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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 docling.document_converter import DocumentConverter
from huggingface_hub import login
login(token = os.getenv('HF_TOKEN'))
repo_id = "SyntheticIAI/CVCRaft"
model_id = "fine_tuned_llama.gguf"
hf_hub_download(
repo_id=repo_id,
filename=model_id,
local_dir = "./models"
)
def process_document(pdf_path):
extracted_pages = extract_pages(pdf_path)
page2content = {}
for extracted_page in tqdm(extracted_pages):
page_id = extracted_page.pageid
content = process_page(extracted_page)
page2content[page_id] = content
return page2content
def process_page(extracted_page):
content = []
elements = [element for element in extracted_page._objs]
elements.sort(key=lambda a: a.y1, reverse=True)
for i, element in enumerate(elements):
if isinstance(element, LTTextContainer):
line_text = extract_text_and_normalize(element)
content.append(line_text)
content = re.sub('\n+', '\n', ''.join(content))
return content
def extract_text_and_normalize(element):
# Extract text from line and split it with new lines
line_texts = element.get_text().split('\n')
norm_text = ''
for line_text in line_texts:
line_text = line_text.strip()
if not line_text:
line_text = '\n'
else:
line_text = re.sub('\s+', ' ', line_text)
if not re.search('[\w\d\,\-]', line_text[-1]):
line_text += '\n'
else:
line_text += ' '
norm_text += line_text
return norm_text
def txt_to_html(text):
html_content = "<html><body>"
for line in text.split('\n'):
html_content += "<p>{}</p>".format(line.strip())
html_content += "</body></html>"
return html_content
def craft_cv(llm, prompt, maxtokens, temperature, top_probability):
# def craft_cv(llm, cv_text, job_description, maxtokens, temperature, top_probability):
instruction = "Given input CV and job description. Please revise the CV according to the given job description and output the revised CV."
output = llm.create_chat_completion(
messages=[
# {"from": "user", "value": instruction + ' Input CV: ' + cv_text + ' , Job Description: ' + job_description},
{"from": "user", "value": prompt},
],
max_tokens=maxtokens,
temperature=temperature
)
output = output['choices'][0]['message']['content']
cv_text=''
return cv_text, output
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=[
{"from": "user", "value": json_format + ' CV text: ' + cv_text},
],
max_tokens=maxtokens,
temperature=temperature
)
output = output['choices'][0]['message']['content']
return output
def pdf_to_text(prompt, maxtokens=2048, temperature=0, top_probability=0.95):
# def pdf_to_text(cv_file, job_description, llm_type='Fine tuned Llama3', maxtokens=2048, temperature=0, top_probability=0.95):
# converter = DocumentConverter()
# result = converter.convert(cv_file)
# cv_text = result.document.export_to_markdown()
# if(llm_type=='Fine tuned Llama3'):
llm = Llama(
model_path="models/" + model_id,
flash_attn=True,
n_gpu_layers=81,
n_batch=1024,
n_ctx=8192,
)
print('MAX TONENS IS ',maxtokens)
# cv_text, crafted_cv = craft_cv(llm, cv_text, job_description, maxtokens, temperature, top_probability)
# print('CRAFTED CV IS ',crafted_cv)
cv_text, crafted_cv = craft_cv(llm, prompt, maxtokens, temperature, top_probability)
crafted_cv = convert_to_json(llm, crafted_cv, maxtokens, temperature, top_probability)
# print('FINAL CV IS ',crafted_cv)
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')
prompt_text = gr.Textbox(label='Enter the job description')
output_text = gr.Textbox()
llm_type = gr.Radio(["Fine tuned Llama3"])
iface = gr.Interface(
fn=pdf_to_text,
# inputs=[cv_file, prompt_text, llm_type],
inputs=['text'],
outputs=['text'],
title='Craft CV',
description="This application assists to customize CV based on input job description",
theme=gr.themes.Soft(),
)
iface.launch() |