trackio

app/main.py

@@ -92,6 +92,22 @@ def main():
                                                  sample_rate=sample_rate)
             print(f"Annotated video saved to: {output_path}")
 
+        # Log swing analysis to Trackio
+        try:
+            import trackio
+            trackio.init(project="par-ity-golf-swing", config={
+                "input_method": "cli",
+                "sample_rate": sample_rate
+            })
+            trackio.log({
+                "swings_analyzed": 1,
+                "frames_processed": len(frames) if frames is not None else 0,
+                "num_detections": len(detections) if detections is not None else 0
+            })
+            trackio.finish()
+        except Exception as _trackio_err:
+            print(f"Trackio logging skipped: {_trackio_err}")
+
         print("\nAnalysis complete!")
 
     except Exception as e:

app/streamlit_app.py

@@ -8,7 +8,6 @@ import tempfile
 import streamlit as st
 from dotenv import load_dotenv
 import base64
-import threading
 from pathlib import Path
 import shutil
 import cv2
@@ -28,7 +27,6 @@ from models.swing_analyzer import segment_swing, analyze_trajectory
 from models.llm_analyzer import generate_swing_analysis, create_llm_prompt, prepare_data_for_llm, check_llm_services, parse_and_format_analysis, display_formatted_analysis
 from utils.visualizer import create_annotated_video
 from utils.comparison import create_key_frame_comparison, extract_key_swing_frames
-from utils.stats_publisher import record_swing_event
 
 # Import RAG functionality
 print("=== RAG Import Debug Information ===")
@@ -765,27 +763,21 @@ def main():
                 'prompt': prompt
             }
 
-            # Fire-and-forget stats publish to Hugging Face Space branch
+            # Track swing analysis with Trackio (one run per analysis)
             try:
-                llm_status = {
-                    'ollama_available': llm_services.get('ollama', {}).get('available', False),
-                    'openai_available': llm_services.get('openai', {}).get('available', False),
-                }
-                stats_payload = {
-                    'event_type': 'swing_analyzed',
-                    'source': 'youtube' if input_option == 'YouTube URL' else 'upload',
-                    'video_name': os.path.basename(video_path) if video_path else None,
-                    'sample_rate': sample_rate,
-                    'frame_count': len(frames) if frames is not None else None,
-                    'llm_status': llm_status,
-                    'pro_comparison_enabled': False,
-                    'app_version': os.getenv('APP_VERSION', 'unknown'),
-                    'space_id': os.getenv('SPACE_ID'),
-                }
-                print(f"Stats payload prepared: {stats_payload}")
-                threading.Thread(target=record_swing_event, args=(stats_payload,), daemon=True).start()
-            except Exception as stats_err:
-                print(f"Stats publish error: {stats_err}")
+                import trackio
+                trackio.init(project="par-ity-golf-swing", config={
+                    "input_method": input_option,
+                    "sample_rate": sample_rate
+                })
+                trackio.log({
+                    "swings_analyzed": 1,
+                    "frames_processed": len(frames) if frames is not None else 0,
+                    "num_detections": len(detections) if detections is not None else 0
+                })
+                trackio.finish()
+            except Exception as _trackio_err:
+                print(f"Trackio logging skipped: {_trackio_err}")
 
             # Keep the original video file for potential annotation
             # Video will be cleaned up when user uploads a new video or session ends

requirements.txt

@@ -17,4 +17,4 @@ scikit-learn==1.3.2
 plotly==5.17.0
 tiktoken==0.5.2
 transformers>=4.20.0 
-huggingface_hub>=0.23.0
+trackio