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import gradio as gr |
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import torch |
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from transformers import pipeline |
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import logging |
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logging.basicConfig( |
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level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" |
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) |
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ARTICLE_GENERATOR_MODEL = "gpt2" |
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SUMMARIZER_MODEL = "Falconsai/text_summarization" |
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TITLE_GENERATOR_MODEL = "czearing/article-title-generator" |
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu" |
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logging.info(f"Using device: {DEVICE}") |
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logging.info("Initializing models...") |
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text_generator = pipeline( |
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"text-generation", model=ARTICLE_GENERATOR_MODEL, device=DEVICE |
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) |
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summarizer = pipeline("summarization", model=SUMMARIZER_MODEL, device=DEVICE) |
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title_generator = pipeline( |
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"text2text-generation", model=TITLE_GENERATOR_MODEL, device=DEVICE |
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) |
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logging.info("Models initialized successfully") |
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def generate_article(query, max_new_tokens): |
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logging.info(f"Generating article for query: {query}") |
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article = text_generator( |
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query, |
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max_new_tokens=max_new_tokens, |
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num_return_sequences=1, |
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)[0]["generated_text"] |
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logging.debug(f"Generated article: {article[:100]}...") |
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return article |
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def generate_title(article): |
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logging.info("Generating title") |
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title = title_generator(article, num_return_sequences=1)[0]["generated_text"] |
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logging.debug(f"Generated title: {title}") |
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return title |
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def generate_summary(article): |
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logging.info("Generating summary") |
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summary = summarizer( |
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article, |
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do_sample=False, |
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)[ |
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0 |
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]["summary_text"] |
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logging.debug(f"Generated summary: {summary}") |
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return summary |
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def generate_blog_post(query, max_new_tokens): |
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logging.info("Starting blog post generation") |
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logging.info("Generating article") |
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article = generate_article(query, max_new_tokens) |
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logging.info("Generating title") |
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title = generate_title(article) |
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logging.info("Generating summary") |
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summary = generate_summary(article) |
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logging.info("Blog post generation completed") |
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return title, summary, article |
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with gr.Blocks() as iface: |
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gr.Markdown("# Blog Post Generator") |
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gr.Markdown( |
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"Enter a topic, and I'll generate a blog post with a title and summary!" |
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) |
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with gr.Row(): |
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input_prompt = gr.Textbox( |
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label="Input Prompt", lines=2, placeholder="Enter your blog post topic..." |
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) |
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with gr.Row(): |
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generate_button = gr.Button("Generate Blog Post", size="sm") |
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gr.Examples( |
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examples=[ |
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"The future of artificial intelligence in healthcare", |
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"Top 10 travel destinations for nature lovers", |
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"How to start a successful online business in 2024", |
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"The impact of climate change on global food security", |
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], |
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inputs=input_prompt, |
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) |
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with gr.Row(): |
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with gr.Column(scale=2): |
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with gr.Blocks() as title_block: |
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gr.Markdown("## Title") |
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title_output = gr.Textbox(label="Title") |
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with gr.Blocks() as body_block: |
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gr.Markdown("## Body") |
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article_output = gr.Textbox(label="Article", lines=30) |
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with gr.Accordion("Options", open=False): |
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max_new_tokens = gr.Slider( |
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minimum=20, |
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maximum=1000, |
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value=500, |
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step=10, |
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label="Max New Tokens", |
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) |
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with gr.Column(scale=1): |
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with gr.Blocks() as summary_block: |
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gr.Markdown("## Summary") |
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summary_output = gr.Textbox(label="Summary", lines=5) |
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generate_button.click( |
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generate_blog_post, |
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inputs=[input_prompt, max_new_tokens], |
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outputs=[title_output, summary_output, article_output], |
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) |
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logging.info("Launching Gradio interface") |
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iface.queue().launch() |
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