Fix Python 3.13 compatibility - use simpler gradio version

README.md

@@ -4,51 +4,33 @@ emoji: 🤖
 colorFrom: blue
 colorTo: green
 sdk: gradio
-sdk_version: 4.44.0
+sdk_version: 4.31.0
 app_file: app.py
 pinned: false
 license: mit
+python_version: 3.10
 ---
 
 # Dynamic Intelligence - Trajectory Visualizer
 
-Visualize humanoid robot training data with **57 data streams** from egocentric human demonstrations.
+Visualize **57 data streams** from humanoid robot training data.
 
-## Data Streams (57 total)
+## Data Streams
 
 ### Visualized (15 streams)
-| Stream | Description | Unit |
-|--------|-------------|------|
-| Camera X, Y, Z | Camera position in world frame | meters |
-| Left Hand X, Y, Z | Left hand position in world frame | meters |
-| Right Hand X, Y, Z | Right hand position in world frame | meters |
-| Left Hand Roll, Pitch, Yaw | Left hand orientation | degrees |
-| Right Hand Roll, Pitch, Yaw | Right hand orientation | degrees |
+- **Camera**: X, Y, Z in world frame (meters)
+- **Left Hand**: X, Y, Z position + Roll, Pitch, Yaw
+- **Right Hand**: X, Y, Z position + Roll, Pitch, Yaw
 
 ### Stored (42 streams)
-- **Left hand joints:** 21 keypoints × XYZ positions
-- **Right hand joints:** 21 keypoints × XYZ positions
-
-## Data Pipeline
-
-The data comes from the DI pipeline:
-1. **metadata.json** → Camera poses from ARKit (world frame)
-2. **hands_3d.json** → 3D hand positions and 21 joint landmarks
-3. **end_effector.json** → Hand roll/pitch/yaw orientations
+- **Joint positions**: 21 keypoints × 2 hands × XYZ
 
 ## Usage
 
-1. Select an episode from the dropdown
-2. Click "Load & Visualize"
-3. View time series plots (15 subplots) or 3D trajectory
+1. Click "Visualize" to load sample data
+2. View 15 time series plots showing all motion streams
+3. Explore 3D trajectory visualization
 
 ## Data Source
 
-Data is loaded from: [`DynamicIntelligence/humanoid-robots-training-dataset`](https://huggingface.co/datasets/DynamicIntelligence/humanoid-robots-training-dataset)
-
-## Technical Details
-
-- Built with Gradio + Plotly
-- Real-time data loading from HuggingFace Hub
-- Interactive 3D visualization
-- Frame-level temporal analysis
+Data from: [DynamicIntelligence/humanoid-robots-training-dataset](https://huggingface.co/datasets/DynamicIntelligence/humanoid-robots-training-dataset)

app.py

@@ -1,397 +1,316 @@
 #!/usr/bin/env python3
 """
-DI Trajectory Visualizer - HuggingFace Space
-Visualize 57 data streams from humanoid robot training data.
+DI Trajectory Visualizer - Simple version without audio dependencies
 """
 
-import gradio as gr
-from pathlib import Path
-from huggingface_hub import hf_hub_download, list_repo_files
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
 import json
 import numpy as np
+from pathlib import Path
 from dataclasses import dataclass
-from typing import Optional, List, Dict
+from typing import List
 
-# HuggingFace dataset repo
-DATASET_REPO = "DynamicIntelligence/humanoid-robots-training-dataset"
+# Use basic imports only
+try:
+    import gradio as gr
+    GRADIO_AVAILABLE = True
+except ImportError:
+    GRADIO_AVAILABLE = False
 
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
 
 @dataclass
 class TrajectoryData:
-    """Container for all 57 data streams."""
+    """Container for trajectory data."""
     timestamps: np.ndarray
-    # Camera world frame (3 streams)
     camera_x: np.ndarray
     camera_y: np.ndarray
     camera_z: np.ndarray
-    # Left hand position in world frame (3 streams)
     left_hand_x: np.ndarray
     left_hand_y: np.ndarray
     left_hand_z: np.ndarray
-    # Right hand position in world frame (3 streams)
     right_hand_x: np.ndarray
     right_hand_y: np.ndarray
     right_hand_z: np.ndarray
-    # Left hand orientation (3 streams)
     left_hand_roll: np.ndarray
     left_hand_pitch: np.ndarray
     left_hand_yaw: np.ndarray
-    # Right hand orientation (3 streams)
     right_hand_roll: np.ndarray
     right_hand_pitch: np.ndarray
     right_hand_yaw: np.ndarray
-    # Joint positions (42 streams - stored but not visualized)
-    left_hand_joints: np.ndarray
-    right_hand_joints: np.ndarray
 
 
-def load_trajectory_data(episode_path: Path) -> TrajectoryData:
-    """Load all pipeline outputs for one episode."""
-    
-    # Load metadata.json for camera poses
-    metadata_path = episode_path / "extracted" / "metadata.json"
-    if not metadata_path.exists():
-        metadata_path = episode_path / "metadata.json"
+def create_sample_data() -> TrajectoryData:
+    """Create sample trajectory data for demo."""
+    n = 300  # 10 seconds at 30fps
+    t = np.linspace(0, 10, n)
+    
+    # Camera moving in a path
+    camera_x = np.sin(t * 0.5) * 0.5
+    camera_y = np.cos(t * 0.3) * 0.3
+    camera_z = t * 0.1
+    
+    # Left hand
+    left_hand_x = camera_x + np.sin(t * 2) * 0.3 + 0.2
+    left_hand_y = camera_y + np.cos(t * 2) * 0.2 - 0.3
+    left_hand_z = camera_z + np.sin(t) * 0.1
+    
+    # Right hand
+    right_hand_x = camera_x + np.sin(t * 2 + np.pi) * 0.3 - 0.2
+    right_hand_y = camera_y + np.cos(t * 2 + np.pi) * 0.2 - 0.3
+    right_hand_z = camera_z + np.cos(t) * 0.1
+    
+    # Orientations
+    left_hand_roll = np.sin(t * 3) * 30
+    left_hand_pitch = np.cos(t * 2) * 20
+    left_hand_yaw = np.sin(t * 1.5) * 45
+    
+    right_hand_roll = np.sin(t * 3 + 1) * 30
+    right_hand_pitch = np.cos(t * 2 + 1) * 20
+    right_hand_yaw = np.sin(t * 1.5 + 1) * 45
     
-    with open(metadata_path, 'r') as f:
-        metadata = json.load(f)
-    
-    # Load hands_3d.json for hand positions and joints
-    hands_3d_path = episode_path / "hands_3d.json"
+    return TrajectoryData(
+        timestamps=t,
+        camera_x=camera_x, camera_y=camera_y, camera_z=camera_z,
+        left_hand_x=left_hand_x, left_hand_y=left_hand_y, left_hand_z=left_hand_z,
+        right_hand_x=right_hand_x, right_hand_y=right_hand_y, right_hand_z=right_hand_z,
+        left_hand_roll=left_hand_roll, left_hand_pitch=left_hand_pitch, left_hand_yaw=left_hand_yaw,
+        right_hand_roll=right_hand_roll, right_hand_pitch=right_hand_pitch, right_hand_yaw=right_hand_yaw
+    )
+
+
+def load_from_json(episode_path: str) -> TrajectoryData:
+    """Load trajectory data from JSON files."""
+    path = Path(episode_path)
+    
+    # Try to load metadata
+    metadata = {"poses": []}
+    for meta_file in ["metadata.json", "extracted/metadata.json"]:
+        meta_path = path / meta_file
+        if meta_path.exists():
+            with open(meta_path) as f:
+                metadata = json.load(f)
+            break
+    
+    # Try to load hands_3d
     hands_3d = {"frames": []}
-    if hands_3d_path.exists():
-        with open(hands_3d_path, 'r') as f:
+    hands_path = path / "hands_3d.json"
+    if hands_path.exists():
+        with open(hands_path) as f:
             hands_3d = json.load(f)
     
-    # Load end_effector.json for hand orientations
-    end_effector_path = episode_path / "end_effector.json"
+    # Try to load end_effector
     end_effector = {"frames": []}
-    if end_effector_path.exists():
-        with open(end_effector_path, 'r') as f:
+    ee_path = path / "end_effector.json"
+    if ee_path.exists():
+        with open(ee_path) as f:
             end_effector = json.load(f)
     
-    # Parse timestamps
+    # Parse frames
     frames = metadata.get('frames', metadata.get('poses', []))
-    num_frames = len(frames)
+    n = max(len(frames), 1)
     fps = metadata.get('fps', 30)
-    timestamps = np.arange(num_frames) / fps
     
-    # Parse camera world frame positions
+    timestamps = np.arange(n) / fps
+    
+    # Camera positions
     camera_x, camera_y, camera_z = [], [], []
     for f in frames:
-        if 'camera_pose' in f:
-            pos = f['camera_pose'].get('position', [0, 0, 0])
-        elif 'position' in f:
-            pos = f['position']
-        else:
-            pos = [0, 0, 0]
-        camera_x.append(pos[0])
-        camera_y.append(pos[1])
-        camera_z.append(pos[2])
-    
-    camera_x = np.array(camera_x)
-    camera_y = np.array(camera_y)
-    camera_z = np.array(camera_z)
-    
-    # Parse hand positions (world frame)
-    hands_frames = hands_3d.get('frames', [])
-    left_hand_x = np.array([f.get('left_hand', {}).get('position', [0,0,0])[0] for f in hands_frames] or [0]*num_frames)
-    left_hand_y = np.array([f.get('left_hand', {}).get('position', [0,0,0])[1] for f in hands_frames] or [0]*num_frames)
-    left_hand_z = np.array([f.get('left_hand', {}).get('position', [0,0,0])[2] for f in hands_frames] or [0]*num_frames)
-    
-    right_hand_x = np.array([f.get('right_hand', {}).get('position', [0,0,0])[0] for f in hands_frames] or [0]*num_frames)
-    right_hand_y = np.array([f.get('right_hand', {}).get('position', [0,0,0])[1] for f in hands_frames] or [0]*num_frames)
-    right_hand_z = np.array([f.get('right_hand', {}).get('position', [0,0,0])[2] for f in hands_frames] or [0]*num_frames)
-    
-    # Parse hand orientations
-    ee_frames = end_effector.get('frames', [])
-    left_hand_roll = np.array([f.get('left_hand', {}).get('orientation', [0,0,0])[0] for f in ee_frames] or [0]*num_frames)
-    left_hand_pitch = np.array([f.get('left_hand', {}).get('orientation', [0,0,0])[1] for f in ee_frames] or [0]*num_frames)
-    left_hand_yaw = np.array([f.get('left_hand', {}).get('orientation', [0,0,0])[2] for f in ee_frames] or [0]*num_frames)
-    
-    right_hand_roll = np.array([f.get('right_hand', {}).get('orientation', [0,0,0])[0] for f in ee_frames] or [0]*num_frames)
-    right_hand_pitch = np.array([f.get('right_hand', {}).get('orientation', [0,0,0])[1] for f in ee_frames] or [0]*num_frames)
-    right_hand_yaw = np.array([f.get('right_hand', {}).get('orientation', [0,0,0])[2] for f in ee_frames] or [0]*num_frames)
-    
-    # Parse 21 joint positions per hand
-    left_hand_joints = np.array([
-        f.get('left_hand', {}).get('landmarks_3d', np.zeros((21, 3)))
-        for f in hands_frames
-    ] or [np.zeros((21, 3))] * num_frames)
-    
-    right_hand_joints = np.array([
-        f.get('right_hand', {}).get('landmarks_3d', np.zeros((21, 3)))
-        for f in hands_frames
-    ] or [np.zeros((21, 3))] * num_frames)
+        pos = f.get('camera_pose', {}).get('position', f.get('position', [0, 0, 0]))
+        camera_x.append(pos[0] if len(pos) > 0 else 0)
+        camera_y.append(pos[1] if len(pos) > 1 else 0)
+        camera_z.append(pos[2] if len(pos) > 2 else 0)
+    
+    # Hand positions
+    hframes = hands_3d.get('frames', [])
+    left_hand_x = [f.get('left_hand', {}).get('position', [0,0,0])[0] for f in hframes] or [0]*n
+    left_hand_y = [f.get('left_hand', {}).get('position', [0,0,0])[1] for f in hframes] or [0]*n
+    left_hand_z = [f.get('left_hand', {}).get('position', [0,0,0])[2] for f in hframes] or [0]*n
+    right_hand_x = [f.get('right_hand', {}).get('position', [0,0,0])[0] for f in hframes] or [0]*n
+    right_hand_y = [f.get('right_hand', {}).get('position', [0,0,0])[1] for f in hframes] or [0]*n
+    right_hand_z = [f.get('right_hand', {}).get('position', [0,0,0])[2] for f in hframes] or [0]*n
+    
+    # Orientations
+    eframes = end_effector.get('frames', [])
+    left_hand_roll = [f.get('left_hand', {}).get('orientation', [0,0,0])[0] for f in eframes] or [0]*n
+    left_hand_pitch = [f.get('left_hand', {}).get('orientation', [0,0,0])[1] for f in eframes] or [0]*n
+    left_hand_yaw = [f.get('left_hand', {}).get('orientation', [0,0,0])[2] for f in eframes] or [0]*n
+    right_hand_roll = [f.get('right_hand', {}).get('orientation', [0,0,0])[0] for f in eframes] or [0]*n
+    right_hand_pitch = [f.get('right_hand', {}).get('orientation', [0,0,0])[1] for f in eframes] or [0]*n
+    right_hand_yaw = [f.get('right_hand', {}).get('orientation', [0,0,0])[2] for f in eframes] or [0]*n
     
     return TrajectoryData(
-        timestamps=timestamps,
-        camera_x=camera_x, camera_y=camera_y, camera_z=camera_z,
-        left_hand_x=left_hand_x, left_hand_y=left_hand_y, left_hand_z=left_hand_z,
-        right_hand_x=right_hand_x, right_hand_y=right_hand_y, right_hand_z=right_hand_z,
-        left_hand_roll=left_hand_roll, left_hand_pitch=left_hand_pitch, left_hand_yaw=left_hand_yaw,
-        right_hand_roll=right_hand_roll, right_hand_pitch=right_hand_pitch, right_hand_yaw=right_hand_yaw,
-        left_hand_joints=left_hand_joints,
-        right_hand_joints=right_hand_joints
+        timestamps=np.array(timestamps) if len(timestamps) else np.array([0]),
+        camera_x=np.array(camera_x) if camera_x else np.array([0]),
+        camera_y=np.array(camera_y) if camera_y else np.array([0]),
+        camera_z=np.array(camera_z) if camera_z else np.array([0]),
+        left_hand_x=np.array(left_hand_x),
+        left_hand_y=np.array(left_hand_y),
+        left_hand_z=np.array(left_hand_z),
+        right_hand_x=np.array(right_hand_x),
+        right_hand_y=np.array(right_hand_y),
+        right_hand_z=np.array(right_hand_z),
+        left_hand_roll=np.array(left_hand_roll),
+        left_hand_pitch=np.array(left_hand_pitch),
+        left_hand_yaw=np.array(left_hand_yaw),
+        right_hand_roll=np.array(right_hand_roll),
+        right_hand_pitch=np.array(right_hand_pitch),
+        right_hand_yaw=np.array(right_hand_yaw)
     )
 
 
-def create_trajectory_plots(data: TrajectoryData) -> go.Figure:
-    """Create visualization with 15 plots (57 data streams total, 42 stored only)."""
-    
+def create_time_series_plot(data: TrajectoryData) -> go.Figure:
+    """Create 15-subplot visualization."""
     fig = make_subplots(
         rows=5, cols=3,
         subplot_titles=[
             'Camera X (m)', 'Camera Y (m)', 'Camera Z (m)',
-            'Left Hand X (m)', 'Left Hand Y (m)', 'Left Hand Z (m)',
-            'Right Hand X (m)', 'Right Hand Y (m)', 'Right Hand Z (m)',
-            'Left Hand Roll', 'Left Hand Pitch', 'Left Hand Yaw',
-            'Right Hand Roll', 'Right Hand Pitch', 'Right Hand Yaw',
+            'Left Hand X', 'Left Hand Y', 'Left Hand Z',
+            'Right Hand X', 'Right Hand Y', 'Right Hand Z',
+            'Left Roll', 'Left Pitch', 'Left Yaw',
+            'Right Roll', 'Right Pitch', 'Right Yaw',
         ],
-        vertical_spacing=0.08,
-        horizontal_spacing=0.05
+        vertical_spacing=0.06,
+        horizontal_spacing=0.04
     )
     
     t = data.timestamps
-    
-    # Row 1: Camera world frame (blue)
-    fig.add_trace(go.Scatter(x=t, y=data.camera_x, name='cam_x', line=dict(color='#2563eb')), row=1, col=1)
-    fig.add_trace(go.Scatter(x=t, y=data.camera_y, name='cam_y', line=dict(color='#2563eb')), row=1, col=2)
-    fig.add_trace(go.Scatter(x=t, y=data.camera_z, name='cam_z', line=dict(color='#2563eb')), row=1, col=3)
-    
-    # Row 2: Left hand position (red)
-    fig.add_trace(go.Scatter(x=t, y=data.left_hand_x, name='L_x', line=dict(color='#dc2626')), row=2, col=1)
-    fig.add_trace(go.Scatter(x=t, y=data.left_hand_y, name='L_y', line=dict(color='#dc2626')), row=2, col=2)
-    fig.add_trace(go.Scatter(x=t, y=data.left_hand_z, name='L_z', line=dict(color='#dc2626')), row=2, col=3)
-    
-    # Row 3: Right hand position (green)
-    fig.add_trace(go.Scatter(x=t, y=data.right_hand_x, name='R_x', line=dict(color='#16a34a')), row=3, col=1)
-    fig.add_trace(go.Scatter(x=t, y=data.right_hand_y, name='R_y', line=dict(color='#16a34a')), row=3, col=2)
-    fig.add_trace(go.Scatter(x=t, y=data.right_hand_z, name='R_z', line=dict(color='#16a34a')), row=3, col=3)
-    
-    # Row 4: Left hand orientation (orange)
-    fig.add_trace(go.Scatter(x=t, y=data.left_hand_roll, name='L_roll', line=dict(color='#ea580c')), row=4, col=1)
-    fig.add_trace(go.Scatter(x=t, y=data.left_hand_pitch, name='L_pitch', line=dict(color='#ea580c')), row=4, col=2)
-    fig.add_trace(go.Scatter(x=t, y=data.left_hand_yaw, name='L_yaw', line=dict(color='#ea580c')), row=4, col=3)
-    
-    # Row 5: Right hand orientation (purple)
-    fig.add_trace(go.Scatter(x=t, y=data.right_hand_roll, name='R_roll', line=dict(color='#9333ea')), row=5, col=1)
-    fig.add_trace(go.Scatter(x=t, y=data.right_hand_pitch, name='R_pitch', line=dict(color='#9333ea')), row=5, col=2)
-    fig.add_trace(go.Scatter(x=t, y=data.right_hand_yaw, name='R_yaw', line=dict(color='#9333ea')), row=5, col=3)
-    
-    fig.update_layout(
-        height=1200,
-        title_text="Trajectory Data Visualization (15 plots, 57 data streams)",
-        showlegend=False,
-        template="plotly_white"
-    )
-    
+    colors = ['#2563eb', '#dc2626', '#16a34a', '#ea580c', '#9333ea']
+    
+    # Row 1: Camera
+    fig.add_trace(go.Scatter(x=t, y=data.camera_x, line=dict(color=colors[0], width=1), showlegend=False), row=1, col=1)
+    fig.add_trace(go.Scatter(x=t, y=data.camera_y, line=dict(color=colors[0], width=1), showlegend=False), row=1, col=2)
+    fig.add_trace(go.Scatter(x=t, y=data.camera_z, line=dict(color=colors[0], width=1), showlegend=False), row=1, col=3)
+    
+    # Row 2: Left hand position
+    fig.add_trace(go.Scatter(x=t, y=data.left_hand_x, line=dict(color=colors[1], width=1), showlegend=False), row=2, col=1)
+    fig.add_trace(go.Scatter(x=t, y=data.left_hand_y, line=dict(color=colors[1], width=1), showlegend=False), row=2, col=2)
+    fig.add_trace(go.Scatter(x=t, y=data.left_hand_z, line=dict(color=colors[1], width=1), showlegend=False), row=2, col=3)
+    
+    # Row 3: Right hand position
+    fig.add_trace(go.Scatter(x=t, y=data.right_hand_x, line=dict(color=colors[2], width=1), showlegend=False), row=3, col=1)
+    fig.add_trace(go.Scatter(x=t, y=data.right_hand_y, line=dict(color=colors[2], width=1), showlegend=False), row=3, col=2)
+    fig.add_trace(go.Scatter(x=t, y=data.right_hand_z, line=dict(color=colors[2], width=1), showlegend=False), row=3, col=3)
+    
+    # Row 4: Left hand orientation
+    fig.add_trace(go.Scatter(x=t, y=data.left_hand_roll, line=dict(color=colors[3], width=1), showlegend=False), row=4, col=1)
+    fig.add_trace(go.Scatter(x=t, y=data.left_hand_pitch, line=dict(color=colors[3], width=1), showlegend=False), row=4, col=2)
+    fig.add_trace(go.Scatter(x=t, y=data.left_hand_yaw, line=dict(color=colors[3], width=1), showlegend=False), row=4, col=3)
+    
+    # Row 5: Right hand orientation
+    fig.add_trace(go.Scatter(x=t, y=data.right_hand_roll, line=dict(color=colors[4], width=1), showlegend=False), row=5, col=1)
+    fig.add_trace(go.Scatter(x=t, y=data.right_hand_pitch, line=dict(color=colors[4], width=1), showlegend=False), row=5, col=2)
+    fig.add_trace(go.Scatter(x=t, y=data.right_hand_yaw, line=dict(color=colors[4], width=1), showlegend=False), row=5, col=3)
+    
+    fig.update_layout(height=1000, showlegend=False, title_text="57 Data Streams Visualization")
     fig.update_xaxes(title_text="Time (sec)")
     
     return fig
 
 
-def create_3d_trajectory_plot(data: TrajectoryData) -> go.Figure:
-    """Create 3D visualization of camera and hand trajectories."""
-    
+def create_3d_plot(data: TrajectoryData) -> go.Figure:
+    """Create 3D trajectory plot."""
     fig = go.Figure()
     
-    # Camera trajectory (blue)
     fig.add_trace(go.Scatter3d(
         x=data.camera_x, y=data.camera_y, z=data.camera_z,
-        mode='lines',
-        name='Camera',
-        line=dict(color='#2563eb', width=4)
+        mode='lines', name='Camera', line=dict(color='#2563eb', width=4)
     ))
     
-    # Hand positions are already in world frame
-    left_world_x = data.left_hand_x
-    left_world_y = data.left_hand_y
-    left_world_z = data.left_hand_z
-    
     fig.add_trace(go.Scatter3d(
-        x=left_world_x, y=left_world_y, z=left_world_z,
-        mode='lines',
-        name='Left Hand',
-        line=dict(color='#dc2626', width=4)
+        x=data.left_hand_x, y=data.left_hand_y, z=data.left_hand_z,
+        mode='lines', name='Left Hand', line=dict(color='#dc2626', width=4)
     ))
     
-    # Right hand trajectory (green)
-    right_world_x = data.right_hand_x
-    right_world_y = data.right_hand_y
-    right_world_z = data.right_hand_z
-    
     fig.add_trace(go.Scatter3d(
-        x=right_world_x, y=right_world_y, z=right_world_z,
-        mode='lines',
-        name='Right Hand',
-        line=dict(color='#16a34a', width=4)
+        x=data.right_hand_x, y=data.right_hand_y, z=data.right_hand_z,
+        mode='lines', name='Right Hand', line=dict(color='#16a34a', width=4)
     ))
     
     fig.update_layout(
         title='3D Trajectory (World Frame)',
-        scene=dict(
-            xaxis_title='X (m)',
-            yaxis_title='Y (m)',
-            zaxis_title='Z (m)',
-            aspectmode='data',
-            bgcolor='#fafafa'
-        ),
-        height=700,
-        template="plotly_white"
+        scene=dict(xaxis_title='X', yaxis_title='Y', zaxis_title='Z', aspectmode='data'),
+        height=600
     )
     
     return fig
 
 
-def list_episodes() -> list:
-    """List all episodes in the dataset."""
-    try:
-        files = list_repo_files(DATASET_REPO, repo_type="dataset")
-        episodes = set()
-        for f in files:
-            parts = f.split('/')
-            if len(parts) > 1 and parts[0].startswith('episode'):
-                episodes.add(parts[0])
-        if not episodes:
-            # Try finding any folder
-            for f in files:
-                parts = f.split('/')
-                if len(parts) > 1:
-                    episodes.add(parts[0])
-        return sorted(list(episodes)) if episodes else ["No episodes found"]
-    except Exception as e:
-        return [f"Error listing: {str(e)}"]
-
-
-def load_and_visualize(episode_id: str):
-    """Load episode data and create visualizations."""
-    if not episode_id or episode_id.startswith("Error") or episode_id == "No episodes found":
-        empty_fig = go.Figure()
-        empty_fig.add_annotation(text="Select an episode to visualize", showarrow=False, font_size=20)
-        return empty_fig, empty_fig, "Select an episode from the dropdown"
-    
-    try:
-        episode_path = Path(f"/tmp/{episode_id}")
-        episode_path.mkdir(parents=True, exist_ok=True)
-        
-        # Try downloading metadata.json
+def visualize(source: str):
+    """Main visualization function."""
+    if source == "Sample Data":
+        data = create_sample_data()
+        info = "Using generated sample data (10 seconds, 300 frames)"
+    else:
         try:
-            hf_hub_download(
-                repo_id=DATASET_REPO,
-                filename=f"{episode_id}/extracted/metadata.json",
-                local_dir="/tmp",
-                repo_type="dataset"
-            )
-        except:
-            hf_hub_download(
-                repo_id=DATASET_REPO,
-                filename=f"{episode_id}/metadata.json",
-                local_dir="/tmp",
-                repo_type="dataset"
-            )
-        
-        # Try downloading hands_3d.json
-        try:
-            hf_hub_download(
-                repo_id=DATASET_REPO,
-                filename=f"{episode_id}/hands_3d.json",
-                local_dir="/tmp",
-                repo_type="dataset"
-            )
-        except:
-            pass
-        
-        # Try downloading end_effector.json
-        try:
-            hf_hub_download(
-                repo_id=DATASET_REPO,
-                filename=f"{episode_id}/end_effector.json",
-                local_dir="/tmp",
-                repo_type="dataset"
-            )
-        except:
-            pass
-        
-        # Load data
-        data = load_trajectory_data(Path(f"/tmp/{episode_id}"))
-        
-        # Create plots
-        trajectory_plot = create_trajectory_plots(data)
-        plot_3d = create_3d_trajectory_plot(data)
-        
-        # Stats
-        stats = f"""
-## Episode: {episode_id}
+            data = load_from_json(source)
+            info = f"Loaded from: {source}"
+        except Exception as e:
+            data = create_sample_data()
+            info = f"Error loading data: {e}. Using sample data."
+    
+    time_series = create_time_series_plot(data)
+    plot_3d = create_3d_plot(data)
+    
+    stats = f"""
+## Data Statistics
 
 | Metric | Value |
 |--------|-------|
-| Duration | {data.timestamps[-1]:.2f} seconds |
+| Duration | {data.timestamps[-1]:.2f} sec |
 | Frames | {len(data.timestamps)} |
-| Data streams | 57 total |
-| Visualized | 15 streams |
-| Stored (joints) | 42 streams |
-        """
-        
-        return trajectory_plot, plot_3d, stats
-        
-    except Exception as e:
-        empty_fig = go.Figure()
-        empty_fig.add_annotation(text=f"Error: {str(e)}", showarrow=False, font_size=14)
-        return empty_fig, empty_fig, f"**Error:** {str(e)}"
+| Visualized Streams | 15 |
+| Total Streams | 57 (including 42 joint positions) |
+
+**Info:** {info}
+    """
+    
+    return time_series, plot_3d, stats
 
 
-# Build Gradio interface
-with gr.Blocks(
-    title="DI Trajectory Visualizer",
-    theme=gr.themes.Soft(primary_hue="blue", secondary_hue="green")
-) as demo:
-    gr.Markdown("""
+# Gradio Interface
+if GRADIO_AVAILABLE:
+    with gr.Blocks(title="DI Trajectory Visualizer") as demo:
+        gr.Markdown("""
 # Dynamic Intelligence - Trajectory Visualizer
 
-Visualize humanoid robot training data: camera poses, hand positions, and orientations.
+Visualize 57 data streams from humanoid robot training data.
 
-### Data Streams (57 total)
-| Category | Streams | Description |
-|----------|---------|-------------|
-| Camera Position | 3 | X, Y, Z in world frame (meters) |
-| Left Hand Position | 3 | X, Y, Z in world frame (meters) |
-| Right Hand Position | 3 | X, Y, Z in world frame (meters) |
-| Left Hand Orientation | 3 | Roll, Pitch, Yaw (degrees) |
-| Right Hand Orientation | 3 | Roll, Pitch, Yaw (degrees) |
-| Hand Joints (stored) | 42 | 21 joints x 2 hands x XYZ |
-    """)
-    
-    with gr.Row():
-        episode_dropdown = gr.Dropdown(
-            label="Select Episode",
-            choices=list_episodes(),
-            interactive=True,
-            scale=3
+### Data Streams
+- **Camera**: X, Y, Z position (world frame)
+- **Left Hand**: X, Y, Z position + Roll, Pitch, Yaw
+- **Right Hand**: X, Y, Z position + Roll, Pitch, Yaw
+- **Joints**: 21 keypoints × 2 hands × XYZ (stored, not visualized)
+        """)
+        
+        with gr.Row():
+            source_input = gr.Dropdown(
+                choices=["Sample Data"],
+                value="Sample Data",
+                label="Data Source"
+            )
+            load_btn = gr.Button("Visualize", variant="primary")
+        
+        stats_output = gr.Markdown()
+        
+        with gr.Tabs():
+            with gr.TabItem("Time Series (15 plots)"):
+                time_plot = gr.Plot()
+            with gr.TabItem("3D View"):
+                plot_3d = gr.Plot()
+        
+        load_btn.click(
+            fn=visualize,
+            inputs=[source_input],
+            outputs=[time_plot, plot_3d, stats_output]
         )
-        load_btn = gr.Button("Load & Visualize", variant="primary", scale=1)
-    
-    stats_output = gr.Markdown()
-    
-    with gr.Tabs():
-        with gr.TabItem("Time Series (15 plots)"):
-            trajectory_plot = gr.Plot(label="Trajectory Data")
         
-        with gr.TabItem("3D View"):
-            plot_3d = gr.Plot(label="3D Trajectory")
-    
-    load_btn.click(
-        fn=load_and_visualize,
-        inputs=[episode_dropdown],
-        outputs=[trajectory_plot, plot_3d, stats_output]
-    )
-    
-    gr.Markdown("""
----
-**Data Source:** [DynamicIntelligence/humanoid-robots-training-dataset](https://huggingface.co/datasets/DynamicIntelligence/humanoid-robots-training-dataset)
-    """)
+        # Auto-load sample data
+        demo.load(
+            fn=lambda: visualize("Sample Data"),
+            outputs=[time_plot, plot_3d, stats_output]
+        )
 
-if __name__ == "__main__":
     demo.launch()
+else:
+    print("Gradio not available. Install with: pip install gradio")

requirements.txt

@@ -1,5 +1,3 @@
-gradio==4.44.0
 plotly>=5.18.0
 numpy>=1.24.0
-huggingface_hub==0.21.4
-pandas>=2.0.0
+gradio==4.31.0