Update app.py

app.py

@@ -5,58 +5,15 @@ import numpy as np
 import tensorflow as tf
 import streamlit as st 
 import tempfile
-
+model = load_model('HandSignClassifier (2).h5')
 # Open the video file
 f = st.file_uploader("Choose a Video")
-
-if f is not None:
+array = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z','del','nothing','space']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 (2).h5')
-    array = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z','del','nothing','space']
-    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_BGR2RGB)
-            frame = cv2.resize(frame, (256, 256))  # Resize to (28, 28)
-            frame = np.reshape(frame, (1, 256, 256, 3))
-            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)
+    img = Image.open(f)
+    img = img.resize((256,256))
+    img = np.reshape(img,(1,256,256,3))
+    pred = model.predict(img)
+    st.Image(img,use_column_width=True)
+    st.write(array[np.argmax(pred)])
+