This script is to extract the text from a PDF file and convert it to JSON format. Used for foundation_one NGS report, for biomarker_findings

extract_text_from_PDF_and_convert_to_JSON_by_ChatGPT.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# title: main
# author: Hsieh-Ting Lin, the Lizard 🦎
# description: This script is to extract the text from a PDF file and convert it to JSON format. Used for foundation_one NGS report, for biomarker_findings
# date: "2024-07-01"
# --END-- #


import json

from openai import OpenAI
from PyPDF2 import PdfReader

# 載入PDF
pdf_path = "./foundation_one_demopdf.pdf"
reader = PdfReader(pdf_path)

# 確保PDF至少有2頁
if len(reader.pages) < 2:
    print("The PDF has less than 2 pages.")
else:
    # 提取第一頁的文字
    page1 = reader.pages[0]  # 索引0代表第一頁
    text1 = page1.extract_text()

    # 提取第二頁的文字
    page2 = reader.pages[1]  # 索引1代表第二頁
    text2 = page2.extract_text()
    client = OpenAI()

    # 組合 text1 和 text2，並加入提示
    template = """
{\n    \"biomarker_findings\": {\n        \"microsatellite_status\": ...\n        \"tumor_mutational_burden\": ...\n        \"genomic_findings\": {\n          ...\n        },\n        \"disease_relevant_genes\": ...\n    }\n}\n
    """
    combined_text = f"Convert these texts to JSON format, with template{template}:\n\nPage 1:\n{text1}\n\nPage 2:\n{text2}"

    response = client.chat.completions.create(
        model="gpt-3.5-turbo", messages=[{"role": "user", "content": combined_text}]
    )
    # print(response)
    # 獲取    # 獲取回應內容
    result = response.choices[0].message.content

    # 將結果儲存為 JSON 檔案
    with open("result.json", "w") as json_file:
        json.dump(json.loads(result), json_file, ensure_ascii=False, indent=4)

Result:

{
    "biomarker_findings": {
        "microsatellite_status": "MS-Stable",
        "tumor_mutational_burden": "1 Muts/Mb",
        "genomic_findings": {
            "EGFR": [
                "L858R",
                "T790M"
            ],
            "MYC": "amplification - equivocal",
            "RICTOR": "amplification",
            "FGF10": "amplification - equivocal",
            "GATA4": "loss of exons 3-7",
            "TP53": "K351fs*31",
            "Disease_relevant_genes_with_no_reportable_alterations": [
                "ALK",
                "BRAF",
                "ERBB2",
                "KRAS",
                "MET",
                "RET",
                "ROS1"
            ]
        }
    }
}

requirements.txt

annotated-types==0.7.0
anyio==4.4.0
certifi==2024.6.2
distro==1.9.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
openai==1.35.7
pycryptodome==3.20.0
pydantic==2.8.0
pydantic-core==2.20.0
pypdf2==3.0.1
sniffio==1.3.1
tqdm==4.66.4
typing-extensions==4.12.2