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| import gradio as gr | |
| # def greet(name): | |
| # return "Hello " + name + "!!" | |
| # demo = gr.Interface(fn=greet, inputs="text", outputs="text") | |
| # demo.launch() | |
| from news_scanner import scan_news | |
| from LLMPipeline import generate_image_prompt #,summarize_headlines | |
| from ImageGenerator import generate_image | |
| from MemeMaker import create_meme | |
| def pipeline(): | |
| # 1. Scrape news | |
| headlines = scan_news() | |
| # # 2. LLM summary | |
| # summary = summarize_headlines(headlines) | |
| # 3. LLM image prompt | |
| img_prompt = generate_image_prompt(headlines)#summary) | |
| # 4. Generate image | |
| img = generate_image(img_prompt) | |
| # # 5. Create meme | |
| meme = create_meme(img,'MEME')# headlines[0]) | |
| return headlines, img_prompt, meme#meme #summary | |
| with gr.Blocks(title="AI News Meme Generator") as app: | |
| gr.Markdown("# News Meme Generator") | |
| gr.Markdown("Este modelo lee un titular, genera un prompt, crea una imagen y hace un meme.") | |
| btn = gr.Button("Generate Meme") | |
| out_headlines = gr.JSON(label="Headline") | |
| # out_summary = gr.Textbox(label="Resumen Humorístico") | |
| out_prompt = gr.Textbox(label="Prompt for Image") | |
| out_image = gr.Image(label="Meme Final") | |
| btn.click(fn=pipeline, outputs=[out_headlines, out_prompt, out_image]) #out_summary | |
| app.launch(ssr_mode=False, share=False) | |