Remove onnx, again

app.py

@@ -7,8 +7,7 @@ from torchvision.models.detection import FasterRCNN_ResNet50_FPN_Weights
 from PIL import Image
 import mediapipe as mp
 from fer import FER  # Facial emotion recognition
-from transformers import AutoFeatureExtractor, AutoModel  # (Unused now for facial recognition)
-import onnxruntime as rt  # New import for ONNX Runtime
+# from transformers import AutoFeatureExtractor, AutoModel  # (Unused now for facial recognition)
 
 # -----------------------------
 # Configuration
@@ -49,12 +48,6 @@ emotion_detector = FER(mtcnn=True)
 # Retrieve object categories from model weights metadata
 object_categories = FasterRCNN_ResNet50_FPN_Weights.DEFAULT.meta["categories"]
 
-# -----------------------------
-# Facial Recognition Model (Marltgap/FaceTransformerOctupletLoss ONNX)
-# (No longer used in the UI; kept here for reference)
-# -----------------------------
-facial_recognition_onnx = rt.InferenceSession("FaceTransformerOctupletLoss.onnx", providers=rt.get_available_providers())
-
 # -----------------------------
 # Overlay Drawing Functions
 # -----------------------------
@@ -151,7 +144,7 @@ def compute_faces_overlay(image):
     return boxes, text
 
 # -----------------------------
-# New Facemesh Functions
+# New Facemesh Functions (for Facial Recognition)
 # -----------------------------
 def compute_facemesh_overlay(image):
     """
@@ -180,39 +173,6 @@ def analyze_facemesh(image):
     annotated_image, text = compute_facemesh_overlay(image)
     return annotated_image, f"<div style='color: lime !important;'>Facemesh Analysis: {text}</div>"
 
-# -----------------------------
-# (Retained) Facial Recognition Function (Not used in UI anymore)
-# -----------------------------
-def compute_facial_recognition_vector(image):
-    """
-    Detects a face using MediaPipe, crops and resizes it to 112x112, then computes its embedding
-    vector using the Marltgap FaceTransformerOctupletLoss ONNX model.
-    """
-    frame_bgr = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
-    frame_bgr_small = cv2.resize(frame_bgr, DESIRED_SIZE)
-    frame_rgb_small = cv2.cvtColor(frame_bgr_small, cv2.COLOR_BGR2RGB)
-    face_results = face_detection.process(frame_rgb_small)
-    if face_results.detections:
-        detection = face_results.detections[0]
-        bbox = detection.location_data.relative_bounding_box
-        h, w, _ = frame_rgb_small.shape
-        x = int(bbox.xmin * w)
-        y = int(bbox.ymin * h)
-        box_w = int(bbox.width * w)
-        box_h = int(bbox.height * h)
-        face_crop = frame_rgb_small[y:y+box_h, x:x+box_w]
-        # Resize the face crop to the required dimensions: 112x112
-        face_crop_resized = cv2.resize(face_crop, (112, 112))
-        # Convert image to float32 (values between 0 and 255)
-        input_image = face_crop_resized.astype(np.float32)
-        # Run inference using the ONNX model
-        outputs = facial_recognition_onnx.run(None, {"input_image": input_image})
-        embedding = outputs[0][0]  # Assuming the output shape is (1, 512)
-        vector_str = np.array2string(embedding, precision=2, separator=',')
-        return face_crop, vector_str
-    else:
-        return np.array(image), "No face detected"
-
 # -----------------------------
 # Main Analysis Functions for Single Image
 # -----------------------------
@@ -266,12 +226,6 @@ def analyze_faces_current(image):
         output = draw_boxes_overlay(output, faces_cache["boxes"], (0, 0, 255))
     return output, f"<div style='color: lime !important;'>Face Detection: {faces_cache['text']}</div>"
 
-# (The old facial recognition analysis function is retained below but not linked to any UI tab)
-def analyze_facial_recognition(image):
-    # Compute and return the facial vector (and the cropped face)
-    face_crop, vector_str = compute_facial_recognition_vector(image)
-    return face_crop, f"<div style='color: lime !important;'>Facial Vector: {vector_str}</div>"
-
 def analyze_all(image):
     current_frame = np.array(image).copy()
     landmarks, posture_text = compute_posture_overlay(image)
@@ -369,7 +323,7 @@ faces_interface = gr.Interface(
 )
 
 # -----------------------------
-# New Facemesh Interface (Replaces the old Facial Recognition tab)
+# New Facemesh Interface (Used for facial recognition via facemesh)
 # -----------------------------
 facemesh_interface = gr.Interface(
     fn=analyze_facemesh,