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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()
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