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app.py

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+# URL: https://huggingface.co/spaces/gradio/text_generation
+# imports
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# loading the model
+tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
+model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
+
+# defining the core function
+def generate(text):
+    generation_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
+    result = generation_pipeline(text)
+    return result[0]["generated_text"]
+
+
+# defining title, description and examples
+title = "Text Generation with GPT-J-6B"
+description = "This demo generates text using GPT-J 6B: a transformer model trained using Ben Wang's Mesh Transformer JAX."
+examples = [
+    ["The Moon's orbit around Earth has"],
+    ["The smooth Borealis basin in the Northern Hemisphere covers 40%"],
+]
+
+# defining the interface
+demo = gr.Interface(
+    fn=generate,
+    inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
+    outputs=gr.outputs.Textbox(label="Generated Text"),
+    title=title,
+    description=description,
+    examples=examples,
+)
+
+# launching
+demo.launch()