app.py

import streamlit as st
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load the model and tokenizer
model_name = "gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

model = GPT2LMHeadModel.from_pretrained(model_name)

# Function to create the prompt
def make_prompt(new_title):
    prompt = f"""
    Write a Blog Post On the given Title like the example:
    Title: The Benefits of Daily Meditation

    Blog Content:
    Meditation is a practice where an individual uses a technique – such as mindfulness, 
or focusing the mind on a particular object, thought, or activity – to train attention and awareness, 
and achieve a mentally clear and emotionally calm and stable state. Daily meditation can bring numerous 
benefits such as reducing stress, improving concentration, and promoting a healthy lifestyle.

    Title: {new_title}

    Blog Content:
    """
    return prompt

# Function to generate the blog content
def generate_blog(prompt):
    input_ids = tokenizer.encode(prompt, return_tensors='pt')
    output = model.generate(input_ids, max_length=800, num_return_sequences=1, no_repeat_ngram_size=2)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Streamlit interface
st.title("AI Blog Generator")

# Input box for the blog title
title = st.text_input("Enter the Blog Title:")

if st.button("Generate Blog"):
    if title:
        prompt = make_prompt(title)
        blog_content = generate_blog(prompt)
        st.subheader("Generated Blog")
        st.write(blog_content)