Update app.py

app.py

@@ -2,7 +2,6 @@
 
 from transformers import AutoImageProcessor, AutoModelForImageClassification
 from IPython.display import display, Javascript
-from google.colab.output import eval_js
 from base64 import b64decode
 from IPython.display import Image
 import cv2
@@ -12,59 +11,36 @@ import time
 from transformers import BarkModel, BarkProcessor
 from IPython.display import Audio
 
-# Defining the camera in the system
-
-def take_photo(filename='photo.jpg', quality=0.8):
-  js = Javascript('''
-    async function takePhoto(quality) {
-      const div = document.createElement('div');
-      const capture = document.createElement('button');
-      capture.textContent = 'Capture';
-      div.appendChild(capture);
-
-      const video = document.createElement('video');
-      video.style.display = 'block';
-      const stream = await navigator.mediaDevices.getUserMedia({video: true});
-
-      document.body.appendChild(div);
-      div.appendChild(video);
-      video.srcObject = stream;
-      await video.play();
-
-      // Resize the output to fit the video element.
-      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);
-
-      // Wait for Capture to be clicked.
-      await new Promise((resolve) => capture.onclick = resolve);
-
-      const canvas = document.createElement('canvas');
-      canvas.width = video.videoWidth;
-      canvas.height = video.videoHeight;
-      canvas.getContext('2d').drawImage(video, 0, 0);
-      stream.getVideoTracks()[0].stop();
-      div.remove();
-      return canvas.toDataURL('image/jpeg', quality);
-    }
-    ''')
-  display(js)
-  data = eval_js('takePhoto({})'.format(quality))
-  binary = b64decode(data.split(',')[1])
-  with open(filename, 'wb') as f:
-    f.write(binary)
-  return filename
-
-# Capturing snaps using given button and saving them
-
-try:
-  filename = take_photo()
-  print('Saved to {}'.format(filename))
-
-  # Show the image which was just taken.
-  display(Image(filename))
-except Exception as err:
-  # Errors will be thrown if the user does not have a webcam or if they do not
-  # grant the page permission to access it.
-  print(str(err))
+
+# Using captured images
+
+def process_webcam_images():
+    cap = cv2.VideoCapture(0)  # 0 corresponds to the default camera (usually your webcam)
+
+    while True:
+        ret, frame = cap.read()
+
+        # Send the image to the Hugging Face model for classification
+        result = model(frame)
+
+        # Display the classification result
+        print(result)
+
+        # Display the captured image
+        cv2.imshow('Webcam Image', frame)
+
+        # Press 'q' to quit the loop
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+if __name__ == "__main__":
+    process_webcam_images()
+
+image=process_webcam_images()
+
 
 # Using the pre-trained Dog Breed Identification Model
 
@@ -73,9 +49,9 @@ dog_breed_model = AutoModelForImageClassification.from_pretrained("wesleyacheng/
 
 # Importing the saved image
 
-img_path='/content/n02088094_60.jpg'
+#img_path='/content/n02088094_60.jpg'
 
-image=cv2.imread(img_path)
+#image=cv2.imread(img_path)
 
 # Preprocessing the captured image using pre-trained model based preprocessor