Update app.py

app.py

@@ -8,15 +8,63 @@ mp_hands = mp.solutions.hands
 mp_drawing = mp.solutions.drawing_utils
 hands = mp_hands.Hands(max_num_hands=1, min_detection_confidence=0.7)
 
-# Simple gesture classifier
+# Gesture classifier with multiple gestures
 def classify_gesture(landmarks):
-    if landmarks:
-        thumb_tip = landmarks[4]
-        index_tip = landmarks[8]
-        if thumb_tip.y < index_tip.y:
-            return "A"  # Thumb up
+    if not landmarks:
+        return "Unknown"
+
+    # Extract needed landmarks
+    thumb_tip = landmarks[4]
+    index_tip = landmarks[8]
+    middle_tip = landmarks[12]
+    ring_tip = landmarks[16]
+    pinky_tip = landmarks[20]
+    wrist = landmarks[0]
+
+    # Helper distance function
+    def dist(a, b):
+        return ((a.x - b.x)**2 + (a.y - b.y)**2) ** 0.5
+
+    # --- Gesture A (Thumb Up) ---
+    if thumb_tip.y < index_tip.y and thumb_tip.y < middle_tip.y:
+        return "A (Thumb Up)"
+
+    # --- Gesture B (Flat Hand) ---
+    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:
+        return "B (Flat Hand)"
+
+    # --- Gesture C (Curved 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)"
+
+    # --- OK Gesture ---
+    if dist(thumb_tip, index_tip) < 0.05:
+        return "OK"
+
+    # --- Peace Gesture ---
+    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"
+
+    # --- Fist Gesture ---
+    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
@@ -42,9 +90,10 @@ def process_frame(frame):
 
     return frame
 
+# Gradio interface (Gradio 3.x compatible)
 demo = gr.Interface(
     fn=process_frame,
-    inputs=gr.Camera(streaming=True),      # <-- FIXED
+    inputs=gr.Image(type="numpy", source="webcam", streaming=True),
     outputs=gr.Image(),
     live=True,
     title="Sign Language Recognition",