Create app.py

app.py

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+import cv2
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+import streamlit as st
+
+# Replace 'your_video.mp4' with the path to your video file
+# Open the video file
+video_path = st.file_uploader("Choose a video file", type=["mp4"])
+video_path = st.video(video_path)
+
+cap = cv2.VideoCapture(video_path)
+
+# Get the frames per second (fps) of the video
+fps = cap.get(cv2.CAP_PROP_FPS)
+
+# Calculate the interval to capture one frame per second
+interval = int(round(1 / fps))
+
+# 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 = np.array(frame)
+        frame = Image.fromarray(frame).resize((28,28))
+        frame = frame.comvert('L')
+        frame = np.reshape((1,28,28,1))
+        pred = model.predict(frame)
+        pred = np.argmax(pred)
+        pred = array[pred]
+        if out[-1] != pred:
+            out = out+pred
+
+    # Increment the frame counter
+    frame_count += 1
+
+# Release the video capture object
+cap.release()
+
+print(out)