| import streamlit as st |
| import torch |
| import torch.nn as nn |
| import json |
| import pandas as pd |
| import networkx as nx |
| import numpy as np |
| from huggingface_hub import hf_hub_download |
| from typing import Dict, List, Optional, Tuple |
|
|
| st.set_page_config(page_title='Logic Engine', page_icon='🧠', layout='wide') |
|
|
| HF_REPO = 'Clementio/PLRS' |
|
|
| @st.cache_resource |
| def load_model(): |
| config_path = hf_hub_download(repo_id=HF_REPO, filename='config.json') |
| with open(config_path) as f: |
| config = json.load(f) |
| model_path = hf_hub_download(repo_id=HF_REPO, filename='sakt_model.pt') |
| class SAKT(nn.Module): |
| def __init__(self, num_skills, embed_dim, num_heads, num_layers, max_seq_len, dropout): |
| super(SAKT, self).__init__() |
| self.num_skills = num_skills |
| self.interaction_embed = nn.Embedding(num_skills * 2 + 1, embed_dim, padding_idx=0) |
| self.skill_embed = nn.Embedding(num_skills + 1, embed_dim, padding_idx=0) |
| self.pos_embed = nn.Embedding(max_seq_len + 1, embed_dim) |
| encoder_layer = nn.TransformerEncoderLayer(d_model=embed_dim, nhead=num_heads, dropout=dropout, batch_first=True, dim_feedforward=embed_dim * 4, norm_first=True) |
| self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers, enable_nested_tensor=False) |
| self.dropout = nn.Dropout(dropout) |
| self.output = nn.Linear(embed_dim, 1) |
| def forward(self, interactions, target_skills, mask, return_attention=False): |
| batch_size, seq_len = interactions.shape |
| positions = torch.arange(seq_len, device=interactions.device).unsqueeze(0).expand(batch_size, -1) |
| x = self.interaction_embed(interactions) |
| x = x + self.pos_embed(positions) |
| x = x * mask.unsqueeze(-1).float() |
| x = self.dropout(x) |
| causal_mask = torch.triu(torch.full((seq_len, seq_len), float('-inf')), diagonal=1) |
| x = self.transformer(x, mask=causal_mask, is_causal=False) |
| x = x * mask.unsqueeze(-1).float() |
| x = x + self.skill_embed(target_skills) |
| return self.output(x).squeeze(-1) |
| device = torch.device('cpu') |
| model = SAKT(num_skills=config['num_skills'], embed_dim=config['embed_dim'], num_heads=config['num_heads'], num_layers=config['num_layers'], max_seq_len=config['max_seq_len'], dropout=config['dropout']) |
| model.load_state_dict(torch.load(model_path, map_location=device)) |
| model.eval() |
| return model, config, device |
|
|
| @st.cache_resource |
| def load_knowledge_maps(): |
| def load_dag(path): |
| with open(path) as f: |
| data = json.load(f) |
| G = nx.DiGraph() |
| for node in data['nodes']: |
| G.add_node(node['id'], label=node['label'], level=node['level'], term=node['term']) |
| for edge in data['edges']: |
| G.add_edge(edge['from'], edge['to']) |
| return G |
| return load_dag('knowledge_maps/math_dag.json'), load_dag('knowledge_maps/cs_dag.json') |
|
|
| @st.cache_data |
| def load_skill_encoder(): |
| return pd.read_csv('data/skill_encoder.csv') |
|
|
| class MasteryVector: |
| def __init__(self, graph, threshold=0.70): |
| self.graph = graph |
| self.threshold = threshold |
| self.mastery = {node: 0.0 for node in graph.nodes} |
| def update(self, topic_id, probability): |
| if topic_id in self.mastery: self.mastery[topic_id] = probability |
| def is_mastered(self, topic_id): |
| return self.mastery.get(topic_id, 0.0) >= self.threshold |
| def get_mastery(self, topic_id): |
| return self.mastery.get(topic_id, 0.0) |
| def get_mastery_summary(self): |
| mastered = [t for t in self.mastery if self.is_mastered(t)] |
| return {'total_topics': len(self.mastery), 'mastered': len(mastered), 'mastery_rate': round(len(mastered)/len(self.mastery), 3), 'mastered_topics': mastered} |
|
|
| class DAGConstraintLayer: |
| def __init__(self, graph, threshold=0.70, soft_threshold=0.50): |
| self.graph = graph |
| self.threshold = threshold |
| self.soft_threshold = soft_threshold |
| def validate(self, topic_id, mastery_vector): |
| if topic_id not in self.graph.nodes: return 'vetoed', 'Topic not found.' |
| prerequisites = list(self.graph.predecessors(topic_id)) |
| label = self.graph.nodes[topic_id].get('label', topic_id) |
| if not prerequisites: return 'approved', f'✅ Foundational topic — no prerequisites.' |
| hard_fails = [] |
| soft_fails = [] |
| for p in prerequisites: |
| m = mastery_vector.get_mastery(p) |
| plabel = self.graph.nodes[p].get('label', p) |
| if m < self.soft_threshold: |
| hard_fails.append((plabel, m)) |
| elif m < self.threshold: |
| soft_fails.append((plabel, m)) |
| if hard_fails: |
| gaps = ', '.join([f"{l} ({m:.0%} mastered, need {self.threshold:.0%})" for l,m in hard_fails]) |
| return 'vetoed', f'❌ Prerequisites not met: {gaps}' |
| elif soft_fails: |
| gaps = ', '.join([f"{l} ({m:.0%} mastered, need {self.threshold:.0%})" for l,m in soft_fails]) |
| return 'challenging', f'⚠️ Challenging — prerequisites nearly met: {gaps}. Proceed with caution.' |
| else: |
| prereq_labels = [self.graph.nodes[p].get('label',p) for p in prerequisites] |
| return 'approved', f'✅ Prerequisites mastered: {", ".join(prereq_labels)}' |
|
|
| class RankingFunction: |
| def __init__(self, graph, threshold=0.70, w_gap=0.40, w_ready=0.35, w_downstream=0.25): |
| self.graph=graph; self.threshold=threshold; self.w_gap=w_gap; self.w_ready=w_ready; self.w_downstream=w_downstream |
| scores = {n: len(nx.descendants(graph, n)) for n in graph.nodes} |
| mx = max(scores.values()) if scores else 1 |
| self._downstream = {n: s/mx for n,s in scores.items()} |
| def score(self, topic_id, mastery_vector): |
| current = mastery_vector.get_mastery(topic_id) |
| gap = min(max(0.0, self.threshold-current)/self.threshold, 1.0) |
| prereqs = list(self.graph.predecessors(topic_id)) |
| readiness = 1.0 if not prereqs else sum(1 for p in prereqs if mastery_vector.is_mastered(p))/len(prereqs) |
| downstream = self._downstream.get(topic_id, 0.0) |
| return round(self.w_gap*gap + self.w_ready*readiness + self.w_downstream*downstream, 3) |
|
|
| class LearningRecommendationPipeline: |
| def __init__(self, graph, threshold=0.70, soft_threshold=0.50, top_n=5): |
| self.graph=graph |
| self.constraint=DAGConstraintLayer(graph, threshold, soft_threshold) |
| self.ranker=RankingFunction(graph, threshold) |
| self.top_n=top_n |
| def run(self, mastery_vector): |
| approved, challenging, vetoed = [], [], [] |
| for topic_id in self.graph.nodes: |
| status, reasoning = self.constraint.validate(topic_id, mastery_vector) |
| entry = {'topic_id': topic_id, 'topic_label': self.graph.nodes[topic_id].get('label', topic_id), 'mastery': round(mastery_vector.get_mastery(topic_id),3), 'reasoning': reasoning, 'status': status} |
| if status == 'approved' and not mastery_vector.is_mastered(topic_id): |
| entry['score'] = self.ranker.score(topic_id, mastery_vector) |
| approved.append(entry) |
| elif status == 'challenging' and not mastery_vector.is_mastered(topic_id): |
| entry['score'] = self.ranker.score(topic_id, mastery_vector) * 0.8 |
| challenging.append(entry) |
| elif status == 'vetoed': |
| vetoed.append(entry) |
| approved.sort(key=lambda x: x['score'], reverse=True) |
| challenging.sort(key=lambda x: x['score'], reverse=True) |
| return {'top_recommendations': approved[:self.top_n], 'challenging': challenging[:3], 'total_approved': len(approved), 'total_challenging': len(challenging), 'total_vetoed': len(vetoed), 'vetoed_sample': vetoed[:5], 'prerequisite_violation_rate': round(len(vetoed)/max(len(list(self.graph.nodes)),1),3)} |
|
|
| ACTIVITY_TO_MATH = {'oucontent':'algebraic_expressions','forumng':'statistics_basic','homepage':'whole_numbers','subpage':'plane_shapes','resource':'indices','url':'number_bases','ouwiki':'proportion_variation','glossary':'algebraic_factorization','quiz':'quadratic_equations'} |
| ACTIVITY_TO_CS = {'oucontent':'programming_concepts','forumng':'ethics_technology','homepage':'computer_basics','subpage':'html_basics','resource':'networking_fundamentals','url':'internet_basics','ouwiki':'cloud_basics','glossary':'intro_databases','quiz':'python_basics'} |
|
|
| def run_sakt_inference(model, config, skill_seq, correct_seq, device): |
| max_len=config['max_seq_len']; n_skills=config['num_skills'] |
| if len(skill_seq)>max_len: skill_seq=skill_seq[-max_len:]; correct_seq=correct_seq[-max_len:] |
| interactions=[s+c*n_skills for s,c in zip(skill_seq[:-1],correct_seq[:-1])] |
| target_skills=skill_seq[1:] |
| seq_len=len(interactions); pad_len=max_len-seq_len |
| interactions=[0]*pad_len+interactions; target_skills=[0]*pad_len+target_skills; mask=[False]*pad_len+[True]*seq_len |
| with torch.no_grad(): |
| logits=model(torch.LongTensor([interactions]).to(device),torch.LongTensor([target_skills]).to(device),torch.BoolTensor([mask]).to(device)) |
| probs=torch.sigmoid(logits).squeeze(0) |
| mastery={}; real_probs=probs[torch.BoolTensor(mask)].cpu().numpy(); real_skills=target_skills[pad_len:] |
| for skill_id,prob in zip(real_skills,real_probs): mastery[int(skill_id)]=float(prob) |
| return mastery |
|
|
| def build_mastery_vector(skill_probs, graph, skill_encoder_df, domain, threshold, soft_threshold): |
| mv=MasteryVector(graph, threshold); mapping=ACTIVITY_TO_MATH if domain=='math' else ACTIVITY_TO_CS |
| topic_scores={} |
| for skill_id,prob in skill_probs.items(): |
| row=skill_encoder_df[skill_encoder_df['skill_id']==skill_id] |
| if row.empty: continue |
| act=row['activity_type'].values[0] if 'activity_type' in row.columns else None |
| topic_id=mapping.get(act) if act else None |
| if topic_id: topic_scores[topic_id]=max(topic_scores.get(topic_id,0.0),prob) |
| for topic_id,score in topic_scores.items(): mv.update(topic_id,score) |
| return mv |
|
|
| def what_if_analysis(topic_id, graph): |
| unlocks = list(nx.descendants(graph, topic_id)) |
| direct_unlocks = list(graph.successors(topic_id)) |
| blocked_by = list(graph.predecessors(topic_id)) |
| unlock_labels = [graph.nodes[n].get('label',n) for n in direct_unlocks] |
| all_unlock_labels = [graph.nodes[n].get('label',n) for n in unlocks] |
| blocked_labels = [graph.nodes[n].get('label',n) for n in blocked_by] |
| return {'direct_unlocks': unlock_labels, 'all_unlocks': all_unlock_labels, 'blocked_by': blocked_labels, 'total_unlocked': len(unlocks)} |
|
|
| def get_attention_weights(model, config, skill_seq, correct_seq, device): |
| max_len=config['max_seq_len']; n_skills=config['num_skills'] |
| if len(skill_seq)>max_len: skill_seq=skill_seq[-max_len:]; correct_seq=correct_seq[-max_len:] |
| interactions=[s+c*n_skills for s,c in zip(skill_seq[:-1],correct_seq[:-1])] |
| target_skills=skill_seq[1:] |
| seq_len=len(interactions); pad_len=max_len-seq_len |
| interactions=[0]*pad_len+interactions; target_skills=[0]*pad_len+target_skills; mask_list=[False]*pad_len+[True]*seq_len |
| interactions_t=torch.LongTensor([interactions]); target_t=torch.LongTensor([target_skills]); mask_t=torch.BoolTensor([mask_list]) |
| attention_weights = [] |
| def hook_fn(module, input, output): |
| if hasattr(module, 'self_attn'): |
| pass |
| with torch.no_grad(): |
| positions=torch.arange(max_len).unsqueeze(0) |
| x=model.interaction_embed(interactions_t)+model.pos_embed(positions) |
| x=x*mask_t.unsqueeze(-1).float() |
| real_mask=mask_t.squeeze(0) |
| real_skills=target_skills[pad_len:] |
| real_probs=torch.sigmoid(model(interactions_t,target_t,mask_t)).squeeze(0)[real_mask].numpy() |
| return real_skills[-10:], real_probs[-10:], seq_len |
|
|
| def main(): |
| model, config, device = load_model() |
| math_graph, cs_graph = load_knowledge_maps() |
| skill_encoder = load_skill_encoder() |
| st.title('🧠 Logic Engine') |
| st.subheader('Domain-Agnostic Constraint-Aware Learning Recommender') |
| st.markdown('---') |
| st.sidebar.title('⚙️ Configuration') |
| domain = st.sidebar.selectbox('Select Domain', ['Mathematics', 'CS Fundamentals']) |
| threshold = st.sidebar.slider('Mastery Threshold', 0.50, 0.90, 0.70, 0.05, help='Minimum mastery to consider a topic fully mastered') |
| soft_threshold = st.sidebar.slider('Challenging Threshold', 0.30, 0.70, 0.50, 0.05, help='Topics above this but below mastery threshold are marked Challenging') |
| top_n = st.sidebar.slider('Top N Recommendations', 3, 10, 5) |
| graph = math_graph if domain=='Mathematics' else cs_graph |
| domain_key = 'math' if domain=='Mathematics' else 'cs' |
| pipeline = LearningRecommendationPipeline(graph, threshold, soft_threshold, top_n) |
| st.sidebar.markdown('---') |
| st.sidebar.markdown('**About**') |
| st.sidebar.markdown('SAKT-based knowledge tracing with DAG prerequisite constraints. Three-tier recommendations: ✅ Approved, ⚠️ Challenging, ❌ Vetoed.') |
| tab1, tab2, tab3, tab4 = st.tabs(['🎯 Recommendations','🔍 What-If Simulator','🗺️ Knowledge Map','📊 Diagnostics']) |
|
|
| with tab1: |
| st.header('Learner Profile') |
| mode = st.radio('Input Mode', ['Manual Mastery Input','Simulate Student Sequence'], horizontal=True) |
| mastery_vector = MasteryVector(graph, threshold) |
| if mode=='Manual Mastery Input': |
| st.markdown('Set your current mastery level for each topic:') |
| cols=st.columns(2); nodes=list(graph.nodes) |
| for i,node in enumerate(nodes): |
| label=graph.nodes[node].get('label',node); level=graph.nodes[node].get('level','') |
| val=cols[i%2].slider(f'{label} ({level})',0.0,1.0,0.0,0.05,key=f'mastery_{node}') |
| mastery_vector.update(node,val) |
| else: |
| seq_length=st.slider('Sequence Length',10,200,50) |
| seed=st.number_input('Student Seed',1,1000,42,1) |
| np.random.seed(int(seed)) |
| sim_skills=np.random.randint(0,config['num_skills'],seq_length).tolist() |
| sim_corrects=np.random.randint(0,2,seq_length).tolist() |
| skill_probs=run_sakt_inference(model,config,sim_skills,sim_corrects,device) |
| mastery_vector=build_mastery_vector(skill_probs,graph,skill_encoder,domain_key,threshold,soft_threshold) |
| st.success(f'SAKT inference complete — {len(skill_probs)} skill predictions generated') |
| real_skills, real_probs, seq_len = get_attention_weights(model,config,sim_skills,sim_corrects,device) |
| if len(real_probs) > 0: |
| st.markdown('**📈 SAKT Mastery Signal (last 10 interactions):**') |
| attn_df = pd.DataFrame({'Interaction Step': [f'Step {seq_len-len(real_probs)+i+1}' for i in range(len(real_probs))], 'Predicted Mastery': [round(float(p),3) for p in real_probs]}) |
| st.bar_chart(attn_df.set_index('Interaction Step')) |
| if st.button('🚀 Generate Recommendations', type='primary'): |
| output=pipeline.run(mastery_vector) |
| summary=mastery_vector.get_mastery_summary() |
| col1,col2,col3,col4,col5=st.columns(5) |
| col1.metric('Topics Mastered',f"{summary['mastered']} / {summary['total_topics']}") |
| col2.metric('Mastery Rate',f"{summary['mastery_rate']:.1%}") |
| col3.metric('✅ Approved',output['total_approved']) |
| col4.metric('⚠️ Challenging',output['total_challenging']) |
| col5.metric('Violation Rate',f"{output['prerequisite_violation_rate']:.1%}") |
| st.markdown('---') |
| st.subheader(f'✅ Top {top_n} Approved Recommendations') |
| if not output['top_recommendations']: st.warning('No approved recommendations — adjust mastery or lower threshold.') |
| else: |
| for i,rec in enumerate(output['top_recommendations'],1): |
| with st.expander(f"{i}. {rec['topic_label']} — Score: {rec['score']} | Mastery: {rec['mastery']:.1%}", expanded=(i<=3)): |
| st.markdown(f"**Reasoning:** {rec['reasoning']}") |
| st.progress(rec['mastery']) |
| if output['challenging']: |
| st.markdown('---') |
| st.subheader('⚠️ Challenging Topics (proceed with caution)') |
| for rec in output['challenging']: |
| with st.expander(f"{rec['topic_label']} | Mastery: {rec['mastery']:.1%}"): |
| st.markdown(f"**Reasoning:** {rec['reasoning']}") |
| st.progress(rec['mastery']) |
| if output['vetoed_sample']: |
| st.markdown('---'); st.subheader('❌ Sample Vetoed Topics') |
| for rec in output['vetoed_sample']: |
| with st.expander(f"✗ {rec['topic_label']}"): |
| st.markdown(f"**Reason:** {rec['reasoning']}") |
|
|
| with tab2: |
| st.header('🔍 What-If Prerequisite Simulator') |
| st.markdown('Explore how mastering a topic unlocks future learning paths — or what is blocking you from starting it.') |
| nodes_list = list(graph.nodes) |
| labels_list = [graph.nodes[n].get('label',n) for n in nodes_list] |
| selected_label = st.selectbox('Select a topic to analyse:', labels_list) |
| selected_node = nodes_list[labels_list.index(selected_label)] |
| if st.button('🔍 Analyse Topic', type='primary'): |
| result = what_if_analysis(selected_node, graph) |
| col1, col2 = st.columns(2) |
| with col1: |
| st.subheader('🔓 If you master this topic...') |
| if result['direct_unlocks']: |
| st.markdown(f"**Directly unlocks {len(result['direct_unlocks'])} topic(s):**") |
| for t in result['direct_unlocks']: st.markdown(f' → {t}') |
| else: |
| st.info('This is a terminal topic — it does not unlock further topics in this map.') |
| if result['all_unlocks']: |
| st.markdown(f"**Total topics eventually unlocked: {result['total_unlocked']}**") |
| with col2: |
| st.subheader('🔒 To start this topic you need...') |
| if result['blocked_by']: |
| st.markdown('**Prerequisites required:**') |
| for t in result['blocked_by']: st.markdown(f' ✓ {t}') |
| else: |
| st.success('This is a foundational topic — no prerequisites needed. You can start it now!') |
| if result['all_unlocks']: |
| st.markdown('---') |
| st.markdown('**Full learning path unlocked:**') |
| st.markdown(' → '.join([selected_label] + result['all_unlocks'][:8]) + ('...' if len(result['all_unlocks'])>8 else '')) |
|
|
| with tab3: |
| st.header(f'{domain} Knowledge Map') |
| st.markdown(f"**{graph.number_of_nodes()} topics** | **{graph.number_of_edges()} prerequisite relationships**") |
| rows=[] |
| for node in graph.nodes: |
| label=graph.nodes[node].get('label',node); level=graph.nodes[node].get('level',''); term=graph.nodes[node].get('term','') |
| prereqs=[graph.nodes[p].get('label',p) for p in graph.predecessors(node)] |
| rows.append({'Topic':label,'Level':level,'Term':term,'Prerequisites':', '.join(prereqs) if prereqs else 'None (Foundational)'}) |
| st.dataframe(pd.DataFrame(rows), use_container_width=True, hide_index=True) |
| longest=nx.dag_longest_path(graph) |
| st.markdown('**Longest prerequisite chain:**') |
| st.markdown(' → '.join([graph.nodes[n].get('label',n) for n in longest])) |
|
|
| with tab4: |
| st.header('System Diagnostics') |
| col1,col2=st.columns(2) |
| with col1: st.subheader('Model Configuration'); st.json(config) |
| with col2: |
| st.subheader('DAG Statistics') |
| st.json({'domain':domain,'nodes':graph.number_of_nodes(),'edges':graph.number_of_edges(),'is_valid_dag':nx.is_directed_acyclic_graph(graph),'longest_path':len(nx.dag_longest_path(graph))}) |
| st.subheader('Constraint Layer') |
| st.markdown(f'**Mastery threshold:** {threshold:.0%} — topics above this are considered mastered') |
| st.markdown(f'**Challenging threshold:** {soft_threshold:.0%} — topics between this and mastery threshold are marked ⚠️ Challenging') |
| st.markdown('**Hard veto:** topics with prerequisites below challenging threshold are fully blocked') |
| st.subheader('Domain Switching') |
| dcol1,dcol2=st.columns(2) |
| with dcol1: st.metric('Math DAG',f'{math_graph.number_of_nodes()} topics') |
| with dcol2: st.metric('CS DAG',f'{cs_graph.number_of_nodes()} topics') |
|
|
| if __name__ == '__main__': |
| main() |
