Update app.py

app.py

@@ -11,9 +11,26 @@ classifier = Classifier("keras_model.h5", "labels.txt")
 offset = 20
 imgSize = 300
 
+# Try different camera indices until a valid one is found
+camera_index = 0
+cap = None
+
+while cap is None or not cap.isOpened():
+    cap = cv2.VideoCapture(camera_index)
+    if cap is None or not cap.isOpened():
+        camera_index += 1
+        print(f"Error: Could not open camera with index {camera_index - 1}. Trying index {camera_index}.")
+
+if cap.isOpened():
+    print(f"Camera opened successfully with index {camera_index}.")
+else:
+    print("Error: No valid camera index found.")
+    exit()
+
 def classify_hand(img):
     imgOutput = img.copy()
-    
+
+    # Detect hands
     hands, _ = detector.findHands(img)
 
     if hands:
@@ -43,8 +60,10 @@ def classify_hand(img):
                 hGap = math.ceil((imgSize - hCal) / 2)
                 imgWhite[hGap: hCal + hGap, :] = imgResize
 
+            # Get hand gesture prediction
             prediction, index = classifier.getPrediction(imgWhite, draw=False)
 
+            # Draw bounding box and label
             cv2.rectangle(imgOutput, (x - offset, y - offset - 70), (x - offset + 400, y - offset + 60 - 50), (0, 255, 0),
                           cv2.FILLED)
             cv2.putText(imgOutput, labels[index], (x, y - 30), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 0), 2)
@@ -52,18 +71,5 @@ def classify_hand(img):
 
     return imgOutput
 
-# OpenCV function to capture frames from the webcam
-def capture_frames():
-    cap = cv2.VideoCapture(0)
-    while True:
-        success, img = cap.read()
-        if not success:
-            print("Error: Could not read frame from the camera.")
-            break
-        processed_img = classify_hand(img)
-        cv2.imshow('Hand Gesture Recognition', processed_img)
-        if cv2.waitKey(1) & 0xFF == ord('q'):
-            break
-
-# Start capturing frames
-capture_frames()
+iface = gr.Interface(fn=classify_hand, inputs='webcam', outputs='image', live=True)
+iface.launch()