Update app.py

app.py

@@ -50,6 +50,15 @@ CLIENT = InferenceHTTPClient(
 PLAYER_DETECTION_MODEL_ID = "football-players-detection-3zvbc/11"
 FIELD_DETECTION_MODEL_ID = "football-field-detection-f07vi/14"
 
+def infer_with_confidence(model_id: str, frame: np.ndarray, confidence_threshold: float = 0.3):
+    """Run inference and filter by confidence threshold"""
+    result = CLIENT.infer(frame, model_id=model_id)
+    detections = sv.Detections.from_inference(result)
+    # Filter by confidence
+    if len(detections) > 0:
+        detections = detections[detections.confidence > confidence_threshold]
+    return result, detections
+
 # ==============================================
 # SIGLIP MODEL (Embeddings)
 # ==============================================
@@ -535,8 +544,7 @@ def analyze_football_video(video_path: str, progress=gr.Progress()) -> Tuple:
                 break
 
             if frame_count % STRIDE == 0:
-                result = CLIENT.infer(frame, model_id=PLAYER_DETECTION_MODEL_ID)
-                detections = sv.Detections.from_inference(result)
+                _, detections = infer_with_confidence(PLAYER_DETECTION_MODEL_ID, frame, 0.3)
                 detections = detections.with_nms(threshold=0.5, class_agnostic=True)
                 players_detections = detections[detections.class_id == PLAYER_ID]
 
@@ -587,8 +595,7 @@ def analyze_football_video(video_path: str, progress=gr.Progress()) -> Tuple:
                         desc=f"🎬 Processing frame {frame_count}/{total_frames}")
 
             # Player and ball detection
-            result = CLIENT.infer(frame, model_id=PLAYER_DETECTION_MODEL_ID, confidence=0.3)
-            detections = sv.Detections.from_inference(result)
+            _, detections = infer_with_confidence(PLAYER_DETECTION_MODEL_ID, frame, 0.3)
 
             if len(detections.xyxy) == 0:
                 out.write(frame)
@@ -653,7 +660,7 @@ def analyze_football_video(video_path: str, progress=gr.Progress()) -> Tuple:
             # STEP 4: Field Detection & Transformation
             # ========================================
             try:
-                result_field = CLIENT.infer(frame, model_id=FIELD_DETECTION_MODEL_ID, confidence=0.3)
+                result_field, _ = infer_with_confidence(FIELD_DETECTION_MODEL_ID, frame, 0.3)
                 key_points = sv.KeyPoints.from_inference(result_field)
                 
                 # Filter confident keypoints