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| import os | |
| import csv | |
| import json | |
| import streamlit as st | |
| from PyPDF2 import PdfReader | |
| from llm_config import instantiate_llm | |
| from langchain.callbacks import get_openai_callback | |
| from langchain.llms import OpenAI | |
| from langchain.chains import LLMChain | |
| from langchain.prompts import PromptTemplate | |
| from langchain.output_parsers import PydanticOutputParser | |
| from template import prompt_template | |
| import pandas as pd | |
| from candidate import Candidate | |
| import logging | |
| from rank import extract_and_rank | |
| def extract_resume(resume): | |
| reader = PdfReader(resume) | |
| return "".join(page.extract_text() for page in reader.pages) | |
| def main(): | |
| st.set_page_config(layout="wide", page_title="Hushh Jobs") | |
| st.header("Hushh Jobs") | |
| model, prompt = instantiate_llm() | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| resumes = st.file_uploader( | |
| "Upload resumes here!", accept_multiple_files=True, type="pdf" | |
| ) | |
| with col2: | |
| no_of_resumes = st.number_input("Enter the number of resumes you want to shortlist",step=1) | |
| with col3: | |
| job_description = st.text_area("Enter the job description here!", height=250) | |
| rank_btn = st.button("Rank") | |
| if resumes and rank_btn: | |
| if len(job_description) < 1: | |
| st.warning( | |
| "Invalid or Empty job description! Please make sure your job description has atleast 25 characters!" | |
| ) | |
| else: | |
| dict_object = {} | |
| rows = [] | |
| ranked_resumes, embeddings_bank, text_bank = extract_and_rank( | |
| resumes, job_description | |
| ) | |
| no_of_resumes=int(no_of_resumes) | |
| for selected_resume in ranked_resumes[:no_of_resumes]: | |
| resume_text = text_bank[selected_resume[0]] | |
| doc_query = f"Return only a json based on this candidate's resume information: {resume_text}" | |
| input = prompt.format_prompt(query=doc_query) | |
| #using PydanticOutputParser for structuring language model responses into a coherent, JSON-like format. | |
| parser = PydanticOutputParser(pydantic_object=Candidate) | |
| with get_openai_callback() as cb: | |
| try: | |
| result = model(input.to_string()) | |
| st.success(result) | |
| class_object= parser.parse(result) #using the above defined pydantic output parser to structure the response in a json-format | |
| dict_object=class_object.__dict__ | |
| #dict_object = json.loads(result) | |
| rows.append(dict_object) | |
| except Exception as error: | |
| print(error) | |
| field_names = [ | |
| "name", | |
| "email", | |
| "phone", | |
| "location", | |
| "degree", | |
| "college", | |
| "skills", | |
| "companies", | |
| "roles", | |
| "degree_year", | |
| "experience", | |
| ] | |
| user_csv = "shortlisted.csv" | |
| write_csv(user_csv=user_csv, field_names=field_names, rows=rows) | |
| df = pd.read_csv(user_csv) | |
| st.dataframe(df) | |
| #def write_csv(user_csv, field_names, rows): | |
| def write_csv(user_csv, field_names, rows): | |
| with open(user_csv, "w") as csvfile: | |
| writer = csv.DictWriter(csvfile, fieldnames=field_names) | |
| writer.writeheader() | |
| for row in rows: | |
| writer.writerow(row) | |
| def write_response(user_csv, response: str): | |
| """ | |
| Write a response from an agent to a Streamlit app. | |
| Args: | |
| response_dict: The response from the agent. | |
| Returns: | |
| None. | |
| """ | |
| df = pd.read_csv(user_csv) | |
| data = eval(response) | |
| st.dataframe(data=data, use_container_width=True) | |
| if __name__ == "__main__": | |
| main() | |