finished the predict function

Pipfile

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+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+gradio = "*"
+transformers = "*"
+tensorflow = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.8"

app.py

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+from transformers import pipeline
+import gradio as gr
+
+# loading the model
+model_checkpoint = 'zinoubm/e-comerce-category-classification'
+model = pipeline(
+            "text-classification", model=model_checkpoint,
+            )
+
+def predict(input):
+  predictions = model(input)
+  predictions = [prediction['label'] for prediction in predictions]
+  return ' '.join(predictions)
+
+# defining demo content
+title = 'E-Commerce Category Prediction.'
+
+description = '''
+    This is a classification model that predicts the category of an input.
+    We have 4 Categories, Electronics, Household, Books and Clothing & Accessories.
+'''
+
+article = '''
+# How to use this interface
+Using the interface is straight forward, just type some text that falls in one of these 4 categories: **Electronics**, **Household**, **Books** or **Clothing & Accessories**.
+and then hit **Submit**. the results will be in the output cell. You can also try one of the provided examples.
+'''
+
+examples = [
+    ['I want to sell a laptop'],
+    ['This is a beatiful T-shirt'],
+    ['Save 50% on detergent powder']
+]
+
+# launching the interface
+gr.Interface(fn=predict,
+             inputs="text",
+             title=title,
+             description=description,
+             article=article,
+             outputs="text",
+             examples = examples,
+             theme='default',
+             ).launch()

requirements.txt

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+gradio
+transformers
+tensorflow