Surface rep metrics in the app

README.md

@@ -168,18 +168,18 @@ Each step lives in `src/pozify/steps/` and exposes a `run(...)` function.
 
 ## Adding Exercises
 
-Supported exercise metadata is centralized in `src/pozify/exercise_catalog.py`. To add a new mocked
+Supported exercise metadata is centralized in `src/pozify/exercise_catalog.py`. To add a new
 exercise, add one `ExerciseSpec` with:
 
 - `key` and `display_name`
 - `default_variation` and confidence
-- `metric_factory` for mock per-rep metrics
+- `metric_factory` for legacy/mock fallback metadata
 - optional `variation_hints`, default `not_issues`, and `mock_issue`
 
 The Gradio dropdown, profile validation, variation detector, rep analysis, and issue marker read from
 this catalog, so new exercises do not require changing each step just to become selectable. Real
-exercise-specific algorithms can still replace individual step implementations later while preserving
-the same pipeline contracts.
+exercise-specific rep metrics live in `src/pozify/steps/rep_analysis.py`; variation rules live in
+`src/pozify/steps/variation_detector.py`.
 
 Recommended replacement order:
 
@@ -187,7 +187,7 @@ Recommended replacement order:
 2. `pose_backends/`: add or refine pose model adapters such as MediaPipe or MMPose.
 3. `exercise_classifier.py`: load the exercise router model.
 4. `rep_counter.py`: implement exercise-specific state machines.
-5. `rep_analysis.py` and `issue_marker.py`: compute real metrics and issue scores.
+5. `issue_marker.py`: compute real issue scores and intervals from rep metrics.
 6. `annotated_renderer.py`: render skeleton overlays and issue highlights.
 7. `coach_summary.py`: call the selected small language model with retrieved knowledge cards.
 

app.py

@@ -54,11 +54,33 @@ def _mock_status_markdown(report: dict[str, Any]) -> str:
     steps = ", ".join(f"`{step}`" for step in mock_steps)
     return (
         "## Pipeline Status\n\n"
-        "The current run uses real video QC, pose extraction, rep segmentation, and annotated video rendering, "
+        "The current run uses real video QC, pose extraction, rep segmentation, "
+        "rep analysis, variation detection, and annotated video rendering, "
         f"but these steps still use placeholders: {steps}."
     )
 
 
+def _metrics_markdown(report: dict[str, Any]) -> str:
+    metrics = report["rep_analysis"]["aggregate_metrics"]
+    lines = [
+        "## Movement Metrics",
+        "",
+        f"- **Average ROM score:** {metrics.get('avg_rom_score', 0):.0%}",
+        f"- **Average stability score:** {metrics.get('avg_stability_score', 0):.0%}",
+        f"- **Average symmetry score:** {metrics.get('avg_symmetry_score', 0):.0%}",
+        f"- **Average rep duration:** {metrics.get('avg_rep_duration_sec', 0)}s",
+        f"- **Tempo consistency:** {metrics.get('avg_tempo_consistency_score', 0):.0%}",
+        f"- **ROM fatigue trend:** {metrics.get('fatigue_trend_rom_delta', 0):+.2f}",
+    ]
+    if "avg_hand_width_ratio" in metrics:
+        lines.append(f"- **Hand width ratio:** {metrics['avg_hand_width_ratio']:.2f}")
+    if "avg_stance_width_ratio" in metrics:
+        lines.append(f"- **Stance width ratio:** {metrics['avg_stance_width_ratio']:.2f}")
+    if "avg_lockout_quality" in metrics:
+        lines.append(f"- **Lockout quality:** {metrics['avg_lockout_quality']:.0%}")
+    return "\n".join(lines)
+
+
 def analyze_video(
     video_path: str | None,
     goal: str,
@@ -83,7 +105,18 @@ def analyze_video(
     report = result["final_report"]
     video_quality = _quality_markdown(report["video_manifest"])
     mock_status = _mock_status_markdown(report)
+    movement_metrics = _metrics_markdown(report)
     summary = report["coach_summary"]
+    exercise = report["exercise"]
+    variation = report["variation"]
+    exercise_line = (
+        f'{exercise["exercise"]} (mock confidence placeholder: {exercise["confidence"]:.0%})'
+    )
+    variation_line = (
+        f'{variation["detected_variation"]} '
+        f'(confidence: {variation["variation_confidence"]:.0%})'
+    )
+    finding = summary["main_findings"][0] if summary["main_findings"] else "No mock finding emitted"
     if not report["video_manifest"]["analysis_allowed"]:
         markdown = f"""{video_quality}
 
@@ -104,14 +137,16 @@ def analyze_video(
 
     markdown = f"""## Scan Summary
 
-- **Exercise router output:** {report["exercise"]["exercise"]} (mock confidence placeholder: {report["exercise"]["confidence"]:.0%})
-- **Variation label:** {report["variation"]["detected_variation"]} (mock confidence placeholder: {report["variation"]["variation_confidence"]:.0%})
+- **Exercise router output:** {exercise_line}
+- **Variation label:** {variation_line}
 - **Reps:** {len(report["reps"]["reps"])}
 - **Analysis mode:** {report["artifacts"].get("analysis_mode", "unknown")}
 - **Pose source:** {report["artifacts"].get("pose_source", "unknown")}
-- **Mock finding:** {summary["main_findings"][0] if summary["main_findings"] else "No mock finding emitted"}
+- **Mock finding:** {finding}
 - **Run ID:** `{report["run_id"]}`
 
+{movement_metrics}
+
 {mock_status}
 
 ## Coach Notes

src/pozify/pipeline.py

@@ -96,8 +96,6 @@ def run_pipeline(
     analysis_mode = "mock" if mock_mode else "real"
     mock_steps = [
         "exercise_classifier",
-        "rep_analysis",
-        "variation_detector",
         "issue_marker",
         "coach_summary",
         "verifier",