Update app.py

app.py

@@ -5,6 +5,9 @@ import torch
 import torch.nn as nn
 import gradio as gr
 
+# ----------------------------
+# Labels
+# ----------------------------
 GESTURE_LABELS = {
     0: "A", 1: "B", 2: "L", 3: "U", 4: "V", 5: "W",
     6: "Z", 7: "F", 8: "five", 9: "one", 10: "three",
@@ -15,6 +18,9 @@ GESTURE_LABELS = {
 }
 CONF_THRESHOLD = 0.6
 
+# ----------------------------
+# Model
+# ----------------------------
 class GestureNet(nn.Module):
     def __init__(self, input_size=126, num_classes=len(GESTURE_LABELS)):
         super().__init__()
@@ -23,6 +29,7 @@ class GestureNet(nn.Module):
         self.fc3 = nn.Linear(128, num_classes)
         self.relu = nn.ReLU()
         self.dropout = nn.Dropout(0.3)
+
     def forward(self, x):
         x = self.relu(self.fc1(x))
         x = self.dropout(x)
@@ -34,13 +41,22 @@ model = GestureNet()
 model.load_state_dict(torch.load("gesture_model1.pth", map_location="cpu"))
 model.eval()
 
+# ----------------------------
+# MediaPipe
+# ----------------------------
 mp_hands = mp.solutions.hands
 
+# ----------------------------
+# Predict
+# ----------------------------
 def predict(image):
-    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)  # Gradio sends RGB
-    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    # Gradio sends RGB, convert to BGR for OpenCV then back to RGB for MediaPipe
+    image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
+
     with mp_hands.Hands(static_image_mode=True, max_num_hands=2) as hands:
         results = hands.process(image_rgb)
+
     coords = []
     if results.multi_hand_landmarks:
         for hand_landmarks in results.multi_hand_landmarks:
@@ -50,26 +66,32 @@ def predict(image):
             if max_val > 0:
                 hand_coords /= max_val
             coords.extend(hand_coords.flatten())
+
     if len(coords) < 126:
         coords.extend([0.0] * (126 - len(coords)))
     elif len(coords) > 126:
         coords = coords[:126]
+
     if len(coords) == 126:
         input_tensor = torch.tensor(coords, dtype=torch.float32).unsqueeze(0)
         with torch.no_grad():
             outputs = model(input_tensor)
-            probs = torch.softmax(outputs, dim=1).numpy()[0]
-        pred_class = np.argmax(probs)
-        confidence = probs[pred_class]
+            probs = torch.softmax(outputs, dim=1)
+            pred_class = torch.argmax(probs, dim=1).item()
+            confidence = probs[0][pred_class].item()
         if confidence >= CONF_THRESHOLD:
             return f"{GESTURE_LABELS[pred_class]} ({confidence*100:.2f}%)"
+
     return "Unknown"
 
+# ----------------------------
+# Gradio UI
+# ----------------------------
 app = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="numpy"),
     outputs="text",
     title="Hand Gesture Recognition",
-    description="Upload an image of a hand gesture"
+    description="Upload an image of a hand gesture to recognize it."
 )
 app.launch()