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from PyPDF2 import PdfReader
import gradio as gr
from langchain.chat_models import ChatOpenAI
from kor import create_extraction_chain, Object, Text
from langchain.prompts.prompt import PromptTemplate
# from langchain.chains import LLMChain
# from langchain.llms import OpenAI
from dotenv import load_dotenv
load_dotenv()
def gen_text(pdf_file):
with open(pdf_file.name, "rb") as f:
reader = PdfReader(f)
num_pages = len(reader.pages)
text = ""
for page in reader.pages:
text += page.extract_text()
print(text)
constraints=context_extracter(text)
return constraints
# def generate_questions(resume,role='',experience=''):
# _PROMPT_TEMPLATE = """
# this is the resume of user:
# {resume_details}
# here is the role he want to join in :
# {role}
# Based on the following experience:
# {experience}
# What are your interview questions for the given user resume and role he want to join in with that experience?
# generate no of questions = {questions}!
# """
# PROMPT = PromptTemplate(input_variables=["resume_details", "role", "experience",'questions'], template=_PROMPT_TEMPLATE)
# llm1 = OpenAI(model_name="text-davinci-003", temperature=0)
# chain = LLMChain(llm=llm1, prompt=PROMPT)
# prompt = chain.predict_and_parse(resume_details= gen_text(resume),
# role= role,
# experience= experience,
# questions=10)
# return prompt.split('\n')
def context_extracter(text):
llm = ChatOpenAI(
model_name="gpt-3.5-turbo-16k",
temperature=0,
max_tokens=2000,
frequency_penalty=0,
presence_penalty=0,
top_p=1.0,
)
schema = Object(
id="interviewer",
description=(
"interviewer is examining resume text and should produce set of attributes which represents that person by his resume"
),
attributes=[
Text(
id="summary_or_objective",
description="A brief overview of the candidate's professional background, skills, and career goals",
examples=[],
many=True,
),
Text(
id="work_experience",
description="Details of previous employment positions, including job titles, company names, employment dates, and a description of responsibilities and achievements for each role ",
examples=[],
many=True,
),
Text(
id="education",
description="Information about the candidate's educational qualifications, including degrees, certificates, and the names of institutions attended",
examples=[],
many=True,
),
Text(
id="skills",
description="A section highlighting the candidate's relevant skills, such as technical skills, languages spoken, software proficiency, or specific tools used",
examples=[],
many=True,
),
Text(
id="achievements_or_awards",
description="Any notable achievements, awards, or recognition received by the candidate during their education or career.",
examples=[],
many=True,
),
Text(
id="certifications_or_licenses",
description="Information about any professional certifications or licenses held by the candidate that are relevant to the desired position",
examples=[],
many=True,
),
Text(
id="projects",
description="Details of significant projects the candidate has worked on, including a brief description, their role, and any notable outcomes",
examples=[],
many=True,
),
Text(
id="publications_or_presentations",
description=" If applicable, a list of publications or presentations the candidate has authored or delivered, including the titles, dates, and locations",
examples=[],
many=True,
),
],
many=True,
)
# chain = LLMChain(llm=llm1, prompt=PROMPT)
chain = create_extraction_chain(llm, schema, encoder_or_encoder_class='json')
return chain.run(text=text)['data']
k=gr.Interface(
fn=gen_text,
inputs=['file'],
outputs=['json']
)
k.launch()
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