Upload app.py

app.py

@@ -0,0 +1,43 @@
+import cv2
+from tensorflow.keras.models import load_model
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image
+import gradio as gr
+
+def recog(img):
+    model = load_model('hack36_2.h5')
+
+    img_array = np.asarray(img)
+    clone = img_array.copy()
+    clone_resized = cv2.resize(clone, (64,64))
+    img_array=clone_resized/255
+    img_final = np.expand_dims(img_array, axis=0)
+    prediction = model.predict(img_final).tolist()[0]
+    alphabet = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'space', 'space', 'space']
+    
+    return {alphabet[i]: prediction[i] for i in range(29)}
+
+
+title = "ASL Fingerspelling Recognition"
+desc = ""
+input = gr.inputs.Image(type="pil", source="webcam")
+# output = gr.outputs.HTML(label="")
+output = gr.outputs.Label(num_top_classes=5)
+# output = "text"
+examples = [
+    ["B_test.jpg"],
+    ["C_test.jpg"],
+    ["Y_test.jpg"]
+]
+
+iface = gr.Interface(
+    fn=recog,
+    title=title,
+    description=desc,
+    examples=examples,
+    inputs=input,
+    outputs=output
+)
+
+iface.launch()