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8de5910 5b9bace 972ba93 5b9bace 972ba93 8de5910 5cbefa7 972ba93 8de5910 77d5138 efb5b85 77d5138 efb5b85 77d5138 efb5b85 77d5138 561887e 77d5138 972ba93 8de5910 77d5138 972ba93 8de5910 37cda17 8de5910 f2bc9af 8de5910 c86244b 77d5138 5cbefa7 77d5138 5cbefa7 8de5910 a95e421 77d5138 1a00369 8de5910 77d5138 5cbefa7 77d5138 c69c642 8de5910 3fe49a8 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 | from pdfminer.high_level import extract_pages
from pdfminer.layout import LTTextContainer
from tqdm import tqdm
import re
import gradio as gr
import os
import accelerate
import spaces
import subprocess
from huggingface_hub import hf_hub_download
from llama_cpp import Llama
from huggingface_hub import login
login(token = os.getenv('HF_TOKEN'))
repo_id = "QuantFactory/Meta-Llama-3-70B-Instruct-GGUF"
model_id = "Meta-Llama-3-70B-Instruct.Q2_K.gguf"
local_dir = "models"
hf_hub_download(
repo_id=repo_id,
filename=model_id,
local_dir = local_dir
)
def convert_to_json(llm, cv_text, maxtokens, temperature, top_probability):
json_format = """
You are an expert at structuring resumes in JSON format. Given a modified resume text, extract the relevant details and convert them into the following structured JSON format:
{
"profileDetails": {
"firstName": "",
"lastName": "",
"email": "",
"contact": "",
"country": "",
"jobTitle": "",
"social": "",
"profileDesc": "",
"address": "",
"city": "",
"state": "",
"zipCode": ""
},
"professionalExperience": [
{
"positionTitle": "",
"location": "",
"company": "",
"description": "",
"startDate": "",
"endDate": ""
}
],
"education": [
{
"institute": "",
"schoolLocation": "",
"degree": "",
"field": "",
"grade": "",
"startDate": "",
"endDate": ""
}
],
"skills": [""],
"hobbies": [""],
"languages": [""],
"certifications": [""],
"projects": [
{
"title": "",
"description": ""
}
],
"jobPreferences": {
"compTarget": "",
"strength": "",
"roleTarget": ""
},
"jobDescription": ""
}
Instructions:
- Extract details accurately from the given resume.
- Ensure proper structuring of dates, responsibilities, and projects.
- If a field is missing in the input, leave it as an empty string or an empty list where applicable.
- Maintain proper formatting and avoid unnecessary additions.
Provide the response in a valid JSON format with no additional explanations.
"""
output = llm.create_chat_completion(
messages=[
{"role": "assistant", "content": json_format},
{
"role": "user",
"content": cv_text
}
],
max_tokens=maxtokens,
temperature=temperature
)
output = output['choices'][0]['message']['content']
return output
def craft_cover_letter(llm, cv_text, job_description, maxtokens, temperature, top_probability):
instruction = "Given input CV and job description. Please prepare cover letter according to the given job description and give as an output."
output = llm.create_chat_completion(
messages=[
{"role": "assistant", "content": instruction},
{
"role": "user",
"content": ' Input CV: ' + cv_text + ' , Job Description: ' + job_description
}
],
max_tokens=maxtokens,
temperature=temperature
)
output = output['choices'][0]['message']['content']
return cv_text, output
@spaces.GPU(duration=150)
def pdf_to_text(cv_text, job_description="", function="Convert to JSON", maxtokens=2048, temperature=0, top_probability=0.95):
llm = Llama(
model_path="models/" + model_id,
flash_attn=True,
n_gpu_layers=81,
n_batch=1024,
n_ctx=8192,
)
if(function == 'Cover Letter'):
_, crafted_cv = craft_cover_letter(llm, cv_text, job_description, maxtokens, temperature, top_probability)
else:
crafted_cv = convert_to_json(llm, cv_text, maxtokens, temperature, top_probability)
return crafted_cv
temp_slider = gr.Slider(minimum=0, maximum=2, value=0.9, label="Temperature Value")
prob_slider = gr.Slider(minimum=0, maximum=1, value=0.95, label="Max Probability Value")
max_tokens = gr.Number(value=600, label="Max Tokens")
cv_file = gr.File(label='Upload the CV')
function = gr.Radio(["Convert to JSON", "Cover Letter"])
prompt_text = gr.Textbox(label='Enter the job description')
output_text = gr.Textbox()
iface = gr.Interface(
fn=pdf_to_text,
inputs=['text', prompt_text, function],
outputs=['text'],
title='Create a Cover Letter or convert PDF to JSON',
description="This application assists to create a cover letter based on input job description",
theme=gr.themes.Soft(),
)
iface.launch() |