Delete app.py with huggingface_hub

app.py

@@ -1,133 +0,0 @@
-"""
-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)