Update app.py

app.py

@@ -8,7 +8,6 @@ mp_hands = mp.solutions.hands
 mp_drawing = mp.solutions.drawing_utils
 hands = mp_hands.Hands(max_num_hands=1, min_detection_confidence=0.7)
 
-# Gesture classifier
 def classify_gesture(landmarks):
     if not landmarks:
         return "Unknown"
@@ -21,75 +20,36 @@ def classify_gesture(landmarks):
     wrist = landmarks[0]
 
     def dist(a, b):
-        return ((a.x - b.x)**2 + (a.y - b.y)**2) ** 0.5
+        return ((a.x - b.x)**2 + (a.y - b.y)**2)**0.5
 
-    if thumb_tip.y < index_tip.y and thumb_tip.y < middle_tip.y:
+    if thumb_tip.y < index_tip.y:
         return "A (Thumb Up)"
-
-    fingers_extended = (
-        index_tip.y < wrist.y and
-        middle_tip.y < wrist.y and
-        ring_tip.y < wrist.y and
-        pinky_tip.y < wrist.y
-    )
-    thumb_side = thumb_tip.x < wrist.x or thumb_tip.x > wrist.x
-    if fingers_extended and thumb_side:
+    if index_tip.y < wrist.y and middle_tip.y < wrist.y and ring_tip.y < wrist.y and pinky_tip.y < wrist.y:
         return "B (Flat Hand)"
-
-    curve = dist(index_tip, thumb_tip)
-    palm = dist(wrist, index_tip)
-    if 0.15 < curve < 0.3 and palm > 0.25:
-        return "C (Curved Hand)"
-
-    if dist(thumb_tip, index_tip) < 0.05:
-        return "OK"
-
-    if (index_tip.y < wrist.y and middle_tip.y < wrist.y and
-        ring_tip.y > wrist.y and pinky_tip.y > wrist.y):
-        return "V / Peace"
-
-    if (index_tip.y > wrist.y and
-        middle_tip.y > wrist.y and
-        ring_tip.y > wrist.y and
-        pinky_tip.y > wrist.y):
-        return "Fist"
-
     return "Unknown"
 
-# Process webcam frame
 def process_frame(frame):
     frame = cv2.flip(frame, 1)
-    h, w, _ = frame.shape
     rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
     result = hands.process(rgb)
-
     gesture = "No hand detected"
 
     if result.multi_hand_landmarks:
         for hand_landmarks in result.multi_hand_landmarks:
-            mp_drawing.draw_landmarks(
-                frame, hand_landmarks, mp_hands.HAND_CONNECTIONS
-            )
+            mp_drawing.draw_landmarks(frame, hand_landmarks, mp_hands.HAND_CONNECTIONS)
             gesture = classify_gesture(hand_landmarks.landmark)
 
-    cv2.putText(frame, f"Sign: {gesture}",
-                (10, 40), cv2.FONT_HERSHEY_SIMPLEX,
-                1, (0, 255, 0), 2)
-
-    cv2.putText(frame, "Made by Simar",
-                (10, h - 10), cv2.FONT_HERSHEY_SIMPLEX,
-                0.8, (255, 0, 255), 2)
-
+    cv2.putText(frame, f"Sign: {gesture}", (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
     return frame
 
-# Gradio 3.x Interface (webcam input)
+# Gradio 5.x interface: Use Video input for live webcam
 demo = gr.Interface(
     fn=process_frame,
-    inputs=gr.Image(type="numpy", source="webcam", streaming=True),
-    outputs=gr.Image(),
+    inputs=gr.Video(source="webcam"),  # webcam live input
+    outputs=gr.Image(type="numpy"),
     live=True,
     title="Sign Language Recognition",
-    description="Demo sign recognition using MediaPipe + OpenCV"
+    description="Live sign recognition using MediaPipe + OpenCV"
 )
 
 demo.launch()