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app.py

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+"""
+DI Annotation Data Visualizer - Simple working version
+"""
+import gradio as gr
+import pandas as pd
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+from huggingface_hub import hf_hub_download
+import json
+
+DATASET_REPO = "DynamicIntelligence/di-annotation-data"
+
+def load_data():
+    """Load trajectory data from HuggingFace."""
+    try:
+        # Try to load parquet
+        local_path = hf_hub_download(
+            repo_id=DATASET_REPO,
+            filename="data/chunk-000/episode_000000.parquet",
+            repo_type="dataset"
+        )
+        df = pd.read_parquet(local_path)
+        return df, None
+    except Exception as e:
+        # Fallback to old format
+        try:
+            local_path = hf_hub_download(
+                repo_id=DATASET_REPO,
+                filename="data/Test_Data_Lidar_trajectory.parquet",
+                repo_type="dataset"
+            )
+            df = pd.read_parquet(local_path)
+            return df, None
+        except Exception as e2:
+            return None, f"Error loading data: {e2}"
+
+def create_plots(df):
+    """Create visualization plots."""
+    if df is None:
+        return None, None
+    
+    # Check which columns exist
+    has_new_format = 'observation.state' in df.columns
+    
+    if has_new_format:
+        # New LeRobot format
+        obs = df['observation.state'].apply(lambda x: x if isinstance(x, list) else [0,0,0])
+        camera_x = [o[0] if len(o) > 0 else 0 for o in obs]
+        camera_y = [o[1] if len(o) > 1 else 0 for o in obs]
+        camera_z = [o[2] if len(o) > 2 else 0 for o in obs]
+        timestamps = df['timestamp'].tolist() if 'timestamp' in df.columns else list(range(len(df)))
+    else:
+        # Old format
+        camera_x = df['camera_x'].tolist() if 'camera_x' in df.columns else []
+        camera_y = df['camera_y'].tolist() if 'camera_y' in df.columns else []
+        camera_z = df['camera_z'].tolist() if 'camera_z' in df.columns else []
+        timestamps = df['timestamp'].tolist() if 'timestamp' in df.columns else list(range(len(df)))
+    
+    if not camera_x:
+        return None, None
+    
+    # 3D trajectory plot
+    fig_3d = go.Figure(data=[go.Scatter3d(
+        x=camera_x, y=camera_y, z=camera_z,
+        mode='lines+markers',
+        marker=dict(size=2, color=timestamps, colorscale='Viridis'),
+        line=dict(width=2, color='blue'),
+        name='Camera Path'
+    )])
+    fig_3d.update_layout(
+        title='Camera 3D Trajectory',
+        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, subplot_titles=['Camera X', 'Camera Y', 'Camera Z'])
+    fig_ts.add_trace(go.Scatter(x=timestamps, y=camera_x, name='X', line=dict(color='red')), row=1, col=1)
+    fig_ts.add_trace(go.Scatter(x=timestamps, y=camera_y, name='Y', line=dict(color='green')), row=2, col=1)
+    fig_ts.add_trace(go.Scatter(x=timestamps, y=camera_z, name='Z', line=dict(color='blue')), row=3, col=1)
+    fig_ts.update_layout(height=600, title='Camera Position vs Time', showlegend=False)
+    
+    return fig_3d, fig_ts
+
+def visualize():
+    """Main visualization function."""
+    df, error = load_data()
+    
+    if error:
+        return None, None, f"Error: {error}"
+    
+    if df is None or len(df) == 0:
+        return None, None, "No data found"
+    
+    fig_3d, fig_ts = create_plots(df)
+    
+    stats = f"""
+**Dataset Stats:**
+- Total frames: {len(df)}
+- Columns: {', '.join(df.columns[:5])}...
+- Source: {DATASET_REPO}
+    """
+    
+    return fig_3d, fig_ts, stats
+
+# Create Gradio interface
+with gr.Blocks(title="DI Annotation Data Visualizer", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# DI Annotation Data Visualizer")
+    gr.Markdown(f"Visualizing trajectory data from [{DATASET_REPO}](https://huggingface.co/datasets/{DATASET_REPO})")
+    
+    load_btn = gr.Button("Load & Visualize Data", variant="primary")
+    
+    with gr.Row():
+        plot_3d = gr.Plot(label="3D Trajectory")
+        plot_ts = gr.Plot(label="Time Series")
+    
+    stats_output = gr.Markdown()
+    
+    load_btn.click(
+        fn=visualize,
+        inputs=[],
+        outputs=[plot_3d, plot_ts, stats_output]
+    )
+    
+    # Auto-load on start
+    demo.load(visualize, inputs=[], outputs=[plot_3d, plot_ts, stats_output])
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)