Add new API endpoint for vector lookups

app.py

@@ -28,7 +28,19 @@ with pyzipper.AESZipFile('persons.zip') as zf:
     PERFORMER_DB = json.loads(zf.read('performers.json'))
 
 
-def predict(image, threshold=20.0, results=3):
+## Prediction functions
+
+
+def image_search_performer(image, threshold=20.0, results=3):
+    """Search for a performer in an image
+
+    Returns a list of performers with at least following keys:
+    - id: the performer's id
+    - distance: the distance between the face in the image and the performer's face
+    - confidence: a confidence score between 0 and 100
+    - hits: the number of times the performer was found in our database
+    """
+    
     image_array = np.array(image)
 
     img = functions.preprocess_face(
@@ -41,8 +53,36 @@ def predict(image, threshold=20.0, results=3):
 
     img = functions.normalize_input(img, normalization="Facenet2018")
     face = model.predict(img)[0]
+    return search_performer(face, threshold, results)
+
+
+def vector_search_performer(vector_json, threshold=20.0, results=3):
+    """Search for a performer from a vector
+
+    The vector should be created with Deepface and should be a 512 vector.
+
+    For best results use the following settings:
+    - detector_backend: retinaface
+    - model: Facenet512
+    - normalization: Facenet2018
+
+    Returns a list of performers with at least following keys:
+    - id: the performer's id
+    - distance: the distance between the face in the image and the performer's face
+    - confidence: a confidence score between 0 and 100
+    - hits: the number of times the performer was found in our database
+    """
+
+    vector = np.array(json.loads(vector_json))
+    return search_performer(vector, threshold, results)
+    
+
+def search_performer(vector, threshold=20.0, results=3):
+    threshold = threshold or 20.0
+    results = results or 3
+    
     ids, distances = index.get_nns_by_vector(
-        face, 50, search_k=10000, include_distances=True
+        vector, 50, search_k=10000, include_distances=True
     )
     persons = {}
     for p, distance in zip(ids, distances):
@@ -91,8 +131,8 @@ def face_distance_to_conf(face_distance, face_match_threshold=20.0):
         return linear_val + ((1.0 - linear_val) * math.pow((linear_val - 0.5) * 2, 0.2))
 
 
-gr.Interface(
-    fn=predict,
+image_search = gr.Interface(
+    fn=image_search_performer,
     inputs=[
         gr.components.Image(),
         gr.components.Slider(label="threshold",minimum=0.0, maximum=30.0, value=20.0),
@@ -101,4 +141,18 @@ gr.Interface(
     outputs=gr.outputs.JSON(label=""),
     title="Who is in the photo?",
     description="Upload an image of a person and we'll tell you who it is.",
-).launch(enable_queue=True, server_name="0.0.0.0")
+)
+
+vector_search = gr.Interface(
+    fn=vector_search_performer,
+    inputs=[
+        gr.components.Textbox(),
+        gr.components.Slider(label="threshold",minimum=0.0, maximum=30.0, value=20.0),
+        gr.components.Slider(label="results", minimum=0, maximum=50, value=3, step=1),
+    ],
+    outputs=gr.outputs.JSON(label=""),
+    title="Who is in the photo?",
+    description="512 vector created with deepface of a person and we'll tell you who it is.",
+)
+
+gr.TabbedInterface([image_search, vector_search]).launch(enable_queue=True, server_name="0.0.0.0")