diff --git a/models/ai_models/computer_vision.py b/models/ai_models/computer_vision.py
new file mode 100644
index 0000000..3074bc6
--- /dev/null
+++ b/models/ai_models/computer_vision.py
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+# Import necessary libraries
+import cv2
+import numpy as np
+from sklearn.svm import SVC
+
+# Define a class for computer vision models
+class ComputerVision:
+    def __init__(self, model_type):
+        self.model_type = model_type
+        self.model = self._create_model()
+
+    def _create_model(self):
+        # Create a computer vision model using OpenCV and scikit-learn
+        if self.model_type == 'face_detection':
+            return cv2.dnn.readNetFromCaffe('face_detection_model.prototxt', 'face_detection_model.caffemodel')
+        elif self.model_type == 'object_detection':
+            return cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights')
+        elif self.model_type == 'image_classification':
+            return SVC(kernel='linear', probability=True)
+
+    def detect_faces(self, image):
+        # Detect faces in an image using the face detection model
+        blob = cv2.dnn.blobFromImage(image, 1, (300, 300), (104, 177, 123))
+        self.model.setInput(blob)
+        detections = self.model.forward()
+        faces = []
+        for i in range(detections.shape[2]):
+            confidence = detections[0, 0, i, 2]
+            if confidence > 0.5:
+                x1 = int(detections[0, 0, i, 3] * image.shape[1])
+                y1 = int(detections[0, 0, i, 4] * image.shape[0])
+                x2 = int(detections[0, 0, i, 5] * image.shape[1])
+                y2 = int(detections[0, 0, i, 6] * image.shape[0])
+                faces.append((x1, y1, x2, y2))
+        return faces
+
+    def detect_objects(self, image):
+        # Detect objects in an image using the object detection model
+        blob = cv2.dnn.blobFromImage(image, 1/255, (416, 416), [0,0,0], 1, crop=False)
+        self.model.setInput(blob)
+        outputs = self.model.forward(self.model.getUnconnectedOutLayersNames())
+        objects = []
+        for output in outputs:
+            for detection in output:
+                scores = detection[5:]
+                class_id = np.argmax(scores)
+                confidence = scores[class_id]
+                if confidence > 0.5:
+                    x1 = int(detection[0] * image.shape[1])
+                    y1 = int(detection[1] * image.shape[0])
+                    x2 = int(detection[2] * image.shape[1])
+                    y2 = int(detection[3] * image.shape[0])
+                    objects.append((x1, y1,x2, y2, class_id, confidence))
+        return objects
+
+    def classify_image(self, image):
+        # Classify an image using the image classification model
+        # Implement feature extraction and classification logic here
+        pass
+
+# Define a function to load a computer vision model
+def load_computer_vision(model_path):
+    # Load the computer vision model from a file
+    model = ComputerVision('face_detection')
+    model.model = cv2.dnn.readNetFromCaffe(model_path)
+    return model