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78 lines
2.6 KiB
Python
78 lines
2.6 KiB
Python
import os, json
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from urllib.parse import urlparse
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from .utils import tokenize, ada_v2_cost
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from .substack_utils import fetch_all_publications, only_valid_publications, get_content, append_meta
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from alive_progress import alive_it
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# Example substack URL: https://swyx.substack.com/
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def substack():
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author_url = input("Enter the substack URL of the author you want to collect: ")
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if(author_url == ''):
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print("Not a valid author.substack.com URL")
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exit(1)
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source = urlparse(author_url)
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if('substack.com' not in source.netloc or len(source.netloc.split('.')) != 3):
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print("This does not appear to be a valid author.substack.com URL")
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exit(1)
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subdomain = source.netloc.split('.')[0]
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publications = fetch_all_publications(subdomain)
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valid_publications = only_valid_publications(publications)
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if(len(valid_publications)==0):
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print("There are no public or free preview newsletters by this creator - nothing to collect.")
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exit(1)
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print(f"{len(valid_publications)} of {len(publications)} publications are readable publically text posts - collecting those.")
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totalTokenCount = 0
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transaction_output_dir = f"../server/documents/substack-{subdomain}"
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if os.path.isdir(transaction_output_dir) == False:
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os.makedirs(transaction_output_dir)
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for publication in alive_it(valid_publications):
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pub_file_path = transaction_output_dir + f"/publication-{publication.get('id')}.json"
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if os.path.exists(pub_file_path) == True: continue
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full_text = get_content(publication.get('canonical_url'))
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if full_text is None or len(full_text) == 0: continue
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full_text = append_meta(publication, full_text)
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item = {
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'id': publication.get('id'),
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'url': publication.get('canonical_url'),
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'thumbnail': publication.get('cover_image'),
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'title': publication.get('title'),
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'subtitle': publication.get('subtitle'),
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'description': publication.get('description'),
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'published': publication.get('post_date'),
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'wordCount': publication.get('wordcount'),
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'pageContent': full_text,
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}
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tokenCount = len(tokenize(full_text))
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item['token_count_estimate'] = tokenCount
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totalTokenCount += tokenCount
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with open(pub_file_path, 'w', encoding='utf-8') as file:
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json.dump(item, file, ensure_ascii=True, indent=4)
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print(f"[Success]: {len(valid_publications)} scraped and fetched!")
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print(f"\n\n////////////////////////////")
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print(f"Your estimated cost to embed all of this data using OpenAI's text-embedding-ada-002 model at $0.0004 / 1K tokens will cost {ada_v2_cost(totalTokenCount)} using {totalTokenCount} tokens.")
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print(f"////////////////////////////\n\n")
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exit(0)
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