Add application file

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,76 @@
+from keras.models import load_model
+from keras.layers import Rescaling,Resizing
+import tensorflow as tf
+import numpy as np
+import gradio as gr
+from numpy import asarray
+
+
+model = load_model('./checkpoints/checkpoint.model.keras')
+
+scale = Rescaling(1./255)
+resize = Resizing(224,224)
+
+
+def action_recognition(image):
+
+    preds = ['Calling','Clapping','Cycling','Dancing','Drinking','Eating','Fighting',
+             'Hugging','Laughing','Listening to Music','Running or Walking','Sitting','Sleeping','Texting','Using Laptop'] 
+
+    # Read image and showy
+    img = asarray(image)
+    
+    # Preprocess image
+    img = scale(img)
+    img = resize(img)
+    img = tf.reshape(img,(1,224,224,3))
+
+    # prediction
+    pred = model.predict(img)
+
+    # Mapping indices to their respective class labels
+    if np.argmax(pred) == 0:
+        print('Calling')
+    elif np.argmax(pred) == 1:
+        print('Clapping')
+    elif np.argmax(pred) == 2:
+        print('Cycling')
+    elif np.argmax(pred) == 3:
+        print('Dancing')
+    elif np.argmax(pred) == 4:
+        print('Drinking')
+    elif np.argmax(pred) == 5:
+        print('Eating')
+    elif np.argmax(pred) == 6:
+        print('Fighting')
+    elif np.argmax(pred) == 7:
+        print('Hugging')
+    elif np.argmax(pred) == 8:
+        print('Laughing')
+    elif np.argmax(pred) == 9:
+        print('Listening to Music')
+    elif np.argmax(pred) == 10:
+        print('Running')
+    elif np.argmax(pred) == 11:
+        print('Sitting')
+    elif np.argmax(pred) == 12:
+        print('Sleeping')
+    elif np.argmax(pred) == 13:
+        print('Texting')
+    elif np.argmax(pred) == 14:
+        print('Using Laptop')
+    
+    # Return the predicted class index and prediction array
+    return preds[np.argmax(pred)]
+    
+
+demo = gr.Interface(
+    fn=action_recognition,
+    inputs=[gr.Image(label="Image")],
+    outputs=['text'],
+    allow_flagging='never'
+)
+
+
+demo.launch(share=False,debug=False)
+

checkpoints/checkpoint.model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:73f81dbd1ac7d76d93486714296ae819752fe60a3f4699d14cb829dde5e5a37c
+size 263332280