import os, json, re import gradio as gr import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from evaluator import KANEvaluator from teacher import GeminiTeacher, EXERCISES, CATEGORY_LABELS from profiler import LearnerProfiler from reporter import ReporterAgent BASE_DIR = os.path.dirname(os.path.abspath(__file__)) MODEL_PATH = os.path.join(BASE_DIR, 'tinykan_cosine.pth') GEMINI_KEY = os.environ.get('GEMINI_API_KEY', '') PROFILE_PATH = os.path.join(BASE_DIR, 'profiles.json') print("Initializing agents...") evaluator = KANEvaluator(MODEL_PATH) teacher = GeminiTeacher(GEMINI_KEY) if GEMINI_KEY else None profiler = LearnerProfiler(PROFILE_PATH) reporter = ReporterAgent() print("All agents initialized") # NOTE: session-specific data (current_exercise, last_given) is now stored in # a per-browser-session gr.State component instead of module-level globals, # to prevent concurrent users from overwriting each other's session data. # --------------------------------------------------------------------- # Visual identity -- refined, professional SaaS-style palette # --------------------------------------------------------------------- INK = "#1A2332" SUBINK = "#64748B" RULE = "#E2E5EA" PAPER = "#F7F8FA" CARD = "#FFFFFF" ACCENT = "#4F46E5" DATA = "#0F766E" LIGHT = "#C7D2FE" theme = gr.themes.Base( primary_hue=gr.themes.colors.indigo, neutral_hue=gr.themes.colors.slate, font=[gr.themes.GoogleFont("Inter"), "system-ui", "sans-serif"], font_mono=[gr.themes.GoogleFont("JetBrains Mono"), "monospace"], ) CSS = f""" @import url('https://fonts.googleapis.com/css2?family=Newsreader:ital,wght@0,400;0,500;0,600;1,400&family=Inter:wght@400;500;600&family=JetBrains+Mono:wght@400;500&display=swap'); .gradio-container {{ background: {PAPER} !important; max-width: 1180px !important; font-family: 'Inter', sans-serif; }} #masthead {{ border-bottom: 1px solid {RULE}; padding-bottom: 18px; margin-bottom: 20px; }} #masthead .kicker {{ font-size: 11px; letter-spacing: 1.5px; text-transform: uppercase; color: {SUBINK}; font-weight: 500; }} #masthead h1 {{ font-family: 'Newsreader', serif; font-size: 26px; font-weight: 600; color: {INK}; margin: 4px 0 0 0; }} /* --- Signature element: agent pipeline status strip --- */ #agent-strip {{ display: flex; align-items: center; gap: 0; margin: 14px 0 0 0; background: {CARD}; border: 1px solid {RULE}; border-radius: 10px; padding: 10px 16px; }} .agent-node {{ display: flex; align-items: center; gap: 8px; font-size: 12px; font-weight: 500; color: {SUBINK}; padding: 4px 10px; border-radius: 20px; transition: all 0.2s ease; }} .agent-node .dot {{ width: 7px; height: 7px; border-radius: 50%; background: {RULE}; flex-shrink: 0; }} .agent-node.active {{ background: {LIGHT}; color: {ACCENT}; }} .agent-node.active .dot {{ background: {ACCENT}; }} .agent-arrow {{ color: {RULE}; font-size: 13px; margin: 0 2px; }} .section-label {{ font-size: 11px; letter-spacing: 1.2px; text-transform: uppercase; font-weight: 600; color: {ACCENT}; border-bottom: 1px solid {RULE}; padding-bottom: 6px; margin-bottom: 12px; }} .sheet {{ background: {CARD}; border: 1px solid {RULE}; border-radius: 12px; padding: 20px 22px; box-shadow: 0 1px 2px rgba(26,35,50,0.04), 0 4px 12px rgba(26,35,50,0.03); }} .note {{ font-size: 12px; color: {SUBINK}; font-style: italic; }} /* Scores/data in monospace for a "measured" feel */ .score-value, .data-figure {{ font-family: 'JetBrains Mono', monospace; font-weight: 500; }} [role="option"], [role="option"] *, ul[role="listbox"] li, ul[role="listbox"] li *, .wrap-inner, .wrap-inner *, .icon-wrap, .icon-wrap *, .dropdown-arrow, .dropdown-arrow *, svg.dropdown-arrow, [data-testid="dropdown"], [data-testid="dropdown"] svg, [data-testid="dropdown"] .icon-wrap, .options, .options *, .options li, .options .item, [data-testid="dropdown"] ul li, [data-testid="dropdown"] ul li *, ul.options li, ul.options li *, div.options li, div.options div, .gr-dropdown ul li, .gr-dropdown ul li *, select, select option {{ cursor: pointer !important; }} """ TYPE_CHOICES = [(label, key) for key, label in CATEGORY_LABELS.items()] + \ [('Automatic (Planner-selected)', 'auto')] def agent_strip_html(active=None): """Renders the 4-agent pipeline status strip, highlighting whichever agent(s) just acted, so the multi-agent flow is visible at a glance.""" active = active or [] nodes = [ ('evaluator', 'Evaluator'), ('teacher', 'Teacher'), ('planner', 'Planner'), ('reporter', 'Reporter'), ] parts = [] for i, (key, label) in enumerate(nodes): cls = "agent-node active" if key in active else "agent-node" parts.append(f'
{label}
') if i < len(nodes) - 1: parts.append('→') return f'
{"".join(parts)}
' def plot_xai(result): plt.rcParams['font.family'] = 'serif' fig, axes = plt.subplots(1, 3, figsize=(15, 4.6)) fig.patch.set_facecolor('#ffffff') dark, light, mid = '#0F766E', '#C7D2FE', '#4F46E5' feat_names = ['cos_MiniLM','cos_MPNet','cos_RoBERTa', '|M-MP|','|M-R|','|MP-R|','mean','max','min'] ax1 = axes[0] feat_colors = [dark if i < 3 else mid if i < 6 else light for i in range(9)] bars = ax1.barh(feat_names, result.importance*100, color=feat_colors, edgecolor='white', linewidth=0.6, height=0.6) ax1.set_xlabel('Importance (%)', fontsize=9.5, color=dark) ax1.set_title('Feature attribution', fontsize=11, fontweight='bold', color=dark, pad=8) ax1.tick_params(labelsize=8, colors=dark) for s in ['top','right']: ax1.spines[s].set_visible(False) for s in ['left','bottom']: ax1.spines[s].set_color('#E2E5EA') for bar, val in zip(bars, result.importance): ax1.text(bar.get_width()+0.3, bar.get_y()+bar.get_height()/2, f'{val*100:.1f}%', va='center', fontsize=7.2, color=dark) ax1.set_facecolor('#ffffff') ax2 = axes[1] wedges, _, autotexts = ax2.pie( [result.gate_kan*100, result.gate_linear*100], labels=['KAN branch', 'Linear branch'], colors=[dark, light], autopct='%1.0f%%', startangle=90, textprops={'fontsize':9,'color':dark}, wedgeprops={'edgecolor':'white','linewidth':2}) for at in autotexts: at.set_fontsize(9.5); at.set_color('white'); at.set_fontweight('bold') ax2.set_title('Gate distribution', fontsize=11, fontweight='bold', color=dark, pad=8) ax3 = axes[2] levels = ['A1','A2','B1','B2','C1','C2'] thresholds = [0, 0.40, 0.55, 0.70, 0.80, 0.90, 1.0] shades = ['#EEF2FF','#E0E7FF','#C7D2FE','#A5B4FC','#4F46E5','#0F766E'] for i, level in enumerate(levels): ax3.barh(0, thresholds[i+1]-thresholds[i], left=thresholds[i], height=0.32, color=shades[i], edgecolor='white') tc = dark if i < 3 else 'white' ax3.text((thresholds[i]+thresholds[i+1])/2, 0, level, ha='center', va='center', fontsize=9, fontweight='bold', color=tc) ax3.axvline(x=result.score, color='#B91C1C', linewidth=2.2, zorder=5) ax3.plot(result.score, 0, 'v', color='#B91C1C', markersize=10, zorder=6) ax3.text(result.score, 0.24, f'{result.score_pct:.1f}%', ha='center', fontsize=9.5, fontweight='bold', color='#B91C1C', bbox=dict(boxstyle='round,pad=0.25', facecolor='white', edgecolor='#B91C1C', linewidth=1.2)) ax3.set_xlim(0,1); ax3.set_ylim(-0.5,0.55) ax3.set_title('Response-quality position', fontsize=11, fontweight='bold', color=dark, pad=8) ax3.set_yticks([]); ax3.set_facecolor('#ffffff') for s in ['left','top','right']: ax3.spines[s].set_visible(False) ax3.spines['bottom'].set_color('#E2E5EA') ax3.tick_params(colors=dark, labelsize=8) plt.tight_layout() path = '/tmp/xai_plot.png' plt.savefig(path, dpi=170, bbox_inches='tight', facecolor='white') plt.close() return path def plot_history(score_history): if len(score_history) < 2: return None fig, ax = plt.subplots(figsize=(9, 3.2)) fig.patch.set_facecolor('#ffffff') x = list(range(1, len(score_history)+1)) y = [s*100 for s in score_history] ax.plot(x, y, color='#4F46E5', linewidth=2, marker='o', markersize=4, markerfacecolor='#C7D2FE', markeredgecolor='#4F46E5') ax.fill_between(x, y, color='#C7D2FE', alpha=0.2) ax.set_ylim(0, 100) ax.set_xlabel('Session', fontsize=9.5, color='#1A2332') ax.set_ylabel('Score (%)', fontsize=9.5, color='#1A2332') ax.set_title('Score progression', fontsize=11, fontweight='bold', color='#1A2332', pad=8) ax.tick_params(labelsize=8, colors='#1A2332') for s in ['top','right']: ax.spines[s].set_visible(False) for s in ['left','bottom']: ax.spines[s].set_color('#E2E5EA') ax.set_facecolor('#ffffff') plt.tight_layout() path = '/tmp/history_plot.png' plt.savefig(path, dpi=160, bbox_inches='tight', facecolor='white') plt.close() return path def planner_panel(name): p = profiler.get(name) status = profiler.planner_status(p) return f"**Planner — {p.current_cefr}** \n{status['message']}" def start_session(name, cefr, ex_type, session_state): session_state = dict(session_state) if session_state else {} last_given = session_state.get('last_given', {}) if not name.strip(): return "Enter your name to begin. / Entrez votre nom pour commencer.", "", None, "", agent_strip_html([]), session_state name = name.strip() profile = profiler.get(name, cefr) effective_cefr = cefr if profile.total_exercises == 0 else ( profile.current_cefr if profile.total_exercises >= profiler.MIN_SESSIONS else cefr) if ex_type == 'auto': available = [e['type'] for e in EXERCISES.get(effective_cefr, EXERCISES['B1'])] chosen_type = profiler.weak_exercise_type(name, list(set(available))) else: chosen_type = ex_type key = (name, effective_cefr, chosen_type) exercise = teacher.get_exercise(effective_cefr, chosen_type, exclude_instruction=last_given.get(key)) if teacher else None if not exercise: from teacher import Exercise exercise = Exercise('translation', 'Traduisez: "Il est important de protéger l\'environnement."', 'Translate into English: "Il est important de protéger l\'environnement."', 'It is important to protect the environment.', effective_cefr, 'General', ['important','protect','environment']) last_given[key] = exercise.instruction session_state['last_given'] = last_given session_state['current_exercise'] = {'exercise': exercise, 'name': name, 'cefr': effective_cefr} adapted_note = " (level adapted by Planner / niveau adapté)" if effective_cefr != cefr else "" profile_md = (f"**{name}**{adapted_note} · Exercise level / Niveau **{effective_cefr}** · " f"{profile.total_exercises} sessions recorded / enregistrées · " f"{profile.avg_score*100:.0f}% average / moyenne") label = CATEGORY_LABELS.get(exercise.type, exercise.type.title()) lines = [f"**{label} — {effective_cefr}**", "", f"FR — {exercise.instruction}", "", f"EN — {exercise.instruction_en}"] if exercise.hints: lines.append("") lines.append(f"*Hints / Indices: {', '.join(exercise.hints)}*") exercise_md = "\n".join(lines) return profile_md, exercise_md, None, planner_panel(name), agent_strip_html(['planner']), session_state def evaluate_answer(learner_answer, session_state): session_state = dict(session_state) if session_state else {} current_exercise = session_state.get('current_exercise') if not current_exercise: return "Start a session first.", "", None, "", "", agent_strip_html([]), session_state if not learner_answer.strip(): return "Write an answer first.", "", None, "", "", agent_strip_html([]), session_state exercise = current_exercise['exercise'] name = current_exercise['name'] cefr = current_exercise['cefr'] source_match = re.search(r'"([^"]+)"', exercise.instruction) source_sentence = source_match.group(1) if source_match else None result = evaluator.evaluate(exercise.reference, learner_answer, category=exercise.type, source_sentence=source_sentence) if teacher: feedback = teacher.get_feedback(exercise, learner_answer, result.score, result.xai_explanation) feedback_md = ( f"**Correction** \n{feedback.correction}\n\n" f"**Explanation** \n{feedback.explanation}\n\n" f"**Suggestion** \n{feedback.improvement}\n\n" f"**Encouragement** \n{feedback.encouragement}\n\n" f"**Next focus** \n{feedback.next_focus}" ) else: feedback_md = result.feedback profile = profiler.update(name, result.score, exercise.type, cefr, learner_answer) score_md = ( f"### Response-quality equivalent / Qualité de réponse : {result.cefr} ({result.score_pct:.1f}%)\n" f"Reflects the semantic quality of this specific answer, not the exercise's " f"target level ({cefr}). / Reflète la qualité sémantique de cette réponse précise, " f"pas le niveau de l'exercice ({cefr}).\n\n" f"| Indicator / Indicateur | Value / Valeur |\n|---|---|\n" f"| Confidence / Confiance | {result.confidence*100:.0f}% |\n" f"| KAN branch | {result.gate_kan*100:.0f}% |\n" f"| Linear branch | {result.gate_linear*100:.0f}% |\n\n" f"*{result.xai_explanation}*" ) profile_md = (f"**{name}** · Exercise level / Niveau **{profile.current_cefr}** · " f"{profile.total_exercises} sessions recorded / enregistrées · " f"{profile.avg_score*100:.0f}% average / moyenne") active_agents = ['evaluator'] if teacher: active_agents.append('teacher') active_agents.append('planner') return score_md, feedback_md, plot_xai(result), profile_md, planner_panel(name), agent_strip_html(active_agents), session_state def show_report(name): if not name.strip(): return "Enter a name above first. / Entrez un nom d'abord.", "", None, agent_strip_html([]) p = profiler.get(name.strip()) r = profiler.report(name.strip()) if 'message' in r: return r['message'], "", None, agent_strip_html([]) md = ( f"**{r['name']}** · currently / actuellement **{r['cefr']}**\n\n" f"| Metric / Indicateur | Value / Valeur |\n|---|---|\n" f"| Sessions | {r['total']} |\n" f"| Average / Moyenne | {r['avg']} |\n" f"| Best / Meilleur | {r['best']} |\n" f"| Trend / Tendance | {r['progression']} |\n\n" f"Recent / Récents : {' · '.join(r['recent'])}" ) rep = reporter.generate_report(p) report_narrative = ( f"**{rep.trend_summary}**\n\n" f"{rep.strength} \n{rep.weakness}\n\n" f"{rep.stability_note}\n\n" f"**Recommandation du Reporter :** {rep.recommendation}" ) return md, report_narrative, plot_history(p.score_history), agent_strip_html(['reporter']) with gr.Blocks(theme=theme, css=CSS, title="Adaptive English Practice") as app: gr.HTML("""
Multi-Agent Language Learning System

Adaptive English Practice

""") agent_strip = gr.HTML(agent_strip_html([])) session_state = gr.State({}) with gr.Row(): with gr.Column(scale=1, elem_classes="sheet"): gr.HTML('
Learner / Apprenant
') name_input = gr.Textbox(label="Name / Nom", placeholder="e.g. Mouna Khelifi") cefr_input = gr.Dropdown(['A1','A2','B1','B2','C1','C2'], value='B1', label="Exercise level / Niveau de l'exercice") type_input = gr.Dropdown(TYPE_CHOICES, value='translation', label="Exercise category / Catégorie d'exercice") btn_start = gr.Button("Begin session / Commencer", variant="primary") profile_md = gr.Markdown("") planner_md = gr.Markdown("", elem_classes="note") with gr.Column(scale=2, elem_classes="sheet"): gr.HTML('
Exercise / Exercice
') exercise_md = gr.Markdown("*Select a level and click Begin session. / " "Choisissez un niveau et cliquez sur Commencer.*") answer_box = gr.Textbox(label="Your answer / Votre réponse", lines=3) btn_eval = gr.Button("Submit answer / Soumettre", variant="primary") gr.HTML('
') with gr.Row(): with gr.Column(scale=1, elem_classes="sheet"): gr.HTML('
Evaluation — TinyKAN-Distilled
') score_md = gr.Markdown("*No submission yet. / Aucune réponse soumise.*") with gr.Column(scale=1, elem_classes="sheet"): gr.HTML('
Feedback — Teacher Agent
') feedback_md = gr.Markdown("*No submission yet. / Aucune réponse soumise.*") with gr.Accordion("Explainability (XAI detail) / Détail explicatif", open=False): xai_plot = gr.Image(show_label=False, type="filepath") gr.HTML('
') with gr.Accordion("Learner history / Historique de l'apprenant", open=False): hist_name = gr.Textbox(label="Name / Nom") btn_report = gr.Button("Load history / Charger l'historique", size="sm") report_md = gr.Markdown("") history_plot = gr.Image(show_label=False, type="filepath") gr.HTML('
Reporter agent analysis / Analyse du Reporter
') reporter_md = gr.Markdown("*Load a history above to generate an analysis. / Chargez un historique ci-dessus pour générer une analyse.*") btn_start.click(fn=start_session, inputs=[name_input, cefr_input, type_input, session_state], outputs=[profile_md, exercise_md, xai_plot, planner_md, agent_strip, session_state]) btn_eval.click(fn=evaluate_answer, inputs=[answer_box, session_state], outputs=[score_md, feedback_md, xai_plot, profile_md, planner_md, agent_strip, session_state]) btn_report.click(fn=show_report, inputs=[hist_name], outputs=[report_md, reporter_md, history_plot, agent_strip]) if __name__ == '__main__': app.launch( server_name='0.0.0.0', server_port=int(os.environ.get('PORT', 7860)), pwa=True )