"""Load html from files, clean up, split, ingest into Weaviate."""
import os
from pathlib import Path

import weaviate
from bs4 import BeautifulSoup
from langchain.text_splitter import CharacterTextSplitter

# def clean_data(data):
#     soup = BeautifulSoup(data)
#     text = soup.find_all("main", {"id": "main-content"})[0].get_text()
#     return "\n".join([t for t in text.split("\n") if t])

# docs = []
# metadatas = []
# for p in Path("langchain.readthedocs.io/en/latest/").rglob("*"):
#     if p.is_dir():
#         continue
#     with open(p) as f:
#         docs.append(clean_data(f.read()))
#         metadatas.append({"source": p})

with open('paper-dir/main.txt') as f:
    paper_text = f.read()

docs = paper_text.split("§")
# metadatas is the first word that comes after the section symbol
metadatas = [doc.split(" ")[0] for doc in docs]

text_splitter = CharacterTextSplitter(
    separator="\n",
    chunk_size=1000,
    chunk_overlap=200,
    length_function=len,
)

documents = text_splitter.create_documents(docs, metadatas=metadatas)


WEAVIATE_URL = os.environ["WEAVIATE_URL"]
client = weaviate.Client(
    url=WEAVIATE_URL,
    additional_headers={"X-OpenAI-Api-Key": os.environ["OPENAI_API_KEY"]},
)

client.schema.delete_class("Paragraph")
client.schema.get()
schema = {
    "classes": [
        {
            "class": "Paragraph",
            "description": "A written paragraph",
            "vectorizer": "text2vec-openai",
            "moduleConfig": {
                "text2vec-openai": {
                    "model": "ada",
                    "modelVersion": "002",
                    "type": "text",
                }
            },
            "properties": [
                {
                    "dataType": ["text"],
                    "description": "The content of the paragraph",
                    "moduleConfig": {
                        "text2vec-openai": {
                            "skip": False,
                            "vectorizePropertyName": False,
                        }
                    },
                    "name": "content",
                },
                {
                    "dataType": ["text"],
                    "description": "The link",
                    "moduleConfig": {
                        "text2vec-openai": {
                            "skip": True,
                            "vectorizePropertyName": False,
                        }
                    },
                    "name": "source",
                },
            ],
        },
    ]
}

client.schema.create(schema)

with client.batch as batch:
    for text in documents:
        batch.add_data_object(
            {
                "content": text.page_content,
                "source": str(text.metadata["source"])
            },
            "Paragraph",
        )