app.py

import cv2
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np 
import tensorflow as tf
import streamlit as st 
import tempfile
model = load_model('HandSignClassifierGUD.h5')
# Open the video file
f = st.file_uploader("Choose a Video")
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']
    # Read the video file from the file-like object
if f is not None:
    img = Image.open(f)
    img = img.resize((28,28))
    img = img.convert('L')
    img = np.reshape(img,(1,28,28,1))
    pred = model.predict(img)
    st.image(img,use_column_width=True)
    st.write(array[np.argmax(pred)])