import gradio as gr
from scraper import crawl
import pandas as pd

def run_scraper(url, topic, depth):
    if not url or not topic:
        return "Please provide both URL and Topic.", None
        
    try:
        depth = int(depth)
    except ValueError:
        return "Depth must be an integer.", None
        
    results = crawl(url, topic, depth)
    
    if not results:
        return f"No content found for topic '{topic}' up to depth {depth}.", None
        
    # Format results for display
    formatted_results = []
    for res in results:
        for match in res['matches']:
            formatted_results.append({
                "URL": res['url'],
                "Matched Content": match
            })
            
    df = pd.DataFrame(formatted_results)
    return f"Found {len(formatted_results)} matches across {len(results)} pages.", df

with gr.Blocks(title="Web Content Topic Scraper") as demo:
    gr.Markdown("# 網站主題內容爬蟲 (Web Content Topic Scraper)")
    gr.Markdown("輸入目標網址與主題，爬蟲會自動尋找相關內容。")
    
    with gr.Row():
        with gr.Column():
            url_input = gr.Textbox(label="目標網址 (URL)", placeholder="https://www.bbc.com/news")
            topic_input = gr.Textbox(label="尋找主題 (Topic)", placeholder="technology")
            depth_input = gr.Slider(minimum=0, maximum=3, step=1, value=1, label="爬取層數 (Depth)")
            submit_btn = gr.Button("開始搜尋 (Search)", variant="primary")
            
        with gr.Column():
            status_output = gr.Textbox(label="狀態 (Status)")
            results_output = gr.Dataframe(label="搜尋結果 (Results)", wrap=True)
            
    submit_btn.click(
        fn=run_scraper,
        inputs=[url_input, topic_input, depth_input],
        outputs=[status_output, results_output]
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", share=False)