic = quiz_state["topic"]
-    difficulty = quiz_state["difficulty"]
-    q_type = quiz_state["q_types"][quiz_state["current"]]
-
-    q, scheme, model_ans, _ = generate_question(subject, topic, q_type, difficulty, 4)
+        return (
+            quiz_state["questions"][idx], quiz_state["schemes"][idx],
+            f"{quiz_state['total']} / {quiz_state['total']}", "done"
+        )
+
+    q, scheme, model_ans, _ = generate_question(
+        quiz_state["subject"], quiz_state["topic"],
+        quiz_state["q_types"][quiz_state["current"]], quiz_state["difficulty"], 4
+    )
     quiz_state["questions"].append(q)
     quiz_state["schemes"].append(scheme)
     quiz_state["model_answers"].append(model_ans)
@@ -471,87 +480,32 @@ def quiz_next(student_answer):
 
 def get_quiz_results():
     if not quiz_state or not quiz_state.get("answers"):
-        return "⚠ No quiz in progress."
+        return "No quiz in progress."
 
     subject = quiz_state["subject"]
-    total_marks = 0
-    awarded_marks = 0
-    result_text = "# 📊 Quiz Results\n\n"
-
-    for i, (q, scheme, ans) in enumerate(zip(
-        quiz_state["questions"], quiz_state["schemes"], quiz_state["answers"]
+    result_text = "# Mock Results\n\n"
+    for i, (q, scheme, ans, model) in enumerate(zip(
+        quiz_state["questions"], quiz_state["schemes"],
+        quiz_state["answers"], quiz_state["model_answers"]
     ), 1):
-        feedback = mark_answer(q, scheme, ans, subject, quiz_state["model_answers"][i - 1])
+        feedback = mark_answer(q, scheme, ans, subject, model)
         result_text += f"## Question {i}\n\n{q}\n\n**Your Answer:** {ans}\n\n{feedback}\n\n---\n\n"
-
     return result_text
 
 
-# ---------- 8. Topic Drill (10 rapid-fire questions) ----------
-def generate_drill(subject, topic):
-    if not topic:
-        return "⚠ Select a topic!"
-
-    prompt = f"""Generate 10 rapid-fire IGCSE {subject} questions on the topic: "{topic}"
-
-These should be a MIX of:
-- 3 × simple recall (1 mark each)
-- 4 × application/explanation (2 marks each)  
-- 3 × analysis/evaluation (3 marks each)
-
-Total: 17 marks
-
-Return ONLY a valid JSON array (no markdown):
-[
-  {{
-    "q_num": 1,
-    "question": "question text",
-    "marks": 1,
-    "type": "Recall/Application/Analysis",
-    "answer": "concise model answer",
-    "key_point": "the single most important thing to include"
-  }},
-  ...10 items total
-]"""
-
-    resp, source = ask_ai(prompt, temperature=0.4)
-
-    try:
-        clean = resp.replace("```json", "").replace("```", "").strip()
-        qs = json.loads(clean)
-
-        output = f"## 🔥 10-Question Drill: {topic}\n\n"
-        output += f"**Total: 17 marks | Subject: {subject}**\n\n---\n\n"
-
-        for q in qs:
-            mark_label = f"[{q['marks']} mark{'s' if q['marks'] > 1 else ''}]"
-            output += f"**Q{q['q_num']}** {mark_label} _{q['type']}_\n{q['question']}\n\n"
-
-        output += "---\n\n### ✅ Model Answers\n\n"
-        for q in qs:
-            output += f"**Q{q['q_num']}** ({q['marks']} marks): {q['answer']}\n_Key point: {q['key_point']}_\n\n"
-
-        if source != "gemini":
-            output += f"\n_Generated by {source.title()}_"
-
-        return output
-
-    except:
-        return resp
-
-
-# ---------- 9. Update Topics Dropdown ----------
+# ---------- 8. UI Helpers ----------
 def update_topics(subject):
-    topics = {"Chemistry": chemistry_topics, "Biology": biology_topics}
+    topics = {"Chemistry": CHEMISTRY_TOPICS, "Biology": BIOLOGY_TOPICS}
     return gr.Dropdown(choices=topics[subject], value=None)
 
+
 def update_q_types(subject):
     return gr.Dropdown(choices=QUESTION_TYPES[subject], value=QUESTION_TYPES[subject][0])
 
 
-# ---------- 10. CSS Styles ----------
+# ---------- 9. CSS ----------
 CUSTOM_CSS = """
-@import url('https://fonts.googleapis.com/css2?family=Syne:wght@400;600;700;800&family=DM+Mono:ital,wght@0,400;0,500;1,400&family=DM+Sans:ital,opsz,wght@0,9..40,300;0,9..40,400;0,9..40,500;1,9..40,300&display=swap');
+@import url('https://fonts.googleapis.com/css2?family=Syne:wght@400;600;700;800&family=DM+Mono:wght@400;500&family=DM+Sans:ital,opsz,wght@0,9..40,300;0,9..40,400;0,9..40,500;1,9..40,300&display=swap');
 
 :root {
     --bg: #0a0e1a;
@@ -560,158 +514,104 @@ CUSTOM_CSS = """
     --border: #1e3a5f;
     --accent: #00d4aa;
     --accent2: #3b82f6;
-    --accent3: #f59e0b;
-    --chem: #10b981;
     --bio: #8b5cf6;
     --text: #e2e8f0;
     --muted: #64748b;
-    --danger: #ef4444;
 }
 
-* { box-sizing: border-box; }
-
 body, .gradio-container {
     background: var(--bg) !important;
-    font-family: 'DM Sans', sans-serif !important;
     color: var(--text) !important;
 }
 
-/* Header */
-.gr-markdown h1 {
-    font-family: 'Syne', sans-serif !important;
-    font-size: 2.4rem !important;
-    font-weight: 800 !important;
-    background: linear-gradient(135deg, var(--accent), var(--accent2), var(--bio)) !important;
-    -webkit-background-clip: text !important;
-    -webkit-text-fill-color: transparent !important;
-    background-clip: text !important;
-    letter-spacing: -0.02em !important;
-}
-
-/* Tabs */
 .tab-nav button {
     font-family: 'Syne', sans-serif !important;
     font-weight: 600 !important;
-    font-size: 0.85rem !important;
-    letter-spacing: 0.05em !important;
+    font-size: 0.82rem !important;
+    letter-spacing: 0.06em !important;
     text-transform: uppercase !important;
     color: var(--muted) !important;
     background: transparent !important;
     border: none !important;
     border-bottom: 2px solid transparent !important;
-    padding: 10px 20px !important;
+    padding: 10px 18px !important;
     transition: all 0.2s !important;
 }
-
 .tab-nav button.selected {
     color: var(--accent) !important;
     border-bottom-color: var(--accent) !important;
 }
 
-/* Inputs */
-input, textarea, select, .gr-input, .gr-textarea {
+input, textarea, select {
     background: var(--surface) !important;
     border: 1px solid var(--border) !important;
     color: var(--text) !important;
     border-radius: 8px !important;
-    font-family: 'DM Sans', sans-serif !important;
 }
-
 textarea:focus, input:focus {
     border-color: var(--accent) !important;
+    box-shadow: 0 0 0 3px rgba(0,212,170,0.1) !important;
     outline: none !important;
-    box-shadow: 0 0 0 3px rgba(0, 212, 170, 0.1) !important;
 }
 
-/* Buttons */
 .gr-button {
     font-family: 'Syne', sans-serif !important;
     font-weight: 600 !important;
-    letter-spacing: 0.03em !important;
+    letter-spacing: 0.04em !important;
     border-radius: 8px !important;
     transition: all 0.2s !important;
 }
-
 .gr-button-primary {
     background: linear-gradient(135deg, var(--accent), var(--accent2)) !important;
     border: none !important;
     color: #fff !important;
 }
-
 .gr-button-primary:hover {
     transform: translateY(-1px) !important;
-    box-shadow: 0 4px 20px rgba(0, 212, 170, 0.3) !important;
+    box-shadow: 0 4px 20px rgba(0,212,170,0.3) !important;
 }
-
 .gr-button-secondary {
     background: var(--surface2) !important;
     border: 1px solid var(--border) !important;
     color: var(--text) !important;
 }
 
-/* Markdown output */
 .gr-markdown {
     background: var(--surface) !important;
     border: 1px solid var(--border) !important;
     border-radius: 10px !important;
     padding: 20px !important;
-    font-family: 'DM Sans', sans-serif !important;
     line-height: 1.7 !important;
 }
-
 .gr-markdown code {
     font-family: 'DM Mono', monospace !important;
-    background: rgba(0,212,170,0.1) !important;
+    background: rgba(0,212,170,0.12) !important;
     color: var(--accent) !important;
     padding: 2px 6px !important;
     border-radius: 4px !important;
 }
-
 .gr-markdown blockquote {
     border-left: 3px solid var(--accent2) !important;
     padding-left: 12px !important;
     color: var(--muted) !important;
     font-style: italic !important;
 }
-
 .gr-markdown h2 {
     font-family: 'Syne', sans-serif !important;
     font-weight: 700 !important;
     color: var(--accent) !important;
-    margin-top: 1.5rem !important;
-    font-size: 1.1rem !important;
-    letter-spacing: 0.02em !important;
+    font-size: 1.05rem !important;
+    margin-top: 1.4rem !important;
 }
-
 .gr-markdown h3 {
     font-family: 'Syne', sans-serif !important;
     font-weight: 600 !important;
     color: var(--accent2) !important;
 }
-
-/* Radio & Dropdown */
-.gr-radio, .gr-dropdown {
-    background: var(--surface) !important;
-    border-radius: 8px !important;
-}
-
-.gr-radio label {
-    font-family: 'DM Sans', sans-serif !important;
-    color: var(--text) !important;
-}
-
-/* Status bar */
-.status-bar {
-    display: flex;
-    gap: 12px;
-    padding: 8px 0;
-    font-family: 'DM Mono', monospace;
-    font-size: 0.75rem;
-}
 """
 
 
-# ---------- 11. Gradio UI ----------
+# ---------- 10. Gradio UI ----------
 with gr.Blocks(
     theme=gr.themes.Base(
         primary_hue="teal",
@@ -720,215 +620,194 @@ with gr.Blocks(
         font=gr.themes.GoogleFont("DM Sans"),
     ),
     css=CUSTOM_CSS,
-    title="IGCSE Science — Question & Marking Platform"
+    title="IGCSE Science - Question & Marking Platform"
 ) as app:
 
     gr.Markdown("""
-# ⚗️ IGCSE Science — Question Practice & Deep Marking
+# IGCSE Science - Question Practice & Deep Marking
 ### Chemistry · Biology · AI-Powered Diagnostic Feedback
     """)
 
     with gr.Tabs():
 
-        # ══════════════════════════════════════════════════════
-        # TAB 1: QUESTION GENERATOR & MARKER
-        # ══════════════════════════════════════════════════════
-        with gr.Tab("🎯 Practice Questions"):
-            gr.Markdown("""### Generate exam-style questions on any topic, then get detailed mark-by-mark feedback""")
+        # TAB 1: PRACTICE QUESTIONS
+        with gr.Tab("Practice Questions"):
+            gr.Markdown("### Generate an exam-style question, write your answer, get full mark-by-mark feedback")
 
             with gr.Row():
                 with gr.Column(scale=1):
                     pg_subject = gr.Radio(["Chemistry", "Biology"], label="Subject", value="Chemistry")
-                    pg_topic = gr.Dropdown(chemistry_topics, label="Topic", allow_custom_value=True)
-                    pg_qtype = gr.Dropdown(QUESTION_TYPES["Chemistry"], label="Question Type",
-                                           value=QUESTION_TYPES["Chemistry"][0])
-                    pg_difficulty = gr.Radio(["Foundation", "Core", "Extended"],
-                                            label="Difficulty / Tier", value="Core")
-                    pg_marks = gr.Slider(1, 9, value=5, step=1, label="Marks")
-
-                    generate_btn = gr.Button("⚡ Generate Question", variant="primary", size="lg")
+                    pg_topic   = gr.Dropdown(CHEMISTRY_TOPICS, label="Topic", allow_custom_value=True)
+                    pg_qtype   = gr.Dropdown(
+                        QUESTION_TYPES["Chemistry"], label="Question Type",
+                        value=QUESTION_TYPES["Chemistry"][0]
+                    )
+                    pg_diff    = gr.Radio(["Foundation", "Core", "Extended"], label="Difficulty", value="Core")
+                    pg_marks   = gr.Slider(1, 9, value=5, step=1, label="Marks")
+                    gen_btn    = gr.Button("Generate Question", variant="primary", size="lg")
 
                 with gr.Column(scale=2):
-                    pg_question = gr.Markdown(label="Question", value="_Your question will appear here_")
-                    pg_progress = gr.Textbox(label="", value="", visible=False)
+                    pg_question = gr.Markdown(value="_Your question will appear here after clicking Generate_")
 
-            pg_subject.change(update_topics, pg_subject, pg_topic)
+            pg_subject.change(update_topics,  pg_subject, pg_topic)
             pg_subject.change(update_q_types, pg_subject, pg_qtype)
 
-            gr.Markdown("---")
-            gr.Markdown("### ✍️ Write Your Answer")
+            gr.Markdown("---\n### Write Your Answer")
             pg_answer = gr.Textbox(
                 lines=10,
                 label="Your Answer",
-                placeholder="Write your full answer here. Use scientific terminology. Explain your reasoning step by step...\n\nFor calculations: show all working and units.\nFor explanations: give cause AND effect.\nFor descriptions: be specific, not vague."
+                placeholder=(
+                    "Write your full answer here.\n\n"
+                    "For explanations: give cause AND effect\n"
+                    "For calculations: show all working with units\n"
+                    "For descriptions: be specific, not vague\n"
+                    "Use correct scientific terminology"
+                )
             )
 
             with gr.Row():
-                show_model = gr.Checkbox(label="Show model answer in feedback (helps you learn from it)", value=True)
-                mark_btn = gr.Button("📝 Submit for Marking", variant="primary", size="lg")
+                show_model_cb = gr.Checkbox(label="Include model answer in feedback", value=True)
+                mark_btn = gr.Button("Submit for Marking", variant="primary", size="lg")
 
-            pg_feedback = gr.Markdown(label="📋 Detailed Examiner Feedback", value="_Submit your answer to receive feedback_")
+            pg_feedback = gr.Markdown(value="_Submit your answer to receive detailed feedback_")
 
-            # Hidden stores
-            pg_scheme = gr.State("")
-            pg_model_answer = gr.State("")
-            pg_source_tag = gr.State("")
+            pg_scheme      = gr.State("")
+            pg_model_store = gr.State("")
 
             def on_generate(subject, topic, q_type, difficulty, marks):
-                q, scheme, model_ans, tag = generate_question(subject, topic, q_type, difficulty, marks)
-                return q, scheme, model_ans, q, "_Submit your answer to receive feedback_"
+                q, scheme, model_ans, _ = generate_question(subject, topic, q_type, difficulty, marks)
+                return q, scheme, model_ans, "_Submit your answer to receive detailed feedback_"
 
-            generate_btn.click(
+            gen_btn.click(
                 on_generate,
-                [pg_subject, pg_topic, pg_qtype, pg_difficulty, pg_marks],
-                [pg_question, pg_scheme, pg_model_answer, pg_question, pg_feedback]
+                [pg_subject, pg_topic, pg_qtype, pg_diff, pg_marks],
+                [pg_question, pg_scheme, pg_model_store, pg_feedback]
             )
 
-            def on_mark(question, scheme, model_ans, student_ans, subject, show_mod):
-                model_to_show = model_ans if show_mod else ""
-                return mark_answer(question, scheme, student_ans, subject, model_to_show)
+            def on_mark(question, scheme, model_ans, student_ans, subject, use_model):
+                return mark_answer(question, scheme, student_ans, subject, model_ans if use_model else "")
 
             mark_btn.click(
                 on_mark,
-                [pg_question, pg_scheme, pg_model_answer, pg_answer, pg_subject, show_model],
+                [pg_question, pg_scheme, pg_model_store, pg_answer, pg_subject, show_model_cb],
                 pg_feedback
             )
 
-            # Reveal mark scheme separately
-            with gr.Accordion("📖 View Mark Scheme (spoiler!)", open=False):
-                pg_scheme_display = gr.Markdown(value="_Generate a question first_")
+            with gr.Accordion("View Mark Scheme (spoiler!)", open=False):
+                pg_scheme_view = gr.Markdown(value="_Generate a question first_")
 
-            def show_scheme(scheme):
-                return scheme if scheme else "_No mark scheme yet. Generate a question first._"
-
-            generate_btn.click(show_scheme, pg_scheme, pg_scheme_display)
+            gen_btn.click(
+                lambda s: s if s else "_Generate a question first_",
+                pg_scheme, pg_scheme_view
+            )
 
 
-        # ══════════════════════════════════════════════════════
-        # TAB 2: TOPIC DRILL (10 Questions)
-        # ══════════════════════════════════════════════════════
-        with gr.Tab("🔥 Topic Drill"):
-            gr.Markdown("""### 10 rapid-fire questions on one topic — with model answers
-*Great for revision and checking what you know*""")
+        # TAB 2: TOPIC DRILL
+        with gr.Tab("Topic Drill"):
+            gr.Markdown("### 10 rapid-fire questions on one topic — answers revealed below\n*Great for revision sweeps*")
 
             with gr.Row():
                 drill_subject = gr.Radio(["Chemistry", "Biology"], label="Subject", value="Chemistry")
-                drill_topic = gr.Dropdown(chemistry_topics, label="Topic", allow_custom_value=True)
+                drill_topic   = gr.Dropdown(CHEMISTRY_TOPICS, label="Topic", allow_custom_value=True)
 
             drill_subject.change(update_topics, drill_subject, drill_topic)
 
-            drill_btn = gr.Button("🚀 Generate 10-Question Drill", variant="primary", size="lg")
-            drill_output = gr.Markdown(label="Drill Questions & Answers", value="_Select a topic and generate your drill_")
-
+            drill_btn    = gr.Button("Generate 10-Question Drill", variant="primary", size="lg")
+            drill_output = gr.Markdown(value="_Select a topic and click Generate_")
             drill_btn.click(generate_drill, [drill_subject, drill_topic], drill_output)
 
 
-        # ══════════════════════════════════════════════════════
-        # TAB 3: TIMED MOCK (5 questions, batch mark)
-        # ══════════════════════════════════════════════════════
-        with gr.Tab("⏱️ Timed Mock"):
-            gr.Markdown("""### Answer 5 questions in sequence, then get full results
-*Simulate real exam conditions*""")
+        # TAB 3: TIMED MOCK
+        with gr.Tab("Timed Mock"):
+            gr.Markdown("### Answer 5 questions in sequence — full batch marking at the end\n*Simulate real exam conditions*")
 
             with gr.Row():
                 mock_subject = gr.Radio(["Chemistry", "Biology"], label="Subject", value="Chemistry")
-                mock_topic = gr.Dropdown(chemistry_topics, label="Topic", allow_custom_value=True)
-                mock_diff = gr.Radio(["Foundation", "Core", "Extended"], label="Difficulty", value="Core")
+                mock_topic   = gr.Dropdown(CHEMISTRY_TOPICS, label="Topic", allow_custom_value=True)
+                mock_diff    = gr.Radio(["Foundation", "Core", "Extended"], label="Difficulty", value="Core")
 
             mock_subject.change(update_topics, mock_subject, mock_topic)
 
-            mock_status = gr.Markdown("_Start a quiz to begin_")
-            mock_start_btn = gr.Button("▶ Start 5-Question Mock", variant="primary", size="lg")
-
-            with gr.Column(visible=False) as mock_question_section:
-                mock_q_display = gr.Markdown(label="Current Question")
-                mock_progress = gr.Textbox(label="Progress", value="1 / 5", interactive=False)
-                mock_answer = gr.Textbox(lines=8, label="Your Answer",
-                                         placeholder="Write your answer here...")
-                mock_next_btn = gr.Button("Next Question →", variant="primary")
-                mock_finish_btn = gr.Button("🏁 Finish & Get Results", variant="secondary", visible=False)
+            mock_status    = gr.Markdown("_Click Start to begin your mock_")
+            mock_start_btn = gr.Button("Start 5-Question Mock", variant="primary", size="lg")
 
-            mock_results = gr.Markdown(label="Quiz Results", value="")
+            with gr.Column(visible=False) as mock_q_section:
+                mock_q_display  = gr.Markdown()
+                mock_progress   = gr.Textbox(label="Progress", value="1 / 5", interactive=False)
+                mock_answer_box = gr.Textbox(
+                    lines=8, label="Your Answer", placeholder="Write your answer here..."
+                )
+                mock_next_btn   = gr.Button("Next Question", variant="primary")
+                mock_finish_btn = gr.Button("Finish and Get Full Results", variant="secondary", visible=False)
 
-            # Store for scheme of current question
-            mock_current_scheme = gr.State("")
+            mock_results = gr.Markdown(value="")
 
             def on_start_mock(subject, topic, difficulty):
                 if not topic:
-                    return ("⚠ Select a topic first",
-                            gr.update(visible=False),
-                            gr.update(visible=False),
-                            "_", "1 / 5", "")
-                status, q_section, finish, q, prog, _ = start_quiz(subject, topic, difficulty)
+                    return "Select a topic first.", gr.update(visible=False), gr.update(visible=False), "", "1 / 5", ""
+                status, _, __, q, prog, ___ = start_quiz(subject, topic, difficulty)
                 return status, gr.update(visible=True), gr.update(visible=False), q, prog, ""
 
             mock_start_btn.click(
                 on_start_mock,
                 [mock_subject, mock_topic, mock_diff],
-                [mock_status, mock_question_section, mock_finish_btn, mock_q_display, mock_progress, mock_results]
+                [mock_status, mock_q_section, mock_finish_btn, mock_q_display, mock_progress, mock_results]
             )
 
             def on_mock_next(answer):
                 q, scheme, prog, done = quiz_next(answer)
-                finish_visible = done == "done"
-                return (q, prog,
-                        gr.update(visible=not finish_visible),
-                        gr.update(visible=finish_visible),
-                        "")
+                is_done = done == "done"
+                return q, prog, gr.update(visible=not is_done), gr.update(visible=is_done), ""
 
             mock_next_btn.click(
                 on_mock_next,
-                [mock_answer],
-                [mock_q_display, mock_progress, mock_next_btn, mock_finish_btn, mock_answer]
+                [mock_answer_box],
+                [mock_q_display, mock_progress, mock_next_btn, mock_finish_btn, mock_answer_box]
             )
-
             mock_finish_btn.click(get_quiz_results, [], mock_results)
 
 
-        # ══════════════════════════════════════════════════════
         # TAB 4: TOPIC EXPLORER
-        # ══════════════════════════════════════════════════════
-        with gr.Tab("🗺️ Topic Explorer"):
-            gr.Markdown("""### Explore all examinable topics
-*Click any topic to generate a focused question set*""")
+        with gr.Tab("Topic Explorer"):
+            gr.Markdown("### Browse all examinable topics or type any topic for a quick question")
 
             with gr.Row():
-                ex_subject = gr.Radio(["Chemistry", "Biology"], label="Subject", value="Chemistry")
-
-            gr.Markdown("#### Chemistry Topics")
-            chem_topics_display = gr.Markdown("\n".join([f"- {t}" for t in chemistry_topics]))
-
-            gr.Markdown("#### Biology Topics")
-            bio_topics_display = gr.Markdown("\n".join([f"- {t}" for t in biology_topics]))
+                gr.Markdown(
+                    "#### Chemistry Topics\n\n" +
+                    "\n".join([f"- {t}" for t in CHEMISTRY_TOPICS])
+                )
+                gr.Markdown(
+                    "#### Biology Topics\n\n" +
+                    "\n".join([f"- {t}" for t in BIOLOGY_TOPICS])
+                )
 
-            gr.Markdown("---")
-            gr.Markdown("#### Quick Question — Type any topic:")
+            gr.Markdown("---\n#### Quick Question")
             with gr.Row():
-                quick_topic = gr.Textbox(label="Topic", placeholder="e.g. Osmosis, Electrolysis, Enzymes...")
+                quick_topic   = gr.Textbox(label="Topic", placeholder="e.g. Osmosis, Electrolysis, Enzymes...")
                 quick_subject = gr.Radio(["Chemistry", "Biology"], label="Subject", value="Chemistry")
-                quick_diff = gr.Radio(["Foundation", "Core", "Extended"], label="Difficulty", value="Core")
+                quick_diff    = gr.Radio(["Foundation", "Core", "Extended"], label="Difficulty", value="Core")
 
-            quick_btn = gr.Button("⚡ Quick Question", variant="primary")
-            quick_output = gr.Markdown(label="Question")
+            quick_btn    = gr.Button("Generate Quick Question", variant="primary")
+            quick_output = gr.Markdown(value="_Enter a topic above and click Generate_")
             quick_scheme = gr.State("")
-            quick_model = gr.State("")
+            quick_model  = gr.State("")
 
-            def quick_question(topic, subject, diff):
-                qtypes = QUESTION_TYPES[subject]
-                import random
-                q, scheme, model, tag = generate_question(subject, topic, random.choice(qtypes), diff, 5)
+            def quick_q(topic, subject, diff):
+                q_type = random.choice(QUESTION_TYPES[subject])
+                q, scheme, model, _ = generate_question(subject, topic, q_type, diff, 5)
                 return q, scheme, model
 
-            quick_btn.click(quick_question, [quick_topic, quick_subject, quick_diff],
-                            [quick_output, quick_scheme, quick_model])
-
+            quick_btn.click(
+                quick_q,
+                [quick_topic, quick_subject, quick_diff],
+                [quick_output, quick_scheme, quick_model]
+            )
 
-    # Footer
     gr.Markdown("""
 ---
-**AI System:** 🟢 Gemini 2.5 (Primary) → 🔵 Cohere Command-R+ → 🟢 Z.ai GLM-4.6 → 🟣 MiniMax M2
+**AI System:** Gemini 2.5 Pro (Primary) → Cohere Command-R+ (Fallback)
 
-_IGCSE Science Question & Marking Platform — Built for deep understanding through practice_
+Add `GEMINI_API_KEY` and/or `COHERE_API_KEY` in Space Secrets to activate.
     """)