""" Smart MCQ Solver — Gradio Web Application Loads ELECTRA-base + LoRA fine-tuned model weights from Hugging Face Hub (SpreadSheets600/electra-lora-mcq) Scored by MAP@3 on Kaggle Science MCQ (0.8849 OOF MAP@3) """ import time import threading import numpy as np import torch import torch.nn as nn import gradio as gr from transformers import AutoTokenizer, AutoModel from peft import PeftModel try: import spaces HAS_SPACES = True except ImportError: HAS_SPACES = False # ── Model Configuration & Hub Repositories ──────────────────────────────── ADAPTER_REPO = "SpreadSheets600/electra-lora-mcq" BACKBONE = "google/electra-base-discriminator" LABELS = ["A", "B", "C", "D", "E"] MAX_LENGTH = 256 # ── Neural Architecture (mirrors notebook) ───────────────────────────────── class EncoderMCQModel(nn.Module): def __init__(self, backbone): super().__init__() self.encoder = backbone h = backbone.config.hidden_size self.dropouts = nn.ModuleList([nn.Dropout(0.2) for _ in range(5)]) self.classifier = nn.Linear(h, 1) def mean_pool(self, hidden, mask): m = mask.unsqueeze(-1).float() return (hidden * m).sum(1) / m.sum(1).clamp(min=1e-9) def forward(self, input_ids, attention_mask): b, n, L = input_ids.shape out = self.encoder( input_ids=input_ids.view(b*n, L), attention_mask=attention_mask.view(b*n, L), ) pool = self.mean_pool(out.last_hidden_state, attention_mask.view(b*n, L)) logit = torch.mean( torch.stack([self.classifier(dp(pool)) for dp in self.dropouts]), 0 ) return logit.view(b, n) # ── Lazy Thread-Safe Model Loader ────────────────────────────────────────── _STATE = {"tokenizer": None, "model": None, "ready": False, "error": None} _LOCK = threading.Lock() def _load_model(): with _LOCK: if _STATE["ready"] or _STATE["error"]: return try: tok = AutoTokenizer.from_pretrained(BACKBONE) base = AutoModel.from_pretrained(BACKBONE) wrap = EncoderMCQModel(base) model = PeftModel.from_pretrained(wrap, ADAPTER_REPO, is_trainable=False) model = model.eval() _STATE["tokenizer"] = tok _STATE["model"] = model _STATE["ready"] = True except Exception as e: _STATE["error"] = str(e) def _ensure_model_ready(): if not _STATE["ready"] and not _STATE["error"]: _load_model() if _STATE["error"]: raise RuntimeError(f"Model weight load error: {_STATE['error']}") # ── Neural Inference Routine ─────────────────────────────────────────────── def run_electra_core(prompt, options): device = "cuda" if torch.cuda.is_available() else "cpu" tok = _STATE["tokenizer"] model = _STATE["model"].to(device) ids_list, mask_list = [], [] for opt in options: enc = tok( f"{prompt} [OPTION] {opt}", max_length=MAX_LENGTH, padding="max_length", truncation=True, return_tensors="pt" ) ids_list.append(enc["input_ids"].squeeze(0)) mask_list.append(enc["attention_mask"].squeeze(0)) ids = torch.stack(ids_list).unsqueeze(0).to(device) masks = torch.stack(mask_list).unsqueeze(0).to(device) logits = model(ids, masks).squeeze(0).cpu().float().numpy() probs = torch.softmax(torch.tensor(logits), dim=0).numpy() return logits, probs if HAS_SPACES: @spaces.GPU @torch.inference_mode() def run_electra_inference(prompt, options): return run_electra_core(prompt, options) else: @torch.inference_mode() def run_electra_inference(prompt, options): return run_electra_core(prompt, options) def calculate_top3(logits): return [LABELS[i] for i in np.argsort(-logits)[:3]] def calculate_map3(correct_label, ranked_labels): for k, label in enumerate(ranked_labels[:3], 1): if label == correct_label: return 1.0 / k return 0.0 # ── Gradio Prediction Callback ───────────────────────────────────────────── def predict_mcq(prompt, opt_a, opt_b, opt_c, opt_d, opt_e, correct_key): if not prompt or not prompt.strip(): raise gr.Error("Please enter a valid question prompt.") options = [opt_a, opt_b, opt_c, opt_d, opt_e] if any(not o or not o.strip() for o in options): raise gr.Error("Please fill in all 5 option choices (A–E).") _ensure_model_ready() t0 = time.perf_counter() logits, probs = run_electra_inference(prompt.strip(), [o.strip() for o in options]) elapsed_ms = (time.perf_counter() - t0) * 1000 ranked = calculate_top3(logits) medals = ["🥇 1st", "🥈 2nd", "🥉 3rd"] top3_markdown = "   |   ".join([f"{medals[r]}: **{ranked[r]}**" for r in range(3)]) conf_dict = {f"Option {LABELS[i]}": float(probs[i]) for i in range(5)} map3_markdown = "" if correct_key and correct_key.strip().upper() in LABELS: cl = correct_key.strip().upper() map3_val = calculate_map3(cl, ranked) pos = next((r + 1 for r, l in enumerate(ranked) if l == cl), None) pos_str = f"rank **{pos}**" if pos else "outside top-3" map3_markdown = f"📊 MAP@3 Metric = **{map3_val:.4f}** (Correct Answer: **{cl}** found at {pos_str})" order = np.argsort(-logits) table_rows = [] for rank_idx, idx in enumerate(order): lbl = LABELS[idx] tick = " ✓" if correct_key and lbl == correct_key.strip().upper() else "" table_rows.append([ f"#{rank_idx + 1}", f"{lbl}{tick}", options[idx][:70] + ("…" if len(options[idx]) > 70 else ""), f"{probs[idx] * 100:.1f}%", f"{logits[idx]:.3f}" ]) device_used = "ZeroGPU" if torch.cuda.is_available() else "CPU" info_markdown = ( f"⏱ **{elapsed_ms:.0f} ms** response time · " f"Device: **{device_used}** · " f"Model: **ELECTRA-base + LoRA ($r=16$)** · " f"5-Fold OOF MAP@3: **0.8849**" ) return top3_markdown, conf_dict, table_rows, map3_markdown, info_markdown # ── Pre-loaded Test Cases ────────────────────────────────────────────────── EXAMPLES = [ ["Which of the following is a prime number?", "15", "21", "23", "25", "27", "C"], ["What is the chemical symbol for gold?", "Ag", "Au", "Fe", "Pb", "Hg", "B"], ["The process by which plants make food using sunlight is called:", "Respiration", "Fermentation", "Photosynthesis", "Transpiration", "Osmosis", "C"], ["Which planet in our solar system has the most moons?", "Jupiter", "Saturn", "Uranus", "Neptune", "Mars", "B"], ["DNA replication occurs during which phase of the cell cycle?", "G1 phase", "S phase", "G2 phase", "M phase", "G0 phase", "B"], ["What is the powerhouse of the cell?", "Nucleus", "Ribosome", "Mitochondria", "Golgi apparatus", "Endoplasmic reticulum", "C"] ] # ── Gradio Blocks Interface ──────────────────────────────────────────────── with gr.Blocks(title="Smart MCQ Solver") as demo: gr.HTML("""

🎯 Smart MCQ Solver

ELECTRA-base + LoRA Neural Option Scorer  ·  MAP@3 = 0.8849 on 5-Fold CV

""") with gr.Row(): with gr.Column(scale=5): gr.Markdown("### 📝 Question Input") prompt_input = gr.Textbox( label="Question Prompt", placeholder="Type your prompt here...", lines=3 ) with gr.Row(): opt_a = gr.Textbox(label="Option A") opt_b = gr.Textbox(label="Option B") with gr.Row(): opt_c = gr.Textbox(label="Option C") opt_d = gr.Textbox(label="Option D") opt_e = gr.Textbox(label="Option E") correct_input = gr.Textbox( label="✅ Correct Answer Key (Optional - A/B/C/D/E for MAP@3 Evaluation)", placeholder="Leave blank if unknown", max_lines=1 ) with gr.Row(): predict_button = gr.Button("🔍 Predict Top-3 & Confidence Scores", variant="primary", size="lg") clear_button = gr.ClearButton( [prompt_input, opt_a, opt_b, opt_c, opt_d, opt_e, correct_input], value="🗑 Clear", size="lg" ) with gr.Column(scale=5): gr.Markdown("### 🏆 Model Predictions & Analytics") top3_output = gr.Markdown(value="*Submit a question to see predictions.*") map3_output = gr.Markdown() gr.Markdown("#### Option Confidence Distribution") conf_output = gr.Label(label="Softmax Probability", num_top_classes=5) gr.Markdown("#### Full Option Ranking Details") table_output = gr.Dataframe( headers=["Rank", "Option", "Text", "Confidence %", "Logit"], interactive=False, wrap=True ) info_output = gr.Markdown() gr.Markdown("---") gr.Examples( examples=EXAMPLES, inputs=[prompt_input, opt_a, opt_b, opt_c, opt_d, opt_e, correct_input], outputs=[top3_output, conf_output, table_output, map3_output, info_output], fn=predict_mcq, cache_examples=False, label="📚 Load Example Test Case", examples_per_page=6 ) with gr.Accordion("ℹ️ Model Architecture & Evaluation Specs", open=False): gr.Markdown(""" | Component | Specification | |---|---| | **Backbone Model** | `google/electra-base-discriminator` | | **LoRA Adapter** | `SpreadSheets600/electra-lora-mcq` ($r=16, \\alpha=32$) | | **Trainable Parameters** | ~1.18M (~1% of total weights) | | **Scoring Head** | Joint 5-way scoring via 5× Dropout(0.2) + Linear layer | | **5-Fold CV MAP@3** | **0.8849** | | **5-Fold CV Accuracy** | **81.1%** | | **Course & Student** | BITS Pilani WILP (DL & GenAI) · Student 24F2008474 | """) predict_button.click( fn=predict_mcq, inputs=[prompt_input, opt_a, opt_b, opt_c, opt_d, opt_e, correct_input], outputs=[top3_output, conf_output, table_output, map3_output, info_output] ) try: demo.launch(ssr_mode=False) except TypeError: demo.launch()