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import gradio as gr |
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from transformers import GPT2LMHeadModel, GPT2Tokenizer |
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import torch |
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model_name = "gpt2" |
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tokenizer = GPT2Tokenizer.from_pretrained(model_name) |
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model = GPT2LMHeadModel.from_pretrained(model_name) |
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def generate_blogpost(topic): |
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prompt = f"Write a detailed blog post about {topic}." |
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inputs = tokenizer.encode(prompt, return_tensors="pt") |
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outputs = model.generate( |
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inputs, |
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max_length=300, |
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num_return_sequences=1, |
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pad_token_id=tokenizer.eos_token_id, |
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do_sample=True, |
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temperature=0.7, |
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top_p=0.9 |
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) |
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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return generated_text |
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iface = gr.Interface( |
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fn=generate_blogpost, |
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inputs=gr.Textbox(lines=2, placeholder="Enter blog topic here..."), |
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outputs=gr.Textbox(), |
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title="Blog Post Generator", |
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description="Generate a detailed blog post for a given topic using GPT-2." |
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) |
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if __name__ == "__main__": |
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iface.launch() |
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