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| import streamlit as st | |
| from dotenv import load_dotenv | |
| import os | |
| from typing import Any, List, Optional | |
| from llama_index.llama_pack.base import BaseLlamaPack | |
| from llama_index.llms.base import LLM | |
| from llama_index.llms import OpenAI | |
| from llama_index import ServiceContext | |
| from llama_index.schema import NodeWithScore | |
| from llama_index.response_synthesizers import TreeSummarize | |
| from pydantic import BaseModel | |
| import PyPDF2 | |
| import io | |
| # Load environment variables from .env file | |
| load_dotenv() | |
| # Get OpenAI API key from environment variables | |
| openai_api_key = os.getenv("OPENAI_API_KEY") | |
| QUERY_TEMPLATE = """ | |
| You are an expert resume reviewer. | |
| Your job is to decide if the candidate passes the resume screen given the job description and a list of criteria: | |
| ### Job Description | |
| {job_description} | |
| ### Screening Criteria | |
| {criteria_str} | |
| """ | |
| class CriteriaDecision(BaseModel): | |
| """The decision made based on a single criterion""" | |
| decision: bool | |
| reasoning: str | |
| class ResumeScreenerDecision(BaseModel): | |
| """The decision made by the resume screener""" | |
| criteria_decisions: List[CriteriaDecision] | |
| overall_reasoning: str | |
| overall_decision: bool | |
| def _format_criteria_str(criteria: List[str]) -> str: | |
| criteria_str = "" | |
| for criterion in criteria: | |
| criteria_str += f"- {criterion}\n" | |
| return criteria_str | |
| class ResumeScreenerPack(BaseLlamaPack): | |
| def __init__( | |
| self, | |
| job_description: str = "", | |
| criteria: List[str] = [], | |
| llm: Optional[LLM] = None | |
| ) -> None: | |
| llm = llm or OpenAI(model="gpt-4", api_key=openai_api_key) | |
| service_context = ServiceContext.from_defaults(llm=llm) | |
| criteria_str = _format_criteria_str(criteria) | |
| self.query = QUERY_TEMPLATE.format( | |
| job_description=job_description, criteria_str=criteria_str | |
| ) | |
| self.synthesizer = TreeSummarize( | |
| output_cls=ResumeScreenerDecision, service_context=service_context | |
| ) | |
| def get_modules(self) -> dict: | |
| """Get modules.""" | |
| return {"synthesizer": self.synthesizer} | |
| def run(self, resume_text: str) -> Any: | |
| """Run pack.""" | |
| node_with_score_input = { | |
| "metadata": {}, # Provide any necessary metadata | |
| "content": resume_text, # Use extracted text as content | |
| "type": "resume_text", # Define the type as per your schema | |
| } | |
| output = self.synthesizer.synthesize( | |
| query=self.query, | |
| nodes=[NodeWithScore(node=node_with_score_input, score=1.0)], | |
| ) | |
| return output.response | |
| def main(): | |
| st.title("Resume Screener App") | |
| # Sidebar for user input | |
| job_description = st.text_area("Job Description") | |
| criteria = st.text_area("Screening Criteria (separate each criterion by a new line)") | |
| uploaded_file = st.file_uploader("Upload Resume (PDF)", type=["pdf"]) | |
| if st.button("Submit"): | |
| if job_description and criteria and uploaded_file: | |
| resume_text = extract_text_from_pdf(uploaded_file) | |
| screener_pack = ResumeScreenerPack(job_description=job_description, criteria=criteria.split("\n")) | |
| with st.spinner("Analyzing the resume..."): | |
| result = screener_pack.run(resume_text) | |
| st.subheader("Screening Results") | |
| st.json(result) | |
| def extract_text_from_pdf(uploaded_file): | |
| if uploaded_file is not None: | |
| try: | |
| # Read PDF content using PyPDF2's PdfReader | |
| pdf_reader = PyPDF2.PdfReader(uploaded_file) | |
| text = "" | |
| for page in pdf_reader.pages: | |
| text += page.extract_text() | |
| return text | |
| except Exception as e: | |
| st.error(f"Error extracting text from PDF: {str(e)}") | |
| return "" | |
| else: | |
| st.error("Please upload a PDF file.") | |
| return "" | |
| if __name__ == "__main__": | |
| main() | |