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app.py

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+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+
+# Load pre-trained GPT-2 model and tokenizer
+model_name = "gpt2-large"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Set pad token to eos token
+tokenizer.pad_token = tokenizer.eos_token
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+
+def generate_text(input_text, max_length=32, num_beams=5, do_sample=False, no_repeat_ngram_size=2):
+    """
+    Generate text based on the given input text.
+    Parameters:
+    - input_text (str): The input text to start generation from.
+    - max_length (int): Maximum length of the generated text.
+    - num_beams (int): Number of beams for beam search.
+    - do_sample (bool): Whether to use sampling or not.
+    - no_repeat_ngram_size (int): Size of the n-gram to avoid repetition.
+    Returns:
+    - generated_text (str): The generated text.
+    """
+    # Encode the input text and move it to the appropriate device
+    input_ids = tokenizer(input_text, return_tensors='pt', padding=True)['input_ids']
+    # Generate text using the model
+    output = model.generate(input_ids, max_length=max_length, num_beams=num_beams,
+                            do_sample=do_sample, no_repeat_ngram_size=no_repeat_ngram_size)
+    # Decode the generated output
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    return generated_text
+
+
+# Create Gradio interface
+input_text = gr.Textbox(lines=10, label="Input Text", placeholder="Enter text for text generation...")
+output_text = gr.Textbox(label="Generated Text")
+
+gr.Interface(generate_text, input_text, output_text,
+             title="Text Generation with GPT-2",
+             description="Generate text using the GPT-2 model.",
+             theme="default", 
+             allow_flagging="never").launch()

requirements.txt

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+torch
+transformers