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

# Open the video file
f = st.file_uploader("Choose a Video")

if f is not None:
    # Read the video file from the file-like object
          
    tfile = tempfile.NamedTemporaryFile(delete=False) 
    tfile.write(f.read())

# Opens the Video file
    cap= cv2.VideoCapture(tfile.name)

    # Get the frames per second (fps) of the video
    fps = (cap.get(cv2.CAP_PROP_FPS)) 
    st.write(fps)

    # Calculate the interval to capture one frame per second
    interval = int(round(fps/1))

    # 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']
    out = ''
    
    while True:
        # Read the next fram
        
        ret, frame = cap.read()

        # Break the loop if the video is over
        if not ret:
            break

        # 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))
            st.image(frame, 'input')# Reshape
            pred = model.predict(frame) 
            st.write(pred)
            pred = np.argmax(pred)
            pred = array[pred]
            if not out or out[-1] != pred:
                out = out + pred

        # Increment the frame counter
        frame_count += 1

    # Release the video capture object
    cap.release()

    st.write(out)