Spaces:
Running
Running
File size: 4,447 Bytes
5cc9ba2 90cd969 0b4e109 90cd969 9504053 443db8e 90cd969 d74d42e 90cd969 dd974ce 549a722 dd974ce b9a93a4 dd974ce 549a722 dd974ce 90cd969 25352c2 90cd969 95cbd86 90cd969 443db8e 4f70ac9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
from openai import AzureOpenAI
from PyPDF2 import PdfReader
import os
import gradio as gr
class HeadingsExtractor:
def __init__(self):
"""
Extract headings from a given paragraph using OpenAI's GPT-3.
Args:
contract_page (str): The paragraph from which headings need to be extracted.
Returns:
str: Extracted headings.
"""
# openai.api_type = os.getenv['api_type']
# openai.api_base = os.getenv['api_base']
# openai.api_version = os.getenv['api_version']
# openai.api_key = os.getenv['api_key']
pass
def file_output_fnn(self,file_path):
file_path = file_path.name
return file_path
def extract_headings(self,contract_page: str) -> str:
"""
Extract headings from a given paragraph using OpenAI's GPT-3.
Args:
contract_page (str): The paragraph from which headings need to be extracted.
Returns:
str: Extracted headings.
"""
try:
#get response from openai api
client = AzureOpenAI(api_key=os.getenv("AZURE_OPENAI_KEY"),
api_version="2023-07-01-preview",
azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
)
conversation = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": f"""Extract Headings from given paragraph do not generate jsu extract the headings from paragraph.
```paragraph :{contract_page}```"""}
]
# Call OpenAI GPT-3.5-turbo
chat_completion = client.chat.completions.create(
model = "GPT-3",
messages = conversation,
max_tokens=1000,
temperature=0
)
response = chat_completion.choices[0].message.content
return response
except Exception as e:
# If an error occurs during the key-value extraction process, log the error
print(f"Error while extracting headings: {str(e)}")
def extract_text(self,pdf_file_path: str) -> str:
"""
Extract text from a PDF document and extract headings from each page.
Args:
pdf_file_path (str): Path to the PDF file to extract text from.
Returns:
str: Extracted headings from the PDF document.
"""
try:
# Open the multi-page PDF using PdfReader
print("path",pdf_file_path)
pdf = PdfReader(pdf_file_path.name)
headings = ''
# Extract text from each page and pass it to the process_text function
for page_number in range(len(pdf.pages)):
# Extract text from the page
page = pdf.pages[page_number]
text = page.extract_text()
# Pass the text to the process_text function for further processing
result = self.extract_headings(text)
headings = headings + result
return headings
except Exception as e:
# If an error occurs during the key-value extraction process, log the error
print(f"Error while extracting text from PDF: {str(e)}")
def gradio_interface(self):
with gr.Blocks(css="style.css",theme='xiaobaiyuan/theme_brief') as demo:
with gr.Row(elem_id = "col-container",scale=0.80):
with gr.Column(elem_id = "col-container",scale=0.80):
file1 = gr.File(label="File",elem_classes="filenameshow")
with gr.Column(elem_id = "col-container",scale=0.20):
upload_button1 = gr.UploadButton(
"Browse File",file_types=[".txt", ".pdf", ".doc", ".docx",".json",".csv"],
elem_classes="uploadbutton")
headings_btn = gr.Button("Get Headings",elem_classes="uploadbutton")
with gr.Row(elem_id = "col-container",scale=0.60):
headings = gr.Textbox(label = "Headings")
upload_button1.upload(self.file_output_fnn,upload_button1,file1)
headings_btn.click(self.extract_text,upload_button1,headings)
|