Deploying Gradio face recognition app

app.py

@@ -10,6 +10,7 @@ def load_trained_model():
         data = pickle.load(f)
     return data["encodings"], data["names"]
 
+# Face recognition function
 def recognize_face(frame):
     encodings, names = load_trained_model()
     rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
@@ -31,9 +32,43 @@ def recognize_face(frame):
 
     return frame
 
+# Process video frame-by-frame
+def process_video(video):
+    cap = cv2.VideoCapture(video)
+    frames = []
+    
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        frame = recognize_face(frame)
+        frames.append(frame)
+
+    cap.release()
+    return frames
+
+# Live webcam feed processing
+def process_webcam():
+    cap = cv2.VideoCapture(0)
+    
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        frame = recognize_face(frame)
+        yield frame  # Stream processed frames
+
+    cap.release()
+
 # Gradio Interface
-def process_image(image):
-    return recognize_face(image)
+iface = gr.Interface(
+    fn={"Upload Video": process_video, "Live Webcam": process_webcam},
+    inputs=gr.Video(source="upload", format="mp4", optional=True),
+    outputs=gr.Video(),
+    title="Face Identification System",
+    description="Upload a video or use a live webcam for real-time face recognition.",
+    live=True
+)
 
-iface = gr.Interface(fn=process_image, inputs="image", outputs="image", title="Face Identification System")
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()