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| # app.py | |
| import gradio as gr | |
| import requests | |
| from bs4 import BeautifulSoup | |
| import google.generativeai as genai | |
| from newspaper import Article | |
| import os | |
| # Configure Gemini API | |
| GEMINI_API_KEY = os.getenv("GEMINI_API_KEY") | |
| genai.configure(api_key=GEMINI_API_KEY) | |
| def fetch_article_content(url): | |
| """Extract article content from URL""" | |
| try: | |
| # Using newspaper3k for better article extraction | |
| article = Article(url) | |
| article.download() | |
| article.parse() | |
| return article.text | |
| except Exception as e: | |
| return f"Error fetching article: {str(e)}" | |
| def generate_platform_post(article_text): | |
| """Generate optimized post using Gemini API""" | |
| try: | |
| model = genai.GenerativeModel('gemini-1.5-pro') | |
| prompt = f""" | |
| Analyze this article content and create: | |
| 1. A compelling title (max 100 characters) | |
| 2. An optimized post in HTML format for Reddit/Quora | |
| 3. Include an image tag with descriptive alt text | |
| Article content: | |
| {article_text[:5000]} # Limit to 5000 chars for token limits | |
| Format your response as: | |
| [TITLE] | |
| [HTML_CONTENT] | |
| Requirements: | |
| - Clean HTML formatting with paragraphs | |
| - Add relevant image tag with descriptive alt text | |
| - Mobile-friendly design | |
| - Minimal CSS styling | |
| """ | |
| response = model.generate_content(prompt) | |
| return parse_gemini_response(response.text) | |
| except Exception as e: | |
| return {"title": "Error generating post", "content": f"<p>{str(e)}</p>"} | |
| def parse_gemini_response(response): | |
| """Parse Gemini's response into title and content""" | |
| try: | |
| title = response.split("[TITLE]")[1].split("[HTML_CONTENT]")[0].strip() | |
| content = response.split("[HTML_CONTENT]")[1].strip() | |
| except: | |
| title = "Content Generation Error" | |
| content = "<p>Failed to parse response from AI</p>" | |
| return {"title": title, "content": content} | |
| def process_url(url): | |
| """Main processing pipeline""" | |
| article_text = fetch_article_content(url) | |
| if article_text.startswith("Error"): | |
| return {"title": "Processing Error", "content": f"<p>{article_text}</p>"} | |
| return generate_platform_post(article_text) | |
| # Create Gradio interface | |
| url_input = gr.Textbox(label="Article URL", placeholder="https://example.com/article...") | |
| title_output = gr.Textbox(label="Generated Title") | |
| content_output = gr.HTML(label="Generated Post") | |
| app = gr.Interface( | |
| fn=process_url, | |
| inputs=url_input, | |
| outputs=[ | |
| gr.Textbox(label="Generated Title"), | |
| gr.HTML(label="Formatted Post") | |
| ], | |
| examples=[ | |
| ["https://example.com/sample-article"] | |
| ], | |
| title="Article to Platform Post Converter", | |
| description="Convert news articles into optimized Reddit/Quora-style posts with AI-generated formatting and image descriptions" | |
| ) | |
| if __name__ == "__main__": | |
| app.launch() |