Delete app.py with huggingface_hub

app.py

@@ -1,201 +0,0 @@
-#!/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(server_name="0.0.0.0", server_port=7860)