Restore full app with better error handling

app.py

@@ -1,16 +1,387 @@
 """
-Minimal test version
+Dynamic Intelligence - Human Demo Visualizer
+Egocentric hand tracking dataset visualizer for robot training data
 """
+
 import gradio as gr
+import json
+import html
+import numpy as np
 from pathlib import Path
+import plotly.graph_objects as go
+import plotly.io as pio
+from typing import Dict, List
 
+# Load pipeline data
 DATA_DIR = Path(__file__).parent / "data"
+
+def load_json_safe(path):
+    try:
+        with open(path, 'r') as f:
+            return json.load(f)
+    except Exception as e:
+        print(f"Error loading {path}: {e}")
+        return {}
+
+# Load data at startup
+print("Loading data...")
+metadata = load_json_safe(DATA_DIR / "metadata.json")
+end_effector = load_json_safe(DATA_DIR / "end_effector.json")
+actions = load_json_safe(DATA_DIR / "actions.json")
+hands_2d = load_json_safe(DATA_DIR / "hands_2d.json")
+print(f"Loaded: metadata={len(metadata)}, ee={len(end_effector)}, actions={len(actions)}, hands={len(hands_2d)}")
+
+# Stats
+total_frames = max(1, len(metadata.get('poses', [])))
+fps = metadata.get('fps', 60)
+hand_detection_rate = len(hands_2d) / total_frames * 100
+
+# Count poses safely
+left_poses = 0
+right_poses = 0
+for frame_data in end_effector.values():
+    if frame_data and isinstance(frame_data, dict):
+        if frame_data.get('left_hand'):
+            left_poses += 1
+        if frame_data.get('right_hand'):
+            right_poses += 1
+
+print(f"Stats: frames={total_frames}, left={left_poses}, right={right_poses}")
+
+# Video path
 video_path = DATA_DIR / "video.mp4"
 
-with gr.Blocks() as demo:
-    gr.Markdown("# Test")
-    gr.Video(value=str(video_path) if video_path.exists() else None)
+# Prepare time-series data
+def prepare_data():
+    """Prepare time-series data for plots."""
+    times = []
+    left_pos = {'x': [], 'y': [], 'z': []}
+    left_rot = {'yaw': [], 'pitch': [], 'roll': []}
+    right_pos = {'x': [], 'y': [], 'z': []}
+    right_rot = {'yaw': [], 'pitch': [], 'roll': []}
+    
+    frame_keys = sorted([int(k) for k in end_effector.keys() if k.isdigit()])
+    
+    for frame_idx in frame_keys:
+        frame_key = str(frame_idx)
+        t = frame_idx / fps
+        times.append(t)
+        
+        ee_data = end_effector.get(frame_key, {}) or {}
+        
+        # Left hand
+        left_hand_data = ee_data.get('left_hand')
+        if left_hand_data and isinstance(left_hand_data, dict):
+            pose = left_hand_data.get('pose_6dof')
+            if pose and len(pose) >= 6:
+                left_pos['x'].append(pose[0] * 100)  # m to cm
+                left_pos['y'].append(pose[1] * 100)
+                left_pos['z'].append(pose[2] * 100)
+                left_rot['roll'].append(pose[3] * 57.3)  # rad to deg
+                left_rot['pitch'].append(pose[4] * 57.3)
+                left_rot['yaw'].append(pose[5] * 57.3)
+            else:
+                for k in left_pos: left_pos[k].append(None)
+                for k in left_rot: left_rot[k].append(None)
+        else:
+            for k in left_pos: left_pos[k].append(None)
+            for k in left_rot: left_rot[k].append(None)
+        
+        # Right hand
+        right_hand_data = ee_data.get('right_hand')
+        if right_hand_data and isinstance(right_hand_data, dict):
+            pose = right_hand_data.get('pose_6dof')
+            if pose and len(pose) >= 6:
+                right_pos['x'].append(pose[0] * 100)
+                right_pos['y'].append(pose[1] * 100)
+                right_pos['z'].append(pose[2] * 100)
+                right_rot['roll'].append(pose[3] * 57.3)
+                right_rot['pitch'].append(pose[4] * 57.3)
+                right_rot['yaw'].append(pose[5] * 57.3)
+            else:
+                for k in right_pos: right_pos[k].append(None)
+                for k in right_rot: right_rot[k].append(None)
+        else:
+            for k in right_pos: right_pos[k].append(None)
+            for k in right_rot: right_rot[k].append(None)
+    
+    return {
+        'times': times,
+        'left_pos': left_pos,
+        'left_rot': left_rot,
+        'right_pos': right_pos,
+        'right_rot': right_rot
+    }
 
-if __name__ == "__main__":
-    demo.launch()
+try:
+    plot_data = prepare_data()
+except Exception as e:
+    print(f"Error preparing plot data: {e}")
+    plot_data = {
+        'times': [],
+        'left_pos': {'x': [], 'y': [], 'z': []},
+        'left_rot': {'yaw': [], 'pitch': [], 'roll': []},
+        'right_pos': {'x': [], 'y': [], 'z': []},
+        'right_rot': {'yaw': [], 'pitch': [], 'roll': []}
+    }
+
+METRIC_LABELS = {
+    "x_cm": "X (cm)",
+    "y_cm": "Y (cm)",
+    "z_cm": "Z (cm)",
+    "yaw_deg": "Yaw (°)",
+    "pitch_deg": "Pitch (°)",
+    "roll_deg": "Roll (°)",
+}
+
+PLOT_GRID = [
+    ["x_cm", "y_cm", "z_cm"],
+    ["yaw_deg", "pitch_deg", "roll_deg"],
+]
+
+PLOT_ORDER = [metric for row in PLOT_GRID for metric in row]
+
+CUSTOM_CSS = """
+:root, .gradio-container, body {
+    background-color: #050a18 !important;
+    color: #f8fafc !important;
+    font-family: 'Inter', 'Segoe UI', system-ui, sans-serif;
+}
+.side-panel {
+    background: #0f172a;
+    padding: 20px;
+    border-radius: 18px;
+    border: 1px solid #1f2b47;
+    min-height: 100%;
+}
+.stats-card ul {
+    list-style: none;
+    padding: 0;
+    margin: 0;
+    font-size: 0.92rem;
+}
+.stats-card li {
+    margin-bottom: 10px;
+    color: #e2e8f0;
+}
+.stats-card span {
+    display: inline-block;
+    margin-right: 6px;
+    color: #7dd3fc;
+}
+.main-panel {
+    padding-top: 8px;
+}
+.instruction-card {
+    background: #0f172a;
+    padding: 18px 20px;
+    border-radius: 18px;
+    border: 1px solid #1f2b47;
+}
+.instruction-label {
+    font-size: 0.75rem;
+    letter-spacing: 0.12em;
+    text-transform: uppercase;
+    color: #94a3b8;
+    margin-bottom: 10px;
+}
+.instruction-text {
+    font-size: 1.1rem;
+    line-height: 1.5;
+}
+.video-card {
+    background: #0f172a;
+    border: 1px solid #1f2b47;
+    border-radius: 18px;
+    padding: 18px 20px;
+    margin-top: 18px;
+}
+.video-title {
+    font-size: 0.78rem;
+    text-transform: uppercase;
+    letter-spacing: 0.18em;
+    color: #94a3b8;
+    margin-bottom: 8px;
+}
+.video-panel video {
+    border-radius: 12px;
+    border: 1px solid #1f2b47;
+    background: #030712;
+}
+.download-button button {
+    border-radius: 999px;
+    border: 1px solid #334155;
+    background: #1e293b;
+    color: #f8fafc;
+    font-size: 0.85rem;
+    padding: 8px 24px;
+}
+.download-button button:hover {
+    border-color: #67e8f9;
+    color: #67e8f9;
+}
+.plots-wrap {
+    margin-top: 18px;
+}
+.plots-wrap .gr-row {
+    gap: 16px;
+}
+.plot-html {
+    background: #111a2c;
+    border-radius: 12px;
+    padding: 10px;
+    border: 1px solid #1f2b47;
+    min-height: 320px;
+}
+.plot-html iframe {
+    width: 100%;
+    height: 300px;
+    border: none;
+}
+"""
+
+def build_plot_fig(metric: str, hand: str = "left") -> go.Figure:
+    """Build Plotly figure for a metric."""
+    try:
+        times = plot_data.get('times', [])
+        
+        if hand == "left":
+            if "cm" in metric:
+                key = metric.replace('_cm', '')
+                data = plot_data.get('left_pos', {}).get(key, [])
+            else:
+                key = metric.replace('_deg', '')
+                data = plot_data.get('left_rot', {}).get(key, [])
+            name = "Left Hand"
+        else:
+            if "cm" in metric:
+                key = metric.replace('_cm', '')
+                data = plot_data.get('right_pos', {}).get(key, [])
+            else:
+                key = metric.replace('_deg', '')
+                data = plot_data.get('right_rot', {}).get(key, [])
+            name = "Right Hand"
+        
+        if not times or not data:
+            # Return empty plot
+            times = [0]
+            data = [0]
+    except Exception as e:
+        print(f"Error in build_plot_fig: {e}")
+        times = [0]
+        data = [0]
+        name = "Error"
+    
+    fig = go.Figure()
+    fig.add_trace(
+        go.Scatter(
+            x=times,
+            y=data,
+            mode="lines",
+            name=name,
+            line=dict(color="#67e8f9", width=2)
+        )
+    )
+    fig.update_layout(
+        margin=dict(l=20, r=20, t=30, b=20),
+        height=250,
+        template="plotly_dark",
+        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
+        xaxis_title="Time (s)",
+        yaxis_title=METRIC_LABELS[metric],
+    )
+    fig.update_xaxes(showgrid=True, gridwidth=0.5, gridcolor="rgba(255,255,255,0.1)")
+    fig.update_yaxes(showgrid=True, gridwidth=0.5, gridcolor="rgba(255,255,255,0.1)")
+    return fig
+
+def build_plot_html(metric: str, hand: str = "left") -> str:
+    """Build Plotly HTML for a metric."""
+    fig = build_plot_fig(metric, hand)
+    return pio.to_html(fig, include_plotlyjs="cdn", full_html=False)
+
+# Build interface
+stats_html = f"""
+<div class="stats-card">
+  <ul>
+    <li><span>Number of samples/frames:</span> {total_frames:,}</li>
+    <li><span>Hand detection rate:</span> {hand_detection_rate:.1f}%</li>
+    <li><span>Left hand poses:</span> {left_poses}</li>
+    <li><span>Right hand poses:</span> {right_poses}</li>
+    <li><span>Frames per second:</span> {fps:.1f}</li>
+  </ul>
+</div>
+"""
+
+instruction_text = "LiDAR-based egocentric hand tracking for robot training data"
 
+theme = gr.themes.Soft(
+    primary_hue="cyan", secondary_hue="blue", neutral_hue="slate"
+).set(
+    body_background_fill="#0c1424",
+    body_text_color="#f8fafc",
+    block_background_fill="#111a2c",
+    block_title_text_color="#f8fafc",
+    input_background_fill="#151f33",
+    border_color_primary="#1f2b47",
+    shadow_drop="none",
+)
+
+def format_instruction_html(text: str) -> str:
+    safe_text = html.escape(text)
+    return (
+        '<div class="instruction-card">'
+        '<p class="instruction-label">Language Instruction</p>'
+        f'<p class="instruction-text">{safe_text}</p>'
+        "</div>"
+    )
+
+with gr.Blocks(theme=theme, css=CUSTOM_CSS) as demo:
+    gr.Markdown("# 🤖 Dynamic Intelligence - Human Demo Visualizer")
+    gr.Markdown(
+        "Egocentric hand tracking dataset for humanoid robot training. "
+        "Pipeline: iPhone LiDAR → MediaPipe → 6DoF End-Effector → Robot Training Data"
+    )
+
+    with gr.Row(equal_height=True):
+        with gr.Column(scale=1, min_width=260, elem_classes=["side-panel"]):
+            gr.HTML(stats_html)
+        with gr.Column(scale=2, min_width=640, elem_classes=["main-panel"]):
+            instruction_box = gr.HTML(
+                format_instruction_html(instruction_text),
+                label="Language Instruction",
+            )
+            with gr.Column(elem_classes=["video-card"]):
+                gr.HTML('<div class="video-title">RGB Video</div>')
+                video = gr.Video(
+                    height=360,
+                    value=str(video_path) if video_path.exists() else None,
+                    elem_classes=["video-panel"],
+                    show_label=False,
+                    show_download_button=False,
+                )
+                download_button = gr.DownloadButton(
+                    label="Download",
+                    value=str(video_path) if video_path.exists() else None,
+                    elem_classes=["download-button"],
+                )
+
+    gr.Markdown("### Left Hand Trajectories", elem_classes=["plots-title"])
+    plot_outputs_left = []
+    with gr.Column(elem_classes=["plots-wrap"]):
+        for row in PLOT_GRID:
+            with gr.Row():
+                for metric in row:
+                    plot = gr.HTML(value=build_plot_html(metric, "left"), elem_classes=["plot-html"])
+                    plot_outputs_left.append(plot)
+    
+    gr.Markdown("### Right Hand Trajectories", elem_classes=["plots-title"])
+    plot_outputs_right = []
+    with gr.Column(elem_classes=["plots-wrap"]):
+        for row in PLOT_GRID:
+            with gr.Row():
+                for metric in row:
+                    plot = gr.HTML(value=build_plot_html(metric, "right"), elem_classes=["plot-html"])
+                    plot_outputs_right.append(plot)
+
+if __name__ == "__main__":
+    demo.queue().launch(show_api=False)