Create app.py

app.py

@@ -0,0 +1,62 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load your model
+model = tf.keras.models.load_model("vgg17.keras")
+
+# Your class labels (replace with yours)
+labels = {
+    0: 'A',
+    1: 'B',
+    2: 'C',
+    3: 'D',
+    4: 'E',
+    5: 'F',
+    6: 'G',
+    7: 'H',
+    8: 'I',
+    9: 'J',
+    10: 'K',
+    11: 'L',
+    12: 'M',
+    13: 'N',
+    14: 'O',
+    15: 'P',
+    16: 'Q',
+    17: 'R',
+    18: 'S',
+    19: 'T',
+    20: 'U',
+    21: 'V',
+    22: 'W',
+    23: 'X',
+    24: 'Y',
+    25: 'Z',
+    26: 'del',
+    27: 'nothing',
+    28: 'space'
+}
+
+def predict(img):
+    img = img.resize((200, 200))
+    img = np.array(img) / 255.0
+    img = np.expand_dims(img, axis=0)
+    prediction = model.predict(img)[0]
+    class_id = np.argmax(prediction)
+    confidence = prediction[class_id]
+
+    return {
+        labels[class_id]: float(confidence)
+    }
+
+# Interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=5),
+    title="ASL Sign Language Recognition"
+)
+
+demo.launch()