import gradio as gr from transformers import GPT2LMHeadModel, GPT2Tokenizer import torch # Load pre-trained model and tokenizer model_name = "gpt2" tokenizer = GPT2Tokenizer.from_pretrained(model_name) model = GPT2LMHeadModel.from_pretrained(model_name) # Function to generate a blog post def generate_blogpost(topic): prompt = f"Write a detailed blog post about {topic}." inputs = tokenizer.encode(prompt, return_tensors="pt") outputs = model.generate( inputs, max_length=300, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id, do_sample=True, # Enable sampling temperature=0.7, # Control the randomness of the output top_p=0.9 # Nucleus sampling ) generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) return generated_text # Define the Gradio interface iface = gr.Interface( fn=generate_blogpost, inputs=gr.Textbox(lines=2, placeholder="Enter blog topic here..."), outputs=gr.Textbox(), title="Blog Post Generator", description="Generate a detailed blog post for a given topic using GPT-2." ) # Launch the Gradio interface without specifying the port if __name__ == "__main__": iface.launch()