55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
import requests, base64, os
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data = {
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"urls": ["https://www.nbcnews.com/business"],
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"screenshot": True,
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}
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response = requests.post("https://crawl4ai.com/crawl", json=data)
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result = response.json()["results"][0]
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print(result.keys())
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# dict_keys(['url', 'html', 'success', 'cleaned_html', 'media',
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# 'links', 'screenshot', 'markdown', 'extracted_content',
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# 'metadata', 'error_message'])
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with open("screenshot.png", "wb") as f:
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f.write(base64.b64decode(result["screenshot"]))
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# Example of filtering the content using CSS selectors
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data = {
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"urls": ["https://www.nbcnews.com/business"],
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"css_selector": "article",
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"screenshot": True,
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}
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# Example of executing a JS script on the page before extracting the content
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data = {
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"urls": ["https://www.nbcnews.com/business"],
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"screenshot": True,
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"js": [
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"""
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const loadMoreButton = Array.from(document.querySelectorAll('button')).
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find(button => button.textContent.includes('Load More'));
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loadMoreButton && loadMoreButton.click();
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"""
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],
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}
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# Example of using a custom extraction strategy
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data = {
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"urls": ["https://www.nbcnews.com/business"],
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"extraction_strategy": "CosineStrategy",
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"extraction_strategy_args": {"semantic_filter": "inflation rent prices"},
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}
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# Example of using LLM to extract content
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data = {
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"urls": ["https://www.nbcnews.com/business"],
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"extraction_strategy": "LLMExtractionStrategy",
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"extraction_strategy_args": {
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"provider": "groq/llama3-8b-8192",
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"api_token": os.environ.get("GROQ_API_KEY"),
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"instruction": """I am interested in only financial news,
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and translate them in French.""",
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},
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}
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