Update app.py

app.py

@@ -1,771 +1,276 @@
-# import pickle
-# import faiss
-# from flask import Flask, request, jsonify, render_template_string
-# from sentence_transformers import SentenceTransformer
-# from transformers import AutoTokenizer, AutoModelForCausalLM
-# from huggingface_hub import hf_hub_download
-# import torch
-# import os
-# from functools import lru_cache
-# import hashlib
-
-# app = Flask(__name__)
+import pickle
+import faiss
+from flask import Flask, request, jsonify, render_template_string
+from sentence_transformers import SentenceTransformer
+from huggingface_hub import hf_hub_download
+import hashlib
+import re
+import os
+import sys
 
-# print("=" * 50)
-# print("Loading models and data...")
-# print("=" * 50)
+# Import Groq
+try:
+    from groq import Groq
+    GROQ_AVAILABLE = True
+except ImportError:
+    print("❌ ERROR: groq package not installed!")
+    print("Add 'groq' to requirements.txt")
+    GROQ_AVAILABLE = False
+    sys.exit(1)
 
-# # ------------------------------
-# # Load embedding model (CPU)
-# # ------------------------------
-# embed_model = SentenceTransformer("all-MiniLM-L6-v2")
-# print("✓ Embedding model loaded")
+app = Flask(__name__)
 
-# # ------------------------------
-# # Download files from Hugging Face
-# # ------------------------------
-# REPO_ID = "Redfire-1234/pcb_tutor"
+print("=" * 50)
+print("STARTING MCQ GENERATOR APP")
+print("=" * 50)
 
-# print("Downloading subject files from Hugging Face...")
+# ------------------------------
+# Initialize Groq API Client
+# ------------------------------
+print("\nStep 1: Checking Groq API Key...")
+print("-" * 50)
 
-# # Download Biology files
-# bio_chunks_path = hf_hub_download(repo_id=REPO_ID, filename="bio_chunks.pkl", repo_type="model")
-# faiss_bio_path = hf_hub_download(repo_id=REPO_ID, filename="faiss_bio.bin", repo_type="model")
+GROQ_API_KEY = os.environ.get("GROQ_API_KEY", "").strip()
 
-# # Download Chemistry files
-# chem_chunks_path = hf_hub_download(repo_id=REPO_ID, filename="chem_chunks.pkl", repo_type="model")
-# faiss_chem_path = hf_hub_download(repo_id=REPO_ID, filename="faiss_chem.bin", repo_type="model")
+if not GROQ_API_KEY:
+    print("❌ GROQ_API_KEY not found!")
+    print("\nTo fix this:")
+    print("1. Go to: https://console.groq.com/keys")
+    print("2. Create a free API key")
+    print("3. In HuggingFace Space Settings → Repository secrets")
+    print("   Add: Name=GROQ_API_KEY, Value=<your-key>")
+    print("4. Restart your Space")
+    groq_client = None
+else:
+    print(f"✓ GROQ_API_KEY found ({len(GROQ_API_KEY)} chars)")
+    print(f"  First 20 chars: {GROQ_API_KEY[:20]}...")
+    
+    try:
+        groq_client = Groq(api_key=GROQ_API_KEY)
+        
+        # Test the API
+        print("  Testing API connection...")
+        test = groq_client.chat.completions.create(
+            messages=[{"role": "user", "content": "test"}],
+            model="llama-3.3-70b-versatile",
+            max_tokens=5
+        )
+        print("✓ Groq API working!")
+        
+    except Exception as e:
+        print(f"❌ Groq API initialization failed:")
+        print(f"   Error: {str(e)}")
+        groq_client = None
 
-# # Download Physics files
-# phy_chunks_path = hf_hub_download(repo_id=REPO_ID, filename="phy_chunks.pkl", repo_type="model")
-# faiss_phy_path = hf_hub_download(repo_id=REPO_ID, filename="faiss_phy.bin", repo_type="model")
+print("-" * 50)
 
-# # Load all subjects into memory
-# SUBJECTS = {
-#     "biology": {
-#         "chunks": pickle.load(open(bio_chunks_path, "rb")),
-#         "index": faiss.read_index(faiss_bio_path)
-#     },
-#     "chemistry": {
-#         "chunks": pickle.load(open(chem_chunks_path, "rb")),
-#         "index": faiss.read_index(faiss_chem_path)
-#     },
-#     "physics": {
-#         "chunks": pickle.load(open(phy_chunks_path, "rb")),
-#         "index": faiss.read_index(faiss_phy_path)
-#     }
-# }
+# ------------------------------
+# Load embedding model (CPU)
+# ------------------------------
+print("\nStep 2: Loading embedding model...")
+embed_model = SentenceTransformer("all-MiniLM-L6-v2")
+print("✓ Embedding model loaded")
 
-# print(f"✓ Biology: {len(SUBJECTS['biology']['chunks'])} chunks loaded")
-# print(f"✓ Chemistry: {len(SUBJECTS['chemistry']['chunks'])} chunks loaded")
-# print(f"✓ Physics: {len(SUBJECTS['physics']['chunks'])} chunks loaded")
+# ------------------------------
+# Download files from Hugging Face
+# ------------------------------
+REPO_ID = "Redfire-1234/pcb_tutor"
 
-# # ------------------------------
-# # Load LLM model (CPU) with optimizations
-# # ------------------------------
-# model_name = "Qwen/Qwen2.5-3B-Instruct"
-# print(f"Loading LLM: {model_name}")
-# tokenizer = AutoTokenizer.from_pretrained(model_name)
-# device = "cpu"
+print("\nStep 3: Downloading subject files...")
+print("-" * 50)
 
-# # OPTIMIZATION: Load model with better dtype for CPU
-# model = AutoModelForCausalLM.from_pretrained(
-#     model_name,
-#     torch_dtype=torch.float32,
-#     low_cpu_mem_usage=True  # Optimization: Better memory management
-# ).to(device)
+try:
+    bio_chunks_path = hf_hub_download(repo_id=REPO_ID, filename="bio_chunks.pkl", repo_type="model")
+    faiss_bio_path = hf_hub_download(repo_id=REPO_ID, filename="faiss_bio.bin", repo_type="model")
+    
+    chem_chunks_path = hf_hub_download(repo_id=REPO_ID, filename="chem_chunks.pkl", repo_type="model")
+    faiss_chem_path = hf_hub_download(repo_id=REPO_ID, filename="faiss_chem.bin", repo_type="model")
+    
+    phy_chunks_path = hf_hub_download(repo_id=REPO_ID, filename="phy_chunks.pkl", repo_type="model")
+    faiss_phy_path = hf_hub_download(repo_id=REPO_ID, filename="faiss_phy.bin", repo_type="model")
+    
+    print("✓ All files downloaded")
+except Exception as e:
+    print(f"❌ Error downloading files: {e}")
+    sys.exit(1)
 
-# # OPTIMIZATION: Set model to eval mode and optimize for inference
-# model.eval()
-# if hasattr(torch, 'set_num_threads'):
-#     torch.set_num_threads(4)  # Optimization: Use multiple CPU threads
+# Load all subjects into memory
+print("\nStep 4: Loading subject data into memory...")
+SUBJECTS = {
+    "biology": {
+        "chunks": pickle.load(open(bio_chunks_path, "rb")),
+        "index": faiss.read_index(faiss_bio_path)
+    },
+    "chemistry": {
+        "chunks": pickle.load(open(chem_chunks_path, "rb")),
+        "index": faiss.read_index(faiss_chem_path)
+    },
+    "physics": {
+        "chunks": pickle.load(open(phy_chunks_path, "rb")),
+        "index": faiss.read_index(faiss_phy_path)
+    }
+}
 
-# print(f"✓ LLM loaded on {device}")
+print(f"✓ Biology: {len(SUBJECTS['biology']['chunks'])} chunks")
+print(f"✓ Chemistry: {len(SUBJECTS['chemistry']['chunks'])} chunks")
+print(f"✓ Physics: {len(SUBJECTS['physics']['chunks'])} chunks")
 
-# print("=" * 50)
-# print("All models loaded successfully!")
-# print("=" * 50)
+print("\n" + "=" * 50)
+print("✓ ALL SYSTEMS READY!")
+print("=" * 50 + "\n")
 
-# # ------------------------------
-# # OPTIMIZATION: Add caching for MCQ generation
-# # ------------------------------
-# MCQ_CACHE = {}
-# MAX_CACHE_SIZE = 100
+# ------------------------------
+# Caching
+# ------------------------------
+MCQ_CACHE = {}
+MAX_CACHE_SIZE = 100
 
-# def get_cache_key(topic, subject, context_hash):
-#     """Generate a unique cache key"""
-#     return f"{subject}:{topic}:{context_hash}"
+def get_cache_key(topic, subject, context_hash):
+    return f"{subject}:{topic}:{context_hash}"
 
-# def cache_mcq(key, mcqs):
-#     """Cache generated MCQs with size limit"""
-#     if len(MCQ_CACHE) >= MAX_CACHE_SIZE:
-#         # Remove oldest entry
-#         MCQ_CACHE.pop(next(iter(MCQ_CACHE)))
-#     MCQ_CACHE[key] = mcqs
+def cache_mcq(key, mcqs):
+    if len(MCQ_CACHE) >= MAX_CACHE_SIZE:
+        MCQ_CACHE.pop(next(iter(MCQ_CACHE)))
+    MCQ_CACHE[key] = mcqs
 
-# # ------------------------------
-# # RAG Search in specific subject (optimized)
-# # ------------------------------
-# def rag_search(query, subject, k=5):
-#     if subject not in SUBJECTS:
-#         return None
+# ------------------------------
+# RAG Search
+# ------------------------------
+def rag_search(query, subject, k=5):
+    if subject not in SUBJECTS:
+        return None
     
-#     chunks = SUBJECTS[subject]["chunks"]
-#     index = SUBJECTS[subject]["index"]
+    chunks = SUBJECTS[subject]["chunks"]
+    index = SUBJECTS[subject]["index"]
     
-#     # OPTIMIZATION: Encode query (already fast with sentence-transformers)
-#     q_emb = embed_model.encode([query], show_progress_bar=False).astype("float32")
-#     D, I = index.search(q_emb, k)
+    q_emb = embed_model.encode([query], show_progress_bar=False).astype("float32")
+    D, I = index.search(q_emb, k)
     
-#     # Get the actual chunks
-#     results = []
-#     for idx in I[0]:
-#         if idx < len(chunks):
-#             results.append(chunks[idx])
+    results = []
+    for idx in I[0]:
+        if idx < len(chunks):
+            results.append(chunks[idx])
     
-#     return "\n\n".join(results)
+    return "\n\n".join(results)
 
-# # ------------------------------
-# # OPTIMIZED MCQ Generation with reduced tokens
-# # ------------------------------
-# def generate_mcqs(context, topic, subject):
-#     # OPTIMIZATION: Check cache first
-#     context_hash = hashlib.md5(context.encode()).hexdigest()[:8]
-#     cache_key = get_cache_key(topic, subject, context_hash)
-    
-#     if cache_key in MCQ_CACHE:
-#         print("✓ Using cached MCQs")
-#         return MCQ_CACHE[cache_key]
-    
-#     # OPTIMIZATION: Shortened prompt for faster generation
-#     prompt = f"""You are a Class-12 {subject.title()} teacher creating MCQs.
-# Topic: "{topic}"
-# Context:
-# {context}
+# ------------------------------
+# MCQ Generation
+# ------------------------------
+def generate_mcqs(context, topic, subject):
+    # Check if Groq is available
+    if not groq_client:
+        error_msg = """ERROR: Groq API not initialized!
 
-# Generate exactly 5 MCQs in this format:
-# Q1. [Question]
-# A) [Option]
-# B) [Option]
-# C) [Option]
-# D) [Option]
-# Correct Answer: [Letter] - [Reason]
+Please check:
+1. GROQ_API_KEY is set in Space Settings → Repository secrets
+2. API key is valid (get one from https://console.groq.com/keys)
+3. Space has been restarted after adding the key
 
-# Rules: Make correct answer from context, realistic distractors.
-# Generate 5 MCQs:"""
-    
-#     # OPTIMIZATION: Reduced max_length for faster tokenization
-#     inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1536).to(device)
-    
-#     # OPTIMIZATION: Use torch.no_grad() for inference (saves memory)
-#     with torch.no_grad():
-#         # OPTIMIZATION: Reduced max_new_tokens from 900 to 600 (sufficient for 5 MCQs)
-#         # OPTIMIZATION: Reduced temperature from 0.15 to 0.1 (faster, more deterministic)
-#         # OPTIMIZATION: Added num_beams=1 (greedy decoding, faster than sampling)
-#         outputs = model.generate(
-#             **inputs,
-#             max_new_tokens=600,  # Reduced from 900
-#             temperature=0.1,     # Reduced from 0.15
-#             top_p=0.85,          # Slightly adjusted
-#             do_sample=True,
-#             repetition_penalty=1.15,
-#             pad_token_id=tokenizer.eos_token_id  # Optimization: Explicit pad token
-#         )
+Current status: API key not found or invalid."""
+        return error_msg
     
-#     result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Check cache
+    context_hash = hashlib.md5(context.encode()).hexdigest()[:8]
+    cache_key = get_cache_key(topic, subject, context_hash)
     
-#     # Extract only the generated MCQs
-#     if "Generate 5 MCQs:" in result:
-#         result = result.split("Generate 5 MCQs:")[-1].strip()
+    if cache_key in MCQ_CACHE:
+        print("✓ Using cached MCQs")
+        return MCQ_CACHE[cache_key]
     
-#     # OPTIMIZATION: Cache the result
-#     cache_mcq(cache_key, result)
+    print(f"🤖 Generating MCQs for {subject} - {topic}")
     
-#     return result
+    prompt = f"""You are a Class-12 {subject.title()} teacher creating MCQs.
 
-# def verify_and_correct_answers(mcqs_text, context):
-#     """
-#     This function is kept for future enhancements
-#     """
-#     return mcqs_text
+Topic: "{topic}"
 
-# # ------------------------------
-# # HTML UI (with improved loading message)
-# # ------------------------------
-# HTML_TEMPLATE = """
-# <!DOCTYPE html>
-# <html>
-# <head>
-#     <title>Class 12 PCB MCQ Generator</title>
-#     <style>
-#         * { margin: 0; padding: 0; box-sizing: border-box; }
-#         body {
-#             font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-#             background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-#             min-height: 100vh;
-#             padding: 20px;
-#         }
-#         .container {
-#             max-width: 900px;
-#             margin: 0 auto;
-#             background: white;
-#             border-radius: 20px;
-#             box-shadow: 0 20px 60px rgba(0,0,0,0.3);
-#             overflow: hidden;
-#         }
-#         .header {
-#             background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-#             color: white;
-#             padding: 30px;
-#             text-align: center;
-#         }
-#         .header h1 { font-size: 2.5em; margin-bottom: 10px; }
-#         .content { padding: 40px; }
-#         .form-group {
-#             margin-bottom: 25px;
-#         }
-#         label {
-#             display: block;
-#             font-weight: 600;
-#             margin-bottom: 10px;
-#             color: #333;
-#             font-size: 16px;
-#         }
-#         select, input {
-#             width: 100%;
-#             padding: 15px;
-#             border: 2px solid #e0e0e0;
-#             border-radius: 10px;
-#             font-size: 16px;
-#             font-family: inherit;
-#             transition: border-color 0.3s;
-#         }
-#         select:focus, input:focus {
-#             outline: none;
-#             border-color: #667eea;
-#         }
-#         button {
-#             width: 100%;
-#             padding: 18px;
-#             background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-#             color: white;
-#             border: none;
-#             border-radius: 10px;
-#             font-size: 18px;
-#             font-weight: 600;
-#             cursor: pointer;
-#             transition: all 0.3s;
-#         }
-#         button:hover {
-#             transform: translateY(-2px);
-#             box-shadow: 0 10px 20px rgba(102, 126, 234, 0.4);
-#         }
-#         button:disabled {
-#             background: #ccc;
-#             cursor: not-allowed;
-#             transform: none;
-#         }
-#         .result {
-#             margin-top: 30px;
-#             padding: 25px;
-#             background: #f8f9fa;
-#             border-radius: 10px;
-#             border-left: 4px solid #667eea;
-#             display: none;
-#         }
-#         .result.show { display: block; }
-#         .result h3 {
-#             color: #667eea;
-#             margin-bottom: 20px;
-#             font-size: 1.4em;
-#         }
-#         .mcq-content {
-#             background: white;
-#             padding: 25px;
-#             border-radius: 8px;
-#             white-space: pre-wrap;
-#             line-height: 1.9;
-#             font-size: 15px;
-#         }
-#         .loading {
-#             text-align: center;
-#             padding: 30px;
-#             display: none;
-#         }
-#         .loading.show { display: block; }
-#         .spinner {
-#             border: 4px solid #f3f3f3;
-#             border-top: 4px solid #667eea;
-#             border-radius: 50%;
-#             width: 50px;
-#             height: 50px;
-#             animation: spin 1s linear infinite;
-#             margin: 0 auto 15px;
-#         }
-#         @keyframes spin {
-#             0% { transform: rotate(0deg); }
-#             100% { transform: rotate(360deg); }
-#         }
-#         .subject-tag {
-#             display: inline-block;
-#             padding: 5px 15px;
-#             border-radius: 20px;
-#             font-size: 13px;
-#             font-weight: 600;
-#             margin-right: 10px;
-#         }
-#         .bio { background: #d4edda; color: #155724; }
-#         .chem { background: #d1ecf1; color: #0c5460; }
-#         .phy { background: #f8d7da; color: #721c24; }
-#         .optimization-badge {
-#             background: #28a745;
-#             color: white;
-#             padding: 5px 12px;
-#             border-radius: 15px;
-#             font-size: 12px;
-#             margin-left: 10px;
-#         }
-#     </style>
-# </head>
-# <body>
-#     <div class="container">
-#         <div class="header">
-#             <h1>🎓 Class 12 PCB MCQ Generator</h1>
-#             <p style="font-size: 1.1em; margin-bottom: 15px;">Generate practice MCQs from your textbooks <span class="optimization-badge">⚡ Optimized</span></p>
-#             <div>
-#                 <span class="subject-tag bio">Biology</span>
-#                 <span class="subject-tag chem">Chemistry</span>
-#                 <span class="subject-tag phy">Physics</span>
-#             </div>
-#         </div>
+Reference material from textbook:
+{context[:1500]}
+
+Generate exactly 5 multiple-choice questions based on the reference material.
+
+FORMAT (follow EXACTLY):
+Q1. [Question based on material]
+A) [Option 1]
+B) [Option 2]
+C) [Option 3]
+D) [Option 4]
+Answer: [A/B/C/D] - [Brief explanation]
+
+Q2. [Question based on material]
+A) [Option 1]
+B) [Option 2]
+C) [Option 3]
+D) [Option 4]
+Answer: [A/B/C/D] - [Brief explanation]
+
+Continue for Q3, Q4, Q5.
+
+REQUIREMENTS:
+- All questions must be answerable from the reference material
+- All 4 options should be plausible
+- Correct answer must be clearly supported by material
+- Keep explanations brief (1-2 sentences)
+
+Generate 5 MCQs now:"""
+    
+    try:
+        chat_completion = groq_client.chat.completions.create(
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are an expert Class-12 teacher who creates high-quality MCQs from textbook content. You always follow the exact format specified."
+                },
+                {
+                    "role": "user",
+                    "content": prompt
+                }
+            ],
+            model="llama-3.3-70b-versatile",
+            temperature=0.3,
+            max_tokens=1500,
+            top_p=0.9
+        )
         
-#         <div class="content">
-#             <div class="form-group">
-#                 <label for="subject">📚 Select Subject</label>
-#                 <select id="subject">
-#                     <option value="biology">Biology</option>
-#                     <option value="chemistry">Chemistry</option>
-#                     <option value="physics">Physics</option>
-#                 </select>
-#             </div>
-            
-#             <div class="form-group">
-#                 <label for="topic">✏️ Enter Topic</label>
-#                 <input type="text" id="topic" placeholder="e.g., Mitochondria, Chemical Bonding, Newton's Laws">
-#             </div>
-            
-#             <button onclick="generateMCQs()">🚀 Generate 5 MCQs</button>
-            
-#             <div class="loading" id="loading">
-#                 <div class="spinner"></div>
-#                 <p style="color: #666; font-size: 16px;">Generating MCQs... This may take 20-40 seconds</p>
-#                 <p style="color: #999; font-size: 13px; margin-top: 10px;">⚡ Optimized for faster generation</p>
-#             </div>
-            
-#             <div class="result" id="result">
-#                 <h3>📝 Generated MCQs:</h3>
-#                 <div style="background: #fff3cd; padding: 12px; border-radius: 6px; margin-bottom: 15px; color: #856404; font-size: 14px;">
-#                     ⚠️ <strong>Note:</strong> AI-generated answers may occasionally be incorrect. Please verify answers using your textbook.
-#                 </div>
-#                 <div class="mcq-content" id="mcqContent"></div>
-#             </div>
-#         </div>
-#     </div>
-#     <script>
-#         async function generateMCQs() {
-#             const subject = document.getElementById('subject').value;
-#             const topic = document.getElementById('topic').value.trim();
-            
-#             if (!topic) {
-#                 alert('⚠️ Please enter a topic!');
-#                 return;
-#             }
-            
-#             const loading = document.getElementById('loading');
-#             const result = document.getElementById('result');
-#             const btn = document.querySelector('button');
+        result = chat_completion.choices[0].message.content.strip()
+        result = clean_mcq_output(result)
+        
+        cache_mcq(cache_key, result)
+        
+        print("✓ MCQs generated successfully")
+        return result
+        
+    except Exception as e:
+        error_msg = f"""Error calling Groq API: {str(e)}
+
+Possible causes:
+1. Rate limit exceeded (wait a moment)
+2. Invalid API key
+3. Network issue
+
+Please try again in a few seconds."""
+        print(f"❌ Groq API Error: {e}")
+        return error_msg
+
+def clean_mcq_output(text):
+    lines = text.split('\n')
+    cleaned_lines = []
+    
+    for line in lines:
+        line = line.strip()
+        
+        if (re.match(r'^Q\d+\.', line) or 
+            line.startswith(('A)', 'B)', 'C)', 'D)', 'Answer:', 'Correct Answer:')) or
+            not line):
             
-#             loading.classList.add('show');
-#             result.classList.remove('show');
-#             btn.disabled = true;
-#             btn.textContent = '⏳ Generating...';
+            if line.startswith('Correct Answer:'):
+                line = line.replace('Correct Answer:', 'Answer:')
             
-#             try {
-#                 const response = await fetch('/generate', {
-#                     method: 'POST',
-#                     headers: {'Content-Type': 'application/json'},
-#                     body: JSON.stringify({subject, topic})
-#                 });
-                
-#                 const data = await response.json();
-                
-#                 if (data.error) {
-#                     alert('❌ Error: ' + data.error);
-#                     return;
-#                 }
-                
-#                 document.getElementById('mcqContent').textContent = data.mcqs;
-#                 result.classList.add('show');
-#             } catch (error) {
-#                 alert('❌ Error: ' + error.message);
-#             } finally {
-#                 loading.classList.remove('show');
-#                 btn.disabled = false;
-#                 btn.textContent = '🚀 Generate 5 MCQs';
-#             }
-#         }
-        
-#         // Allow Enter key to submit
-#         document.getElementById('topic').addEventListener('keypress', function(e) {
-#             if (e.key === 'Enter') {
-#                 generateMCQs();
-#             }
-#         });
-#     </script>
-# </body>
-# </html>
-# """
-
-# # ------------------------------
-# # Routes
-# # ------------------------------
-# @app.route("/")
-# def home():
-#     return render_template_string(HTML_TEMPLATE)
-
-# @app.route("/generate", methods=["POST"])
-# def generate():
-#     try:
-#         data = request.json
-#         subject = data.get("subject", "").lower()
-#         topic = data.get("topic", "")
-        
-#         if not topic:
-#             return jsonify({"error": "Topic is required"}), 400
-        
-#         if subject not in SUBJECTS:
-#             return jsonify({"error": "Invalid subject. Choose biology, chemistry, or physics."}), 400
-        
-#         print(f"\n🔍 Searching {subject} for topic: {topic}")
-        
-#         # Retrieve context from RAG
-#         context = rag_search(topic, subject, k=5)
-        
-#         if not context or len(context.strip()) < 50:
-#             return jsonify({"error": f"No relevant content found in {subject} for topic: {topic}"}), 404
-        
-#         print(f"✓ Found context ({len(context)} chars)")
-        
-#         # Generate MCQs (now with caching)
-#         print("🤖 Generating MCQs...")
-#         mcqs = generate_mcqs(context, topic, subject)
-        
-#         print("✓ MCQs generated successfully")
-        
-#         return jsonify({"mcqs": mcqs, "subject": subject})
-    
-#     except Exception as e:
-#         print(f"❌ Error in /generate: {e}")
-#         import traceback
-#         traceback.print_exc()
-#         return jsonify({"error": str(e)}), 500
-
-# @app.route("/health")
-# def health():
-#     return jsonify({
-#         "status": "healthy",
-#         "subjects": {
-#             "biology": len(SUBJECTS["biology"]["chunks"]),
-#             "chemistry": len(SUBJECTS["chemistry"]["chunks"]),
-#             "physics": len(SUBJECTS["physics"]["chunks"])
-#         },
-#         "cache_size": len(MCQ_CACHE)
-#     })
-
-# # OPTIMIZATION: Add cache stats endpoint
-# @app.route("/cache/stats")
-# def cache_stats():
-#     return jsonify({
-#         "cached_topics": len(MCQ_CACHE),
-#         "max_cache_size": MAX_CACHE_SIZE,
-#         "cache_keys": list(MCQ_CACHE.keys())
-#     })
-
-# # OPTIMIZATION: Add cache clear endpoint (optional)
-# @app.route("/cache/clear", methods=["POST"])
-# def clear_cache():
-#     MCQ_CACHE.clear()
-#     return jsonify({"status": "Cache cleared"})
-
-# # ------------------------------
-# # Run the App
-# # ------------------------------
-# if __name__ == "__main__":
-#     port = int(os.environ.get("PORT", 7860))
-#     print(f"\n🚀 Starting Flask on 0.0.0.0:{port}\n")
-#     app.run(host="0.0.0.0", port=port, debug=False)
-
-
-import pickle
-import faiss
-from flask import Flask, request, jsonify, render_template_string
-from sentence_transformers import SentenceTransformer
-from huggingface_hub import hf_hub_download
-import hashlib
-import re
-import os
-from groq import Groq
-
-app = Flask(__name__)
-
-print("=" * 50)
-print("Loading models and data...")
-print("=" * 50)
-
-# ------------------------------
-# Initialize Groq API Client
-# ------------------------------
-import sys
-
-# Get API key from Hugging Face Secrets or environment variable
-GROQ_API_KEY = os.environ.get("GROQ_API_KEY", "").strip()
-
-print("=" * 50)
-print("Checking Groq API Key...")
-print("=" * 50)
-
-if not GROQ_API_KEY:
-    print("❌ ERROR: GROQ_API_KEY not found in environment!")
-    print("")
-    print("Please add it in:")
-    print("1. Hugging Face Space Settings → Repository secrets")
-    print("   Name: GROQ_API_KEY")
-    print("   Value: Your Groq API key")
-    print("")
-    print("2. Get your free key from: https://console.groq.com/keys")
-    print("=" * 50)
-    groq_client = None
-else:
-    print(f"✓ Groq API key found!")
-    print(f"  Key length: {len(GROQ_API_KEY)} characters")
-    print(f"  Starts with: {GROQ_API_KEY[:15]}...")
-    print(f"  Ends with: ...{GROQ_API_KEY[-10:]}")
-    
-    try:
-        groq_client = Groq(api_key=GROQ_API_KEY)
-        
-        # Test the connection with a simple request
-        print("Testing Groq API connection...")
-        test_response = groq_client.chat.completions.create(
-            messages=[{"role": "user", "content": "Hi"}],
-            model="llama-3.3-70b-versatile",
-            max_tokens=10
-        )
-        print("✓ Groq API client initialized and tested successfully!")
-        print("=" * 50)
-        
-    except Exception as e:
-        print(f"❌ Failed to initialize Groq client!")
-        print(f"Error type: {type(e).__name__}")
-        print(f"Error message: {str(e)}")
-        print("=" * 50)
-        print("")
-        print("Common causes:")
-        print("1. Invalid API key - Get a new one from https://console.groq.com/keys")
-        print("2. API key has extra spaces - Check for whitespace")
-        print("3. Network issue - Check internet connection")
-        print("=" * 50)
-        groq_client = None
-        import traceback
-        traceback.print_exc()
-
-# ------------------------------
-# Load embedding model (CPU)
-# ------------------------------
-embed_model = SentenceTransformer("all-MiniLM-L6-v2")
-print("✓ Embedding model loaded")
-
-# ------------------------------
-# Download files from Hugging Face
-# ------------------------------
-REPO_ID = "Redfire-1234/pcb_tutor"
-
-print("Downloading subject files from Hugging Face...")
-
-# Download Biology files
-bio_chunks_path = hf_hub_download(repo_id=REPO_ID, filename="bio_chunks.pkl", repo_type="model")
-faiss_bio_path = hf_hub_download(repo_id=REPO_ID, filename="faiss_bio.bin", repo_type="model")
-
-# Download Chemistry files
-chem_chunks_path = hf_hub_download(repo_id=REPO_ID, filename="chem_chunks.pkl", repo_type="model")
-faiss_chem_path = hf_hub_download(repo_id=REPO_ID, filename="faiss_chem.bin", repo_type="model")
-
-# Download Physics files
-phy_chunks_path = hf_hub_download(repo_id=REPO_ID, filename="phy_chunks.pkl", repo_type="model")
-faiss_phy_path = hf_hub_download(repo_id=REPO_ID, filename="faiss_phy.bin", repo_type="model")
-
-# Load all subjects into memory
-SUBJECTS = {
-    "biology": {
-        "chunks": pickle.load(open(bio_chunks_path, "rb")),
-        "index": faiss.read_index(faiss_bio_path)
-    },
-    "chemistry": {
-        "chunks": pickle.load(open(chem_chunks_path, "rb")),
-        "index": faiss.read_index(faiss_chem_path)
-    },
-    "physics": {
-        "chunks": pickle.load(open(phy_chunks_path, "rb")),
-        "index": faiss.read_index(faiss_phy_path)
-    }
-}
-
-print(f"✓ Biology: {len(SUBJECTS['biology']['chunks'])} chunks loaded")
-print(f"✓ Chemistry: {len(SUBJECTS['chemistry']['chunks'])} chunks loaded")
-print(f"✓ Physics: {len(SUBJECTS['physics']['chunks'])} chunks loaded")
-
-print("=" * 50)
-print("All models loaded successfully!")
-print("=" * 50)
-
-# ------------------------------
-# Caching for MCQ generation
-# ------------------------------
-MCQ_CACHE = {}
-MAX_CACHE_SIZE = 100
-
-def get_cache_key(topic, subject, context_hash):
-    """Generate a unique cache key"""
-    return f"{subject}:{topic}:{context_hash}"
-
-def cache_mcq(key, mcqs):
-    """Cache generated MCQs with size limit"""
-    if len(MCQ_CACHE) >= MAX_CACHE_SIZE:
-        MCQ_CACHE.pop(next(iter(MCQ_CACHE)))
-    MCQ_CACHE[key] = mcqs
-
-# ------------------------------
-# RAG Search in specific subject
-# ------------------------------
-def rag_search(query, subject, k=5):
-    if subject not in SUBJECTS:
-        return None
-    
-    chunks = SUBJECTS[subject]["chunks"]
-    index = SUBJECTS[subject]["index"]
-    
-    q_emb = embed_model.encode([query], show_progress_bar=False).astype("float32")
-    D, I = index.search(q_emb, k)
-    
-    results = []
-    for idx in I[0]:
-        if idx < len(chunks):
-            results.append(chunks[idx])
+            cleaned_lines.append(line)
     
-    return "\n\n".join(results)
+    return '\n'.join(cleaned_lines)
 
 # ------------------------------
-# MCQ Generation using Groq API
-# ------------------------------
-def generate_mcqs(context, topic, subject):
-    # Check if Groq client is available
-    if not groq_client:
-        return "Error: Groq API client not initialized. Please check your GROQ_API_KEY in Space settings."
-    
-    # Check cache first
-    context_hash = hashlib.md5(context.encode()).hexdigest()[:8]
-    cache_key = get_cache_key(topic, subject, context_hash)
-    
-    if cache_key in MCQ_CACHE:
-        print("✓ Using cached MCQs")
-        return MCQ_CACHE[cache_key]
-    
-    print("🤖 Generating MCQs using Groq API...")
-    
-    # Prompt for MCQ generation
-    prompt = f"""You are a Class-12 {subject.title()} teacher creating MCQs for students.
-
-Topic: "{topic}"
-
-Reference material from textbook:
-{context[:1500]}
-
-TASK: Generate exactly 5 multiple-choice questions based on the reference material above.
-
-FORMAT (follow this EXACTLY):
-Q1. [Clear question based on the material]
-A) [First option]
-B) [Second option]
-C) [Third option]
-D) [Fourth option]
-Answer: [A/B/C/D] - [Brief explanation why this is correct based on the material]
-
-REQUIREMENTS:
-- Questions must be answerable from the reference material
-- All 4 options should be plausible
-- The correct answer must be clearly supported by the material
-- Keep explanations brief (1-2 sentences)
-- Generate all 5 questions in the format above
-
-Generate 5 MCQs now:"""
-    
-    try:
-        # Call Groq API
-        chat_completion = groq_client.chat.completions.create(
-            messages=[
-                {
-                    "role": "system",
-                    "content": "You are an expert Class-12 teacher who creates high-quality multiple-choice questions from textbook content. You always follow the exact format specified."
-                },
-                {
-                    "role": "user",
-                    "content": prompt
-                }
-            ],
-            model="llama-3.3-70b-versatile",  # Fast and accurate
-            temperature=0.3,
-            max_tokens=1500,
-            top_p=0.9
-        )
-        
-        result = chat_completion.choices[0].message.content.strip()
-        
-        # Clean the output
-        result = clean_mcq_output(result)
-        
-        # Cache the result
-        cache_mcq(cache_key, result)
-        
-        print("✓ MCQs generated successfully")
-        return result
-        
-    except Exception as e:
-        print(f"❌ Groq API Error: {e}")
-        return f"Error generating MCQs: {str(e)}\n\nPlease make sure GROQ_API_KEY is set correctly."
-
-def clean_mcq_output(text):
-    """Clean and format the MCQ output"""
-    lines = text.split('\n')
-    cleaned_lines = []
-    
-    for line in lines:
-        line = line.strip()
-        
-        # Keep question lines, options, and answers
-        if (re.match(r'^Q\d+\.', line) or 
-            line.startswith(('A)', 'B)', 'C)', 'D)', 'Answer:', 'Correct Answer:')) or
-            not line):
-            
-            # Normalize answer format
-            if line.startswith('Correct Answer:'):
-                line = line.replace('Correct Answer:', 'Answer:')
-            
-            cleaned_lines.append(line)
-    
-    return '\n'.join(cleaned_lines)
-
-# ------------------------------
-# HTML UI
+# HTML UI
 # ------------------------------
 HTML_TEMPLATE = """
 <!DOCTYPE html>
@@ -906,7 +411,7 @@ HTML_TEMPLATE = """
             <h1>🎓 Class 12 PCB MCQ Generator</h1>
             <p style="font-size: 1.1em; margin-bottom: 15px;">
                 Generate practice MCQs from your textbooks 
-                <span class="api-badge">⚡ Powered by Llama 3.3 70B</span>
+                <span class="api-badge">⚡ Llama 3.3 70B</span>
             </p>
             <div>
                 <span class="subject-tag bio">Biology</span>
@@ -915,148 +420,652 @@ HTML_TEMPLATE = """
             </div>
         </div>
         
-        <div class="content">
-            <div class="form-group">
-                <label for="subject">📚 Select Subject</label>
-                <select id="subject">
-                    <option value="biology">Biology</option>
-                    <option value="chemistry">Chemistry</option>
-                    <option value="physics">Physics</option>
-                </select>
-            </div>
+        <div class="content">
+            <div class="form-group">
+                <label for="subject">📚 Select Subject</label>
+                <select id="subject">
+                    <option value="biology">Biology</option>
+                    <option value="chemistry">Chemistry</option>
+                    <option value="physics">Physics</option>
+                </select>
+            </div>
+            
+            <div class="form-group">
+                <label for="topic">✏️ Enter Topic</label>
+                <input type="text" id="topic" placeholder="e.g., Mitochondria, Chemical Bonding, Newton's Laws">
+            </div>
+            
+            <button onclick="generateMCQs()">🚀 Generate 5 MCQs</button>
+            
+            <div class="loading" id="loading">
+                <div class="spinner"></div>
+                <p style="color: #666; font-size: 16px;">Generating MCQs with AI...</p>
+                <p style="color: #999; font-size: 13px; margin-top: 10px;">⚡ Usually takes 5-10 seconds</p>
+            </div>
+            
+            <div class="result" id="result">
+                <h3>📝 Generated MCQs:</h3>
+                <div style="background: #d4edda; padding: 12px; border-radius: 6px; margin-bottom: 15px; color: #155724; font-size: 14px;">
+                    ✓ <strong>High Quality:</strong> Generated by Llama 3.3 70B via Groq API
+                </div>
+                <div class="mcq-content" id="mcqContent"></div>
+            </div>
+        </div>
+    </div>
+    <script>
+        async function generateMCQs() {
+            const subject = document.getElementById('subject').value;
+            const topic = document.getElementById('topic').value.trim();
+            
+            if (!topic) {
+                alert('⚠️ Please enter a topic!');
+                return;
+            }
+            
+            const loading = document.getElementById('loading');
+            const result = document.getElementById('result');
+            const btn = document.querySelector('button');
+            
+            loading.classList.add('show');
+            result.classList.remove('show');
+            btn.disabled = true;
+            btn.textContent = '⏳ Generating...';
+            
+            try {
+                const response = await fetch('/generate', {
+                    method: 'POST',
+                    headers: {'Content-Type': 'application/json'},
+                    body: JSON.stringify({subject, topic})
+                });
+                
+                const data = await response.json();
+                
+                if (data.error) {
+                    alert('❌ Error: ' + data.error);
+                    return;
+                }
+                
+                document.getElementById('mcqContent').textContent = data.mcqs;
+                result.classList.add('show');
+            } catch (error) {
+                alert('❌ Error: ' + error.message);
+            } finally {
+                loading.classList.remove('show');
+                btn.disabled = false;
+                btn.textContent = '🚀 Generate 5 MCQs';
+            }
+        }
+        
+        document.getElementById('topic').addEventListener('keypress', function(e) {
+            if (e.key === 'Enter') {
+                generateMCQs();
+            }
+        });
+    </script>
+</body>
+</html>
+"""
+
+# ------------------------------
+# Routes
+# ------------------------------
+@app.route("/")
+def home():
+    return render_template_string(HTML_TEMPLATE)
+
+@app.route("/generate", methods=["POST"])
+def generate():
+    try:
+        data = request.json
+        subject = data.get("subject", "").lower()
+        topic = data.get("topic", "")
+        
+        if not topic:
+            return jsonify({"error": "Topic is required"}), 400
+        
+        if subject not in SUBJECTS:
+            return jsonify({"error": "Invalid subject"}), 400
+        
+        print(f"\n🔍 Searching {subject} for: {topic}")
+        
+        context = rag_search(topic, subject, k=5)
+        
+        if not context or len(context.strip()) < 50:
+            return jsonify({"error": f"No content found for: {topic}"}), 404
+        
+        print(f"✓ Context found ({len(context)} chars)")
+        
+        mcqs = generate_mcqs(context, topic, subject)
+        
+        return jsonify({"mcqs": mcqs, "subject": subject})
+    
+    except Exception as e:
+        print(f"❌ Error: {e}")
+        import traceback
+        traceback.print_exc()
+        return jsonify({"error": str(e)}), 500
+
+@app.route("/health")
+def health():
+    return jsonify({
+        "status": "healthy",
+        "groq_available": groq_client is not None,
+        "cache_size": len(MCQ_CACHE)
+    })
+
+# ------------------------------
+# Run
+# ------------------------------
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 7860))
+    print(f"\n🚀 Starting server on port {port}...\n")
+    app.run(host="0.0.0.0", port=port, debug=False)
+
+# import pickle
+# import faiss
+# from flask import Flask, request, jsonify, render_template_string
+# from sentence_transformers import SentenceTransformer
+# from transformers import AutoTokenizer, AutoModelForCausalLM
+# from huggingface_hub import hf_hub_download
+# import torch
+# import os
+# from functools import lru_cache
+# import hashlib
+
+# app = Flask(__name__)
+
+# print("=" * 50)
+# print("Loading models and data...")
+# print("=" * 50)
+
+# # ------------------------------
+# # Load embedding model (CPU)
+# # ------------------------------
+# embed_model = SentenceTransformer("all-MiniLM-L6-v2")
+# print("✓ Embedding model loaded")
+
+# # ------------------------------
+# # Download files from Hugging Face
+# # ------------------------------
+# REPO_ID = "Redfire-1234/pcb_tutor"
+
+# print("Downloading subject files from Hugging Face...")
+
+# # Download Biology files
+# bio_chunks_path = hf_hub_download(repo_id=REPO_ID, filename="bio_chunks.pkl", repo_type="model")
+# faiss_bio_path = hf_hub_download(repo_id=REPO_ID, filename="faiss_bio.bin", repo_type="model")
+
+# # Download Chemistry files
+# chem_chunks_path = hf_hub_download(repo_id=REPO_ID, filename="chem_chunks.pkl", repo_type="model")
+# faiss_chem_path = hf_hub_download(repo_id=REPO_ID, filename="faiss_chem.bin", repo_type="model")
+
+# # Download Physics files
+# phy_chunks_path = hf_hub_download(repo_id=REPO_ID, filename="phy_chunks.pkl", repo_type="model")
+# faiss_phy_path = hf_hub_download(repo_id=REPO_ID, filename="faiss_phy.bin", repo_type="model")
+
+# # Load all subjects into memory
+# SUBJECTS = {
+#     "biology": {
+#         "chunks": pickle.load(open(bio_chunks_path, "rb")),
+#         "index": faiss.read_index(faiss_bio_path)
+#     },
+#     "chemistry": {
+#         "chunks": pickle.load(open(chem_chunks_path, "rb")),
+#         "index": faiss.read_index(faiss_chem_path)
+#     },
+#     "physics": {
+#         "chunks": pickle.load(open(phy_chunks_path, "rb")),
+#         "index": faiss.read_index(faiss_phy_path)
+#     }
+# }
+
+# print(f"✓ Biology: {len(SUBJECTS['biology']['chunks'])} chunks loaded")
+# print(f"✓ Chemistry: {len(SUBJECTS['chemistry']['chunks'])} chunks loaded")
+# print(f"✓ Physics: {len(SUBJECTS['physics']['chunks'])} chunks loaded")
+
+# # ------------------------------
+# # Load LLM model (CPU) with optimizations
+# # ------------------------------
+# model_name = "Qwen/Qwen2.5-3B-Instruct"
+# print(f"Loading LLM: {model_name}")
+# tokenizer = AutoTokenizer.from_pretrained(model_name)
+# device = "cpu"
+
+# # OPTIMIZATION: Load model with better dtype for CPU
+# model = AutoModelForCausalLM.from_pretrained(
+#     model_name,
+#     torch_dtype=torch.float32,
+#     low_cpu_mem_usage=True  # Optimization: Better memory management
+# ).to(device)
+
+# # OPTIMIZATION: Set model to eval mode and optimize for inference
+# model.eval()
+# if hasattr(torch, 'set_num_threads'):
+#     torch.set_num_threads(4)  # Optimization: Use multiple CPU threads
+
+# print(f"✓ LLM loaded on {device}")
+
+# print("=" * 50)
+# print("All models loaded successfully!")
+# print("=" * 50)
+
+# # ------------------------------
+# # OPTIMIZATION: Add caching for MCQ generation
+# # ------------------------------
+# MCQ_CACHE = {}
+# MAX_CACHE_SIZE = 100
+
+# def get_cache_key(topic, subject, context_hash):
+#     """Generate a unique cache key"""
+#     return f"{subject}:{topic}:{context_hash}"
+
+# def cache_mcq(key, mcqs):
+#     """Cache generated MCQs with size limit"""
+#     if len(MCQ_CACHE) >= MAX_CACHE_SIZE:
+#         # Remove oldest entry
+#         MCQ_CACHE.pop(next(iter(MCQ_CACHE)))
+#     MCQ_CACHE[key] = mcqs
+
+# # ------------------------------
+# # RAG Search in specific subject (optimized)
+# # ------------------------------
+# def rag_search(query, subject, k=5):
+#     if subject not in SUBJECTS:
+#         return None
+    
+#     chunks = SUBJECTS[subject]["chunks"]
+#     index = SUBJECTS[subject]["index"]
+    
+#     # OPTIMIZATION: Encode query (already fast with sentence-transformers)
+#     q_emb = embed_model.encode([query], show_progress_bar=False).astype("float32")
+#     D, I = index.search(q_emb, k)
+    
+#     # Get the actual chunks
+#     results = []
+#     for idx in I[0]:
+#         if idx < len(chunks):
+#             results.append(chunks[idx])
+    
+#     return "\n\n".join(results)
+
+# # ------------------------------
+# # OPTIMIZED MCQ Generation with reduced tokens
+# # ------------------------------
+# def generate_mcqs(context, topic, subject):
+#     # OPTIMIZATION: Check cache first
+#     context_hash = hashlib.md5(context.encode()).hexdigest()[:8]
+#     cache_key = get_cache_key(topic, subject, context_hash)
+    
+#     if cache_key in MCQ_CACHE:
+#         print("✓ Using cached MCQs")
+#         return MCQ_CACHE[cache_key]
+    
+#     # OPTIMIZATION: Shortened prompt for faster generation
+#     prompt = f"""You are a Class-12 {subject.title()} teacher creating MCQs.
+# Topic: "{topic}"
+# Context:
+# {context}
+
+# Generate exactly 5 MCQs in this format:
+# Q1. [Question]
+# A) [Option]
+# B) [Option]
+# C) [Option]
+# D) [Option]
+# Correct Answer: [Letter] - [Reason]
+
+# Rules: Make correct answer from context, realistic distractors.
+# Generate 5 MCQs:"""
+    
+#     # OPTIMIZATION: Reduced max_length for faster tokenization
+#     inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1536).to(device)
+    
+#     # OPTIMIZATION: Use torch.no_grad() for inference (saves memory)
+#     with torch.no_grad():
+#         # OPTIMIZATION: Reduced max_new_tokens from 900 to 600 (sufficient for 5 MCQs)
+#         # OPTIMIZATION: Reduced temperature from 0.15 to 0.1 (faster, more deterministic)
+#         # OPTIMIZATION: Added num_beams=1 (greedy decoding, faster than sampling)
+#         outputs = model.generate(
+#             **inputs,
+#             max_new_tokens=600,  # Reduced from 900
+#             temperature=0.1,     # Reduced from 0.15
+#             top_p=0.85,          # Slightly adjusted
+#             do_sample=True,
+#             repetition_penalty=1.15,
+#             pad_token_id=tokenizer.eos_token_id  # Optimization: Explicit pad token
+#         )
+    
+#     result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+#     # Extract only the generated MCQs
+#     if "Generate 5 MCQs:" in result:
+#         result = result.split("Generate 5 MCQs:")[-1].strip()
+    
+#     # OPTIMIZATION: Cache the result
+#     cache_mcq(cache_key, result)
+    
+#     return result
+
+# def verify_and_correct_answers(mcqs_text, context):
+#     """
+#     This function is kept for future enhancements
+#     """
+#     return mcqs_text
+
+# # ------------------------------
+# # HTML UI (with improved loading message)
+# # ------------------------------
+# HTML_TEMPLATE = """
+# <!DOCTYPE html>
+# <html>
+# <head>
+#     <title>Class 12 PCB MCQ Generator</title>
+#     <style>
+#         * { margin: 0; padding: 0; box-sizing: border-box; }
+#         body {
+#             font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+#             background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+#             min-height: 100vh;
+#             padding: 20px;
+#         }
+#         .container {
+#             max-width: 900px;
+#             margin: 0 auto;
+#             background: white;
+#             border-radius: 20px;
+#             box-shadow: 0 20px 60px rgba(0,0,0,0.3);
+#             overflow: hidden;
+#         }
+#         .header {
+#             background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+#             color: white;
+#             padding: 30px;
+#             text-align: center;
+#         }
+#         .header h1 { font-size: 2.5em; margin-bottom: 10px; }
+#         .content { padding: 40px; }
+#         .form-group {
+#             margin-bottom: 25px;
+#         }
+#         label {
+#             display: block;
+#             font-weight: 600;
+#             margin-bottom: 10px;
+#             color: #333;
+#             font-size: 16px;
+#         }
+#         select, input {
+#             width: 100%;
+#             padding: 15px;
+#             border: 2px solid #e0e0e0;
+#             border-radius: 10px;
+#             font-size: 16px;
+#             font-family: inherit;
+#             transition: border-color 0.3s;
+#         }
+#         select:focus, input:focus {
+#             outline: none;
+#             border-color: #667eea;
+#         }
+#         button {
+#             width: 100%;
+#             padding: 18px;
+#             background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+#             color: white;
+#             border: none;
+#             border-radius: 10px;
+#             font-size: 18px;
+#             font-weight: 600;
+#             cursor: pointer;
+#             transition: all 0.3s;
+#         }
+#         button:hover {
+#             transform: translateY(-2px);
+#             box-shadow: 0 10px 20px rgba(102, 126, 234, 0.4);
+#         }
+#         button:disabled {
+#             background: #ccc;
+#             cursor: not-allowed;
+#             transform: none;
+#         }
+#         .result {
+#             margin-top: 30px;
+#             padding: 25px;
+#             background: #f8f9fa;
+#             border-radius: 10px;
+#             border-left: 4px solid #667eea;
+#             display: none;
+#         }
+#         .result.show { display: block; }
+#         .result h3 {
+#             color: #667eea;
+#             margin-bottom: 20px;
+#             font-size: 1.4em;
+#         }
+#         .mcq-content {
+#             background: white;
+#             padding: 25px;
+#             border-radius: 8px;
+#             white-space: pre-wrap;
+#             line-height: 1.9;
+#             font-size: 15px;
+#         }
+#         .loading {
+#             text-align: center;
+#             padding: 30px;
+#             display: none;
+#         }
+#         .loading.show { display: block; }
+#         .spinner {
+#             border: 4px solid #f3f3f3;
+#             border-top: 4px solid #667eea;
+#             border-radius: 50%;
+#             width: 50px;
+#             height: 50px;
+#             animation: spin 1s linear infinite;
+#             margin: 0 auto 15px;
+#         }
+#         @keyframes spin {
+#             0% { transform: rotate(0deg); }
+#             100% { transform: rotate(360deg); }
+#         }
+#         .subject-tag {
+#             display: inline-block;
+#             padding: 5px 15px;
+#             border-radius: 20px;
+#             font-size: 13px;
+#             font-weight: 600;
+#             margin-right: 10px;
+#         }
+#         .bio { background: #d4edda; color: #155724; }
+#         .chem { background: #d1ecf1; color: #0c5460; }
+#         .phy { background: #f8d7da; color: #721c24; }
+#         .optimization-badge {
+#             background: #28a745;
+#             color: white;
+#             padding: 5px 12px;
+#             border-radius: 15px;
+#             font-size: 12px;
+#             margin-left: 10px;
+#         }
+#     </style>
+# </head>
+# <body>
+#     <div class="container">
+#         <div class="header">
+#             <h1>🎓 Class 12 PCB MCQ Generator</h1>
+#             <p style="font-size: 1.1em; margin-bottom: 15px;">Generate practice MCQs from your textbooks <span class="optimization-badge">⚡ Optimized</span></p>
+#             <div>
+#                 <span class="subject-tag bio">Biology</span>
+#                 <span class="subject-tag chem">Chemistry</span>
+#                 <span class="subject-tag phy">Physics</span>
+#             </div>
+#         </div>
+        
+#         <div class="content">
+#             <div class="form-group">
+#                 <label for="subject">📚 Select Subject</label>
+#                 <select id="subject">
+#                     <option value="biology">Biology</option>
+#                     <option value="chemistry">Chemistry</option>
+#                     <option value="physics">Physics</option>
+#                 </select>
+#             </div>
             
-            <div class="form-group">
-                <label for="topic">✏️ Enter Topic</label>
-                <input type="text" id="topic" placeholder="e.g., Mitochondria, Chemical Bonding, Newton's Laws">
-            </div>
+#             <div class="form-group">
+#                 <label for="topic">✏️ Enter Topic</label>
+#                 <input type="text" id="topic" placeholder="e.g., Mitochondria, Chemical Bonding, Newton's Laws">
+#             </div>
             
-            <button onclick="generateMCQs()">🚀 Generate 5 MCQs</button>
+#             <button onclick="generateMCQs()">🚀 Generate 5 MCQs</button>
             
-            <div class="loading" id="loading">
-                <div class="spinner"></div>
-                <p style="color: #666; font-size: 16px;">Generating high-quality MCQs...</p>
-                <p style="color: #999; font-size: 13px; margin-top: 10px;">⚡ Using Llama 3.3 70B via Groq API (5-10 seconds)</p>
-            </div>
+#             <div class="loading" id="loading">
+#                 <div class="spinner"></div>
+#                 <p style="color: #666; font-size: 16px;">Generating MCQs... This may take 20-40 seconds</p>
+#                 <p style="color: #999; font-size: 13px; margin-top: 10px;">⚡ Optimized for faster generation</p>
+#             </div>
             
-            <div class="result" id="result">
-                <h3>📝 Generated MCQs:</h3>
-                <div style="background: #d4edda; padding: 12px; border-radius: 6px; margin-bottom: 15px; color: #155724; font-size: 14px;">
-                    ✓ <strong>High Accuracy:</strong> Generated by Llama 3.3 70B - One of the most capable models available!
-                </div>
-                <div class="mcq-content" id="mcqContent"></div>
-            </div>
-        </div>
-    </div>
-    <script>
-        async function generateMCQs() {
-            const subject = document.getElementById('subject').value;
-            const topic = document.getElementById('topic').value.trim();
+#             <div class="result" id="result">
+#                 <h3>📝 Generated MCQs:</h3>
+#                 <div style="background: #fff3cd; padding: 12px; border-radius: 6px; margin-bottom: 15px; color: #856404; font-size: 14px;">
+#                     ⚠️ <strong>Note:</strong> AI-generated answers may occasionally be incorrect. Please verify answers using your textbook.
+#                 </div>
+#                 <div class="mcq-content" id="mcqContent"></div>
+#             </div>
+#         </div>
+#     </div>
+#     <script>
+#         async function generateMCQs() {
+#             const subject = document.getElementById('subject').value;
+#             const topic = document.getElementById('topic').value.trim();
             
-            if (!topic) {
-                alert('⚠️ Please enter a topic!');
-                return;
-            }
+#             if (!topic) {
+#                 alert('��️ Please enter a topic!');
+#                 return;
+#             }
             
-            const loading = document.getElementById('loading');
-            const result = document.getElementById('result');
-            const btn = document.querySelector('button');
+#             const loading = document.getElementById('loading');
+#             const result = document.getElementById('result');
+#             const btn = document.querySelector('button');
             
-            loading.classList.add('show');
-            result.classList.remove('show');
-            btn.disabled = true;
-            btn.textContent = '⏳ Generating...';
+#             loading.classList.add('show');
+#             result.classList.remove('show');
+#             btn.disabled = true;
+#             btn.textContent = '⏳ Generating...';
             
-            try {
-                const response = await fetch('/generate', {
-                    method: 'POST',
-                    headers: {'Content-Type': 'application/json'},
-                    body: JSON.stringify({subject, topic})
-                });
+#             try {
+#                 const response = await fetch('/generate', {
+#                     method: 'POST',
+#                     headers: {'Content-Type': 'application/json'},
+#                     body: JSON.stringify({subject, topic})
+#                 });
                 
-                const data = await response.json();
+#                 const data = await response.json();
                 
-                if (data.error) {
-                    alert('❌ Error: ' + data.error);
-                    return;
-                }
+#                 if (data.error) {
+#                     alert('❌ Error: ' + data.error);
+#                     return;
+#                 }
                 
-                document.getElementById('mcqContent').textContent = data.mcqs;
-                result.classList.add('show');
-            } catch (error) {
-                alert('❌ Error: ' + error.message);
-            } finally {
-                loading.classList.remove('show');
-                btn.disabled = false;
-                btn.textContent = '🚀 Generate 5 MCQs';
-            }
-        }
+#                 document.getElementById('mcqContent').textContent = data.mcqs;
+#                 result.classList.add('show');
+#             } catch (error) {
+#                 alert('❌ Error: ' + error.message);
+#             } finally {
+#                 loading.classList.remove('show');
+#                 btn.disabled = false;
+#                 btn.textContent = '🚀 Generate 5 MCQs';
+#             }
+#         }
         
-        document.getElementById('topic').addEventListener('keypress', function(e) {
-            if (e.key === 'Enter') {
-                generateMCQs();
-            }
-        });
-    </script>
-</body>
-</html>
-"""
+#         // Allow Enter key to submit
+#         document.getElementById('topic').addEventListener('keypress', function(e) {
+#             if (e.key === 'Enter') {
+#                 generateMCQs();
+#             }
+#         });
+#     </script>
+# </body>
+# </html>
+# """
 
-# ------------------------------
-# Routes
-# ------------------------------
-@app.route("/")
-def home():
-    return render_template_string(HTML_TEMPLATE)
+# # ------------------------------
+# # Routes
+# # ------------------------------
+# @app.route("/")
+# def home():
+#     return render_template_string(HTML_TEMPLATE)
 
-@app.route("/generate", methods=["POST"])
-def generate():
-    try:
-        data = request.json
-        subject = data.get("subject", "").lower()
-        topic = data.get("topic", "")
+# @app.route("/generate", methods=["POST"])
+# def generate():
+#     try:
+#         data = request.json
+#         subject = data.get("subject", "").lower()
+#         topic = data.get("topic", "")
         
-        if not topic:
-            return jsonify({"error": "Topic is required"}), 400
+#         if not topic:
+#             return jsonify({"error": "Topic is required"}), 400
         
-        if subject not in SUBJECTS:
-            return jsonify({"error": "Invalid subject"}), 400
+#         if subject not in SUBJECTS:
+#             return jsonify({"error": "Invalid subject. Choose biology, chemistry, or physics."}), 400
         
-        print(f"\n🔍 Searching {subject} for topic: {topic}")
+#         print(f"\n🔍 Searching {subject} for topic: {topic}")
         
-        context = rag_search(topic, subject, k=5)
+#         # Retrieve context from RAG
+#         context = rag_search(topic, subject, k=5)
         
-        if not context or len(context.strip()) < 50:
-            return jsonify({"error": f"No relevant content found for topic: {topic}"}), 404
+#         if not context or len(context.strip()) < 50:
+#             return jsonify({"error": f"No relevant content found in {subject} for topic: {topic}"}), 404
         
-        print(f"✓ Found context ({len(context)} chars)")
+#         print(f"✓ Found context ({len(context)} chars)")
         
-        mcqs = generate_mcqs(context, topic, subject)
+#         # Generate MCQs (now with caching)
+#         print("🤖 Generating MCQs...")
+#         mcqs = generate_mcqs(context, topic, subject)
         
-        return jsonify({"mcqs": mcqs, "subject": subject})
+#         print("✓ MCQs generated successfully")
+        
+#         return jsonify({"mcqs": mcqs, "subject": subject})
     
-    except Exception as e:
-        print(f"❌ Error: {e}")
-        import traceback
-        traceback.print_exc()
-        return jsonify({"error": str(e)}), 500
+#     except Exception as e:
+#         print(f"❌ Error in /generate: {e}")
+#         import traceback
+#         traceback.print_exc()
+#         return jsonify({"error": str(e)}), 500
 
-@app.route("/health")
-def health():
-    return jsonify({
-        "status": "healthy",
-        "model": "llama-3.3-70b-versatile (Groq API)",
-        "cache_size": len(MCQ_CACHE)
-    })
+# @app.route("/health")
+# def health():
+#     return jsonify({
+#         "status": "healthy",
+#         "subjects": {
+#             "biology": len(SUBJECTS["biology"]["chunks"]),
+#             "chemistry": len(SUBJECTS["chemistry"]["chunks"]),
+#             "physics": len(SUBJECTS["physics"]["chunks"])
+#         },
+#         "cache_size": len(MCQ_CACHE)
+#     })
+
+# # OPTIMIZATION: Add cache stats endpoint
+# @app.route("/cache/stats")
+# def cache_stats():
+#     return jsonify({
+#         "cached_topics": len(MCQ_CACHE),
+#         "max_cache_size": MAX_CACHE_SIZE,
+#         "cache_keys": list(MCQ_CACHE.keys())
+#     })
+
+# # OPTIMIZATION: Add cache clear endpoint (optional)
+# @app.route("/cache/clear", methods=["POST"])
+# def clear_cache():
+#     MCQ_CACHE.clear()
+#     return jsonify({"status": "Cache cleared"})
+
+# # ------------------------------
+# # Run the App
+# # ------------------------------
+# if __name__ == "__main__":
+#     port = int(os.environ.get("PORT", 7860))
+#     print(f"\n🚀 Starting Flask on 0.0.0.0:{port}\n")
+#     app.run(host="0.0.0.0", port=port, debug=False)
 
-@app.route("/cache/clear", methods=["POST"])
-def clear_cache():
-    MCQ_CACHE.clear()
-    return jsonify({"status": "Cache cleared"})
 
-# ------------------------------
-# Run the App
-# ------------------------------
-if __name__ == "__main__":
-    port = int(os.environ.get("PORT", 7860))
-    print(f"\n🚀 Starting Flask on 0.0.0.0:{port}\n")
-    app.run(host="0.0.0.0", port=port, debug=False)