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| # app.py | |
| """ | |
| Gradio App for Smart Web Analyzer Plus | |
| Key Features: | |
| - Accepts a URL | |
| - Lets users select analysis modes (Clean Text, Summarization, Sentiment, Topic) | |
| - Fetches and processes content | |
| - Displays JSON output with results | |
| - Includes example URLs | |
| """ | |
| import gradio as gr | |
| from smart_web_analyzer import ( | |
| fetch_web_content, | |
| clean_text, | |
| summarize_text, | |
| analyze_sentiment, | |
| detect_topic, | |
| preview_clean_text, | |
| ) | |
| def analyze_url(url, modes): | |
| """ | |
| Fetches web content and performs selected analyses (modes). | |
| Parameters: | |
| url (str): URL to analyze | |
| modes (list): list of selected modes | |
| Returns: | |
| dict: a dictionary of results or an error message | |
| """ | |
| results = {} | |
| # Attempt to fetch the web content | |
| try: | |
| html_content = fetch_web_content(url) | |
| except Exception as e: | |
| return {"error": str(e)} # show error in JSON output | |
| # Clean the content | |
| cleaned = clean_text(html_content) | |
| # Perform selected analyses | |
| if "Clean Text Preview" in modes: | |
| results["Clean Text Preview"] = preview_clean_text(cleaned, max_chars=500) | |
| if "Summarization" in modes: | |
| results["Summarization"] = summarize_text(cleaned) | |
| if "Sentiment Analysis" in modes: | |
| results["Sentiment Analysis"] = analyze_sentiment(cleaned) | |
| if "Topic Detection" in modes: | |
| topics = detect_topic(cleaned) | |
| if isinstance(topics, dict) and "error" in topics: | |
| results["Topic Detection"] = topics["error"] | |
| else: | |
| # Format detected topics into a readable string | |
| # for the output | |
| topics_formatted = "\n".join([f"{t}: {s:.2f}" for t, s in topics.items()]) | |
| results["Topic Detection"] = topics_formatted | |
| return results | |
| # Build Gradio Interface | |
| def build_app(): | |
| with gr.Blocks(title="Smart Web Analyzer Plus") as demo: | |
| gr.Markdown("# Smart Web Analyzer Plus") | |
| gr.Markdown( | |
| "Analyze web content for summarization, sentiment, and topics. " | |
| "Choose your analysis modes and enter a URL below." | |
| ) | |
| with gr.Row(): | |
| url_input = gr.Textbox( | |
| label="Enter URL", | |
| placeholder="https://example.com", | |
| lines=1 | |
| ) | |
| mode_selector = gr.CheckboxGroup( | |
| label="Select Analysis Modes", | |
| choices=["Clean Text Preview", "Summarization", "Sentiment Analysis", "Topic Detection"], | |
| value=["Clean Text Preview", "Summarization", "Sentiment Analysis", "Topic Detection"] | |
| ) | |
| output_box = gr.JSON(label="Analysis Results") | |
| # Button to run analysis | |
| analyze_button = gr.Button("Analyze") | |
| # On click, run the analysis function | |
| analyze_button.click( | |
| fn=analyze_url, | |
| inputs=[url_input, mode_selector], | |
| outputs=output_box | |
| ) | |
| # Example URLs | |
| gr.Markdown("### Example URLs") | |
| gr.Examples( | |
| examples=[ | |
| ["https://www.artificialintelligence-news.com/2024/02/14/openai-anthropic-google-white-house-red-teaming/"], | |
| ["https://www.artificialintelligence-news.com/2024/02/13/ai-21-labs-wordtune-chatgpt-plugin/"] | |
| ], | |
| inputs=url_input, | |
| label="Click an example to analyze" | |
| ) | |
| return demo | |
| if __name__ == "__main__": | |
| demo_app = build_app() | |
| demo_app.launch() | |