import sys import os import types # Python 3.13 audioop stub (not needed on 3.11 but harmless) if 'audioop' not in sys.modules: sys.modules['audioop'] = types.ModuleType('audioop') import gradio as gr import numpy as np import matplotlib.pyplot as plt import matplotlib matplotlib.use('Agg') model = None def load_model(): global model if model is not None: return "✅ Already loaded!" try: from tribev2 import TribeModel model = TribeModel.from_pretrained("facebook/tribev2", cache_folder="./tribe_cache") return "✅ Model loaded!" except Exception as e: return f"❌ Error: {str(e)}" REGIONS = [ ("Visual cortex", 0.00, 0.15, "#378ADD"), ("Auditory cortex", 0.15, 0.30, "#D85A30"), ("Language (Broca's area)", 0.30, 0.45, "#7F77DD"), ("Prefrontal (attention)", 0.45, 0.62, "#1D9E75"), ("Temporal (memory)", 0.62, 0.78, "#BA7517"), ("Emotion (limbic)", 0.78, 1.00, "#D4537E"), ] def score_predictions(preds): avg = np.mean(np.abs(preds), axis=0) global_max = avg.max() + 1e-8 half = len(avg) // 2 scores = {} for name, s, e, _ in REGIONS: start, end = int(half * s), int(half * e) scores[name] = round(float(np.mean(avg[start:end]) / global_max * 100), 1) return scores, round(sum(scores.values()) / len(scores), 1) def make_brain_plot(preds): try: from nilearn import plotting, datasets avg = np.mean(np.abs(preds), axis=0) avg_norm = (avg - avg.min()) / (avg.max() - avg.min() + 1e-8) half = len(avg_norm) // 2 fsaverage = datasets.fetch_surf_fsaverage("fsaverage5") fig, axes = plt.subplots(1, 2, figsize=(14, 5), subplot_kw={"projection": "3d"}) fig.patch.set_facecolor("#111111") plotting.plot_surf_stat_map(fsaverage.infl_left, avg_norm[:half], hemi="left", view="lateral", colorbar=True, cmap="hot", title="Left hemisphere", axes=axes[0], figure=fig) plotting.plot_surf_stat_map(fsaverage.infl_right, avg_norm[half:], hemi="right", view="lateral", colorbar=True, cmap="hot", title="Right hemisphere", axes=axes[1], figure=fig) plt.tight_layout() plt.savefig("/tmp/brain_map.png", dpi=130, bbox_inches="tight", facecolor="#111111") plt.close() return "/tmp/brain_map.png" except Exception as e: print(f"Brain plot error: {e}") return None def make_score_chart(scores, overall): fig, ax = plt.subplots(figsize=(9, 4)) fig.patch.set_facecolor("#1a1a1a") ax.set_facecolor("#1a1a1a") names = [r[0] for r in REGIONS] colors = [r[3] for r in REGIONS] vals = [scores.get(n, 0) for n in names] bars = ax.barh(names, vals, color=colors, height=0.55) ax.set_xlim(0, 100) ax.axvline(70, color="#888", linestyle="--", linewidth=1, alpha=0.6) ax.set_xlabel("Activation score", color="#ccc", fontsize=11) ax.set_title(f"Brain region activation | Overall: {overall}/100", color="white", fontsize=13, fontweight="bold", pad=12) ax.tick_params(colors="#ccc") for spine in ax.spines.values(): spine.set_edgecolor("#333") for bar, val in zip(bars, vals): ax.text(bar.get_width() + 1, bar.get_y() + bar.get_height() / 2, f"{val}", va="center", color="white", fontsize=10, fontweight="bold") plt.tight_layout() plt.savefig("/tmp/score_chart.png", dpi=130, bbox_inches="tight", facecolor="#1a1a1a") plt.close() return "/tmp/score_chart.png" def generate_suggestions(scores, overall): tips = [] if scores.get("Prefrontal (attention)", 100) < 70: tips.append("→ Open with a bold question or surprising fact to boost attention") if scores.get("Emotion (limbic)", 100) < 70: tips.append("→ Add emotional language — 'imagine', 'feel', personal stories") if scores.get("Temporal (memory)", 100) < 70: tips.append("→ Include specific numbers or data points to improve memorability") if scores.get("Visual cortex", 100) < 70: tips.append("→ Use more visual language — describe what viewers will 'see'") if scores.get("Language (Broca's area)", 100) < 70: tips.append("→ Break long sentences into shorter, punchier ones") if scores.get("Auditory cortex", 100) < 70: tips.append("→ Add rhythm and repetition — the brain responds to sound patterns") if not tips: tips.append("→ Excellent! Consider adding a strong call-to-action at the end") status = "🟢 Strong" if overall >= 75 else "🟡 Good, needs polish" if overall >= 55 else "🔴 Needs work" return f"**Overall: {overall}/100 — {status}**\n\n" + "\n".join(tips) def analyze_script(script_text, progress=gr.Progress()): if not script_text or not script_text.strip(): return None, None, "⚠️ Please paste a script first.", None if model is None: progress(0.1, desc="Loading TRIBE v2 model (first time ~5 mins)...") msg = load_model() if "Error" in msg: return None, None, msg, None try: from gtts import gTTS progress(0.2, desc="Converting script to speech...") tts = gTTS(text=script_text.strip(), lang="en", slow=False) tts.save("/tmp/script_audio.mp3") progress(0.4, desc="Running TRIBE v2 prediction (1-3 mins)...") df = model.get_events_dataframe(audio_path="/tmp/script_audio.mp3") preds, segments = model.predict(events=df) progress(0.7, desc="Scoring regions...") scores, overall = score_predictions(preds) progress(0.8, desc="Rendering maps...") brain_img = make_brain_plot(preds) score_img = make_score_chart(scores, overall) suggestions = generate_suggestions(scores, overall) np.save("/tmp/brain_predictions.npy", preds) progress(1.0, desc="Done!") return brain_img, score_img, suggestions, "/tmp/brain_predictions.npy" except Exception as e: return None, None, f"❌ Error:\n{str(e)}", None css = "#title{text-align:center} #subtitle{text-align:center;color:#888;font-size:14px}" with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo"), css=css) as demo: gr.Markdown("# 🧠 Script Brain Optimizer", elem_id="title") gr.Markdown("Paste your script → real fMRI predictions via **TRIBE v2** → iterate", elem_id="subtitle") with gr.Row(): with gr.Column(scale=1): script_input = gr.Textbox(label="Your script", placeholder="Paste your content script here...", lines=12, max_lines=20) with gr.Row(): clear_btn = gr.Button("Clear", variant="secondary", scale=1) analyze_btn = gr.Button("🧠 Analyze", variant="primary", scale=3) suggestions_out = gr.Markdown(value="*Paste a script and click Analyze...*") download_out = gr.File(label="Download predictions (.npy)") with gr.Column(scale=2): brain_img_out = gr.Image(label="Brain activation map", height=320) score_img_out = gr.Image(label="Region scores", height=280) analyze_btn.click(fn=analyze_script, inputs=[script_input], outputs=[brain_img_out, score_img_out, suggestions_out, download_out]) clear_btn.click(fn=lambda: ("", None, None, "*Paste a script and click Analyze...*", None), outputs=[script_input, brain_img_out, score_img_out, suggestions_out, download_out]) gr.Markdown("---\n*Powered by [TRIBE v2](https://github.com/facebookresearch/tribev2) by Meta FAIR*") if __name__ == "__main__": demo.launch(server_name="0.0.0.0", server_port=7860)