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app.py

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+#!/usr/bin/env python3
+"""
+DI Annotation Data Visualizer
+Visualizes data from: DynamicIntelligence/di-annotation-data
+"""
+
+import gradio as gr
+from huggingface_hub import hf_hub_download, list_repo_files, HfApi
+import json
+import numpy as np
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+from pathlib import Path
+import pandas as pd
+
+# YOUR dataset - not the old one
+DATASET_REPO = "DynamicIntelligence/di-annotation-data"
+
+def list_episodes():
+    """List all episodes in the dataset."""
+    try:
+        api = HfApi()
+        files = list(api.list_repo_files(repo_id=DATASET_REPO, repo_type="dataset"))
+        
+        # Find episodes
+        episodes = set()
+        for f in files:
+            if f.startswith("episodes/") and f.endswith("/trajectory.json"):
+                parts = f.split("/")
+                if len(parts) >= 2:
+                    episodes.add(parts[1])
+            elif f.startswith("episodes/") and "/" in f:
+                parts = f.split("/")
+                if len(parts) >= 2 and parts[1]:
+                    episodes.add(parts[1])
+        
+        return sorted(list(episodes)) if episodes else ["No episodes found"]
+    except Exception as e:
+        return [f"Error: {str(e)}"]
+
+
+def load_episode_data(episode_id: str):
+    """Load trajectory data for an episode."""
+    try:
+        # Download trajectory.json
+        local_path = hf_hub_download(
+            repo_id=DATASET_REPO,
+            filename=f"episodes/{episode_id}/trajectory.json",
+            repo_type="dataset"
+        )
+        
+        with open(local_path) as f:
+            data = json.load(f)
+        
+        return data, None
+    except Exception as e:
+        return None, str(e)
+
+
+def create_plots(data: dict):
+    """Create trajectory plots."""
+    
+    camera = data.get("camera", {})
+    x = camera.get("x", [])
+    y = camera.get("y", [])
+    z = camera.get("z", [])
+    timestamps = data.get("timestamps", list(range(len(x))))
+    
+    if not x:
+        empty = go.Figure()
+        empty.add_annotation(text="No trajectory data", showarrow=False, font_size=20)
+        return empty, empty
+    
+    # 3D trajectory
+    fig_3d = go.Figure()
+    fig_3d.add_trace(go.Scatter3d(
+        x=x, y=y, z=z,
+        mode='lines',
+        line=dict(color='blue', width=4),
+        name='Camera'
+    ))
+    fig_3d.add_trace(go.Scatter3d(
+        x=[x[0]], y=[y[0]], z=[z[0]],
+        mode='markers',
+        marker=dict(color='green', size=10),
+        name='Start'
+    ))
+    fig_3d.add_trace(go.Scatter3d(
+        x=[x[-1]], y=[y[-1]], z=[z[-1]],
+        mode='markers',
+        marker=dict(color='red', size=10),
+        name='End'
+    ))
+    fig_3d.update_layout(
+        title="Camera Trajectory (World Frame)",
+        scene=dict(xaxis_title='X (m)', yaxis_title='Y (m)', zaxis_title='Z (m)'),
+        height=500
+    )
+    
+    # Time series
+    fig_ts = make_subplots(rows=3, cols=1, shared_xaxes=True,
+                          subplot_titles=['Camera X', 'Camera Y', 'Camera Z'])
+    
+    fig_ts.add_trace(go.Scatter(x=timestamps, y=x, name='X', line=dict(color='red')), row=1, col=1)
+    fig_ts.add_trace(go.Scatter(x=timestamps, y=y, name='Y', line=dict(color='green')), row=2, col=1)
+    fig_ts.add_trace(go.Scatter(x=timestamps, y=z, name='Z', line=dict(color='blue')), row=3, col=1)
+    
+    fig_ts.update_layout(height=500, title="Position vs Time", showlegend=True)
+    fig_ts.update_xaxes(title_text="Time (s)", row=3, col=1)
+    
+    return fig_3d, fig_ts
+
+
+def load_and_visualize(episode_id: str):
+    """Main function to load and visualize episode."""
+    
+    if not episode_id or episode_id.startswith("Error") or episode_id == "No episodes found":
+        empty = go.Figure()
+        empty.add_annotation(text="Select an episode to visualize", showarrow=False, font_size=20)
+        return empty, empty, "No episode selected"
+    
+    data, error = load_episode_data(episode_id)
+    
+    if error:
+        empty = go.Figure()
+        empty.add_annotation(text=f"Error: {error}", showarrow=False, font_size=14)
+        return empty, empty, f"**Error:** {error}"
+    
+    fig_3d, fig_ts = create_plots(data)
+    
+    # Stats
+    camera = data.get("camera", {})
+    annotations = data.get("annotations", [])
+    
+    stats = f"""
+## Episode: {episode_id}
+
+| Property | Value |
+|----------|-------|
+| Task | {data.get('task', 'Unknown')} |
+| Language Instruction | {data.get('language_instruction', data.get('task', 'N/A'))} |
+| Frames | {data.get('num_frames', len(camera.get('x', [])))} |
+| FPS | {data.get('fps', 30)} |
+| Frame Range | {data.get('frame_range', {}).get('start', 0)} → {data.get('frame_range', {}).get('end', 'N/A')} |
+| Annotations | {len(annotations)} segments |
+"""
+    
+    return fig_3d, fig_ts, stats
+
+
+# Build Gradio interface
+with gr.Blocks(
+    title="DI Annotation Visualizer",
+    theme=gr.themes.Soft(primary_hue="blue")
+) as demo:
+    gr.Markdown(f"""
+# DI Annotation Data Visualizer
+
+Visualizing data from: [{DATASET_REPO}](https://huggingface.co/datasets/{DATASET_REPO})
+
+This shows **YOUR** annotated episodes, not the old training dataset.
+    """)
+    
+    with gr.Row():
+        episode_dropdown = gr.Dropdown(
+            label="Select Episode",
+            choices=list_episodes(),
+            interactive=True,
+            scale=3
+        )
+        refresh_btn = gr.Button("🔄 Refresh", scale=1)
+        load_btn = gr.Button("📊 Load & Visualize", variant="primary", scale=1)
+    
+    stats_output = gr.Markdown()
+    
+    with gr.Tabs():
+        with gr.TabItem("🌐 3D Trajectory"):
+            plot_3d = gr.Plot(label="3D Trajectory")
+        
+        with gr.TabItem("📈 Time Series"):
+            plot_ts = gr.Plot(label="Position vs Time")
+    
+    # Events
+    load_btn.click(
+        fn=load_and_visualize,
+        inputs=[episode_dropdown],
+        outputs=[plot_3d, plot_ts, stats_output]
+    )
+    
+    refresh_btn.click(
+        fn=lambda: gr.Dropdown(choices=list_episodes()),
+        outputs=[episode_dropdown]
+    )
+    
+    gr.Markdown(f"""
+---
+**Dataset:** [{DATASET_REPO}](https://huggingface.co/datasets/{DATASET_REPO})
+    """)
+
+if __name__ == "__main__":
+    demo.launch()