Create app.py

app.py

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+# Human Action Recognition
+
+## LOADING MODULES
+
+
+from transformers import pipeline
+from PIL import Image
+import requests
+
+pipe = pipeline("image-classification", "rvv-karma/Human-Action-Recognition-VIT-Base-patch16-224")
+
+def classify_image(input):
+    predictions = pipe(image)
+    return {prediction["label"]: prediction["score"] for prediction in predictions}
+
+    
+# Output:
+# [{'score': 0.9918079972267151, 'label': 'dancing'},
+#  {'score': 0.00207977625541389, 'label': 'clapping'},
+#  {'score': 0.0015223610680550337, 'label': 'running'},
+#  {'score': 0.0009153694845736027, 'label': 'fighting'},
+#  {'score': 0.0006987180095165968, 'label': 'sitting'}]
+
+
+ex=[['cat2.jpg'],
+    ['dog2.jpeg'],  
+     ['cat3.jpg'],
+     ['dog.jpeg']]
+
+"""
+## RUNNING WEB UI"""
+
+image = gr.inputs.Image()
+label = gr.outputs.Label(num_top_classes=5)
+
+gr.Interface(fn=classify_image, inputs=image, outputs=label, title='Human Action Recognition',
+             height=600, width=1200, examples=ex, theme='peach').launch(debug=True)
+