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| from transformers import GPT2Tokenizer, GPT2LMHeadModel | |
| import torch | |
| import gradio as gr | |
| # Load the tokenizer and the model | |
| tokenizer = GPT2Tokenizer.from_pretrained("gpt2-medium") | |
| model = GPT2LMHeadModel.from_pretrained("gpt2-medium") | |
| # Function for generating text | |
| def generate_text(prompt): | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| outputs = model.generate(inputs.input_ids, max_length=100, num_return_sequences=1) | |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # Define a function that will be used in the Gradio interface | |
| def generate_from_prompt(prompt): | |
| return generate_text(prompt) | |
| # Create the Gradio interface | |
| gr.Interface(fn=generate_from_prompt, inputs="text", outputs="text", title="GPT-2 Medium Text Generator").launch() | |