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app.py

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+import os
+os.system("pip install transformers")
+os.system("pip install gradio")
+os.system("pip install tensorflow")
+import gradio as gr
+
+import tensorflow as tf
+from transformers import GPT2LMHeadModel, GPT2Tokenizer, set_seed
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2-large")
+model = GPT2LMHeadModel.from_pretrained("gpt2-large", pad_token_id=tokenizer.eos_token_id)
+
+
+def generate(prompt,textCount):
+    input_ids = tokenizer.encode(prompt, return_tensors='pt')
+    # generate text until the output length (which includes the context length) reaches 50
+    output = model.generate(input_ids, max_length=textCount, num_beams=5, no_repeat_ngram_size=2, early_stopping=True)
+    out = tokenizer.decode(output[0], skip_special_tokens=True)
+    return out
+
+
+
+demo = gr.Interface(
+    fn=generate,
+    inputs=[gr.Textbox(lines=8, placeholder="Paragraph Here..."),"number"],
+    outputs="text",title="Text generation app with GPT2",
+    description="This is a text generation app, it can prove useful when you want to generate texts. All you need to do is copy and paste a short prompt . ",
+    examples=[
+        ["During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in"
+],["The bald eagle is"]
+    ],
+)
+demo.launch()