app.py

import cv2
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
import streamlit as st

# Open the video file
video_file = st.file_uploader("Choose a video file", type=["mp4"])

if video_file is not None:
    # Read the video file from the file-like object
    video_path = video_file.name
    cap = cv2.VideoCapture(video_path)

    # 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 = load_model('HandSignClassifier.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 frame
        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))  # Reshape
            pred = model.predict(frame)
            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()

    print(out)