llmahmad/test_app_text_generation

1import os2os.system('pip install streamlit transformers torch')3 4import streamlit as st5from transformers import GPT2LMHeadModel, GPT2Tokenizer6import torch7 8# Load the GPT-2 model and tokenizer9model_name = 'gpt2-large'10tokenizer = GPT2Tokenizer.from_pretrained(model_name)11model = GPT2LMHeadModel.from_pretrained(model_name)12 13def generate_blog_post(topic):14    try:15        # Encode the input topic16        inputs = tokenizer.encode(topic, return_tensors='pt')17 18        # Generate the blog post19        outputs = model.generate(inputs, max_length=500, num_return_sequences=1, no_repeat_ngram_size=2, 20                                 do_sample=True, top_k=50, top_p=0.95, temperature=0.9)21 22        # Decode the generated text23        blog_post = tokenizer.decode(outputs[0], skip_special_tokens=True)24        return blog_post25    except Exception as e:26        st.error(f"Error: {e}")27        return ""28 29# Streamlit app30st.title("Blog Post Generator")31st.write("Enter a topic to generate a blog post.")32 33topic = st.text_input("Topic:")34 35if st.button("Generate"):36    with st.spinner('Generating...'):37        blog_post = generate_blog_post(topic)38    st.write(blog_post)39