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import json
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import streamlit as st
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from .web_utilities import st_cache_data_if, supported_cache
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from pdf2image import convert_from_bytes, convert_from_path
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import pytesseract
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@st_cache_data_if(supported_cache, max_entries=10, ttl=3600)
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def convert_df(df):
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return df.dropna(how="all").to_csv(sep="\t", index=False).encode("utf-8")
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@st_cache_data_if(supported_cache, max_entries=10, ttl=3600)
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def convert_df_no_header(df):
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return (
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df.dropna(how="all").to_csv(sep="\t", index=False, header=None).encode("utf-8")
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)
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@st_cache_data_if(supported_cache, max_entries=10, ttl=3600)
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def convert_json(df):
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dict_return = {"features": []}
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df_check = df.dropna(subset=["HPO ID", "Phenotype name"])
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if len(df_check) > 0:
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df_dict_list = df[["HPO ID", "Phenotype name"]].to_dict(orient="index")
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for key, value in df_dict_list.items():
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dict_return["features"].append(
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{
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"id": value["HPO ID"],
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"observed": "yes",
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"label": value["Phenotype name"],
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"type": "phenotype",
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}
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)
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return json.dumps(dict_return)
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else:
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return json.dumps(dict_return)
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@st_cache_data_if(supported_cache, max_entries=10, ttl=3600)
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def convert_list_phenogenius(df):
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df_check = df.dropna(subset=["HPO ID", "Phenotype name"])
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if len(df_check) > 0:
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return ",".join(df_check["HPO ID"].to_list())
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else:
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return "No HPO in letters."
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@st_cache_data_if(supported_cache, max_entries=10, ttl=3600)
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def convert_pdf_to_text(file):
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if isinstance(file, bytes):
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images = convert_from_bytes(file)
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else:
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images = convert_from_path(file)
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extraction = []
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for img in images:
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text = pytesseract.image_to_string(img)
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extraction.append(text)
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return " ".join(extraction) |
