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app.py

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+from transformers import pipeline
+import gradio as gr
+
+
+_MODEL_NAME = "lksef"
+_HF_USER = "universalml"
+
+
+def prediction_function(input_file):
+    # get user name of their hugging face
+    model_path = _HF_USER + "/" + _MODEL_NAME
+    # takes some time
+    classifier = pipeline("image-classification", model=model_path)
+
+    try:
+        result = classifier(input_file)
+        predictions = dict()
+        labels = []
+        for each_label in result:
+            predictions[each_label["label"]] = each_label["score"]
+            labels.append(each_label["label"])
+        result = predictions
+    except:
+        result = "no data provided!!"
+
+    return result
+
+
+# change _MODEL_NAME parameter
+def create_demo():
+    demo = gr.Interface(
+        fn=prediction_function,
+        inputs=gr.Image(type="pil"),
+        outputs=gr.Label(num_top_classes=3),
+    )
+    demo.launch()
+
+
+create_demo()