Instructions to use lenawilli/SOE_Python_App with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use lenawilli/SOE_Python_App with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://lenawilli/SOE_Python_App") - Notebooks
- Google Colab
- Kaggle
| import gradio as gr | |
| import requests | |
| from bs4 import BeautifulSoup | |
| def gerar_artigo(url): | |
| try: | |
| html = requests.get(url).text | |
| soup = BeautifulSoup(html, "html.parser") | |
| texto = soup.get_text() | |
| artigo = f""" | |
| SEO Article Generated | |
| Source URL: | |
| {url} | |
| Summary of Content: | |
| {texto[:2000]} | |
| Suggested SEO Structure | |
| Title: Optimized Article Based on Source | |
| H2 Introduction | |
| Rewrite the introduction based on the extracted content. | |
| H2 Main Topic | |
| Explain the main ideas of the article. | |
| H2 Additional Insights | |
| Add more value and explanations. | |
| H2 Conclusion | |
| Summarize the topic and include final thoughts. | |
| """ | |
| return artigo | |
| except: | |
| return "Error reading URL" | |
| interface = gr.Interface( | |
| fn=gerar_artigo, | |
| inputs="text", | |
| outputs="text", | |
| title="URL to SEO Article Generator", | |
| description="Paste a URL and generate a new article structure." | |
| ) | |
| interface.launch() |