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| import streamlit as st | |
| from transformers import pipeline | |
| from PIL import Image | |
| import requests | |
| import torch | |
| from diffusers import DiffusionPipeline | |
| def generate_text(prompt, text_generator): | |
| generated_text = text_generator(prompt, max_length=200, num_return_sequences=1, temperature=0.7)[0]['generated_text'] | |
| return generated_text | |
| def generate_image(prompt, img_gen): | |
| generated_image = img_gen(prompt)[0] | |
| return generated_image | |
| def generate_blog_post(keywords, text_generator, img_gen): | |
| # Text generation | |
| generated_text = generate_text(f"Write about {keywords}", text_generator) | |
| # Image generation | |
| generated_image = generate_image(keywords, img_gen) | |
| return f"# {keywords}\n\n## Introduction\n{generated_text}\n\n## Body\n{generated_text}\n\n## Conclusion\n{generated_text}\n\nGenerated Image: {generated_image}" | |
| def main(): | |
| # Load models | |
| text_model_name = "EleutherAI/gpt-neo-1.3B" | |
| text_generator = pipeline("text-generation", model=text_model_name, tokenizer=text_model_name) | |
| img_gen = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder") | |
| # Title of the app | |
| st.title("AI Blog Post Generator") | |
| # User input for keywords | |
| user_keywords = st.text_input("Enter keywords for the blog post:") | |
| # Button to generate blog post | |
| if st.button("Generate Blog Post"): | |
| # Generate blog post | |
| blog_post = generate_blog_post(user_keywords, text_generator, img_gen) | |
| # Display the generated blog post | |
| st.markdown(blog_post, unsafe_allow_html=True) | |
| if __name__ == "__main__": | |
| main() | |