Update app.py

app.py

@@ -28,7 +28,7 @@ if f is not None:
     # Initialize a counter for frames
     frame_count = 0
     model = tf.keras.models.load_model('HandSignClassifier (1).h5')
-    array = ['a','b','c','d','e','f','g','h','i','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y']
+    array = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z','del','nothing','space']
     out = ''
     
     while True:
@@ -42,9 +42,8 @@ if f is not None:
 
         # Check if it's time to capture a frame
         if frame_count % interval == 0:
-            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # Convert to grayscale
-            frame = cv2.resize(frame, (28, 28))  # Resize to (28, 28)
-            frame = np.reshape(frame, (1, 28, 28, 1))
+            frame = cv2.resize(frame, (256, 256))  # Resize to (28, 28)
+            frame = np.reshape(frame, (1, 256, 256, 3))
             st.image(frame, 'input')# Reshape
             pred = model.predict(frame) 
             st.write(pred)