Update app.py

app.py

@@ -4,47 +4,49 @@ 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"])
-
-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)
+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)
+
+    # 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 = 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)