Create app.py

app.py

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+import cv2
+import mediapipe as mp
+import numpy as np
+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",
+    11: "two", 12: "six", 13: "seven", 14: "eight",
+    15: "nine", 16: "ten", 17: "E", 18: "four",
+    19: "i", 20: "k", 21: "r", 22: "zero",
+    23: "m", 24: "s"
+}
+
+CONF_THRESHOLD = 0.6
+
+# ----------------------------
+# Model
+# ----------------------------
+class GestureNet(nn.Module):
+    def __init__(self, input_size=126, num_classes=len(GESTURE_LABELS)):
+        super().__init__()
+        self.fc1 = nn.Linear(input_size, 256)
+        self.fc2 = nn.Linear(256, 128)
+        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)
+        x = self.relu(self.fc2(x))
+        x = self.dropout(x)
+        return self.fc3(x)
+
+model = GestureNet()
+model.load_state_dict(torch.load("gesture_model1.pth", map_location="cpu"))
+model.eval()
+
+# ----------------------------
+# MediaPipe
+# ----------------------------
+mp_hands = mp.solutions.hands
+
+def predict(image):
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    with mp_hands.Hands(static_image_mode=True, max_num_hands=2) as hands:
+        results = hands.process(image)
+
+    coords = []
+
+    if results.multi_hand_landmarks:
+        for hand_landmarks in results.multi_hand_landmarks:
+            hand_coords = np.array([[lm.x, lm.y, lm.z] for lm in hand_landmarks.landmark])
+
+            hand_coords -= hand_coords[0]
+            max_val = np.max(np.linalg.norm(hand_coords, axis=1))
+            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]
+
+        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"
+)
+
+app.launch()