Upload app.py

app.py

@@ -1,8 +1,6 @@
 import os
 
-import cv2
 import mediapipe as mp
-import numpy as np
 import tensorflow as tf
 
 N_ROWS = 543
@@ -13,7 +11,6 @@ NUM_CLASSES = 250
 INPUT_SIZE = 32
 
 
-
 # Tensorflow layer to process data in TFLite
 # Data needs to be processed in the model itself, so we cannot use Python
 class PreprocessLayer(tf.keras.layers.Layer):
@@ -117,7 +114,6 @@ interpreter.allocate_tensors()
 input_details = interpreter.get_input_details()
 output_details = interpreter.get_output_details()
 
-
 index_to_class = {
     "TV": 0, "after": 1, "airplane": 2, "all": 3, "alligator": 4, "animal": 5, "another": 6, "any": 7, "apple": 8,
     "arm": 9, "aunt": 10, "awake": 11, "backyard": 12, "bad": 13, "balloon": 14, "bath": 15, "because": 16, "bed": 17,
@@ -199,9 +195,7 @@ with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=
     import cv2
     import numpy as np
     import gradio as gr
-    from tensorflow import lite as tflite
     import tensorflow as tf
-    import mediapipe as mp
 
 
     # ... (Previous code remains the same)
@@ -235,27 +229,6 @@ with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=
 
     # Set mediapipe model
     cap = cv2.VideoCapture(0)
-    while cap.isOpened():
-        # Read feed
-        ret, frame = cap.read()
-        if not ret:
-            print("Failed to capture frame from the webcam.")
-            break
-
-        try:
-            # Make predictions
-            prediction = predict_with_webcam(frame)
-
-            # Display the frame with the prediction
-            cv2.putText(frame, prediction, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-            cv2.imshow('Webcam Landmark Prediction', frame)
-
-        except Exception as e:
-            print("Error occurred:", e)
-
-        # Exit the loop when 'q' key is pressed
-        if cv2.waitKey(1) & 0xFF == ord('q'):
-            break
 
     cap.release()
     cv2.destroyAllWindows()