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fb5a2ab 8f7d8af fb5a2ab c915259 fb5a2ab 76bf7cc fb5a2ab | 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 | import os
import gradio as gr
from pdfminer.high_level import extract_text
from docx import Document
from groq import Groq
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
# Initialize Groq client with your API key
key = os.getenv("GroqCV")
client = Groq(api_key=key)
# Function to read PDF files
def read_pdf(file_path):
text = extract_text(file_path)
return text
# Function to read DOCX files
def read_docx(file_path):
doc = Document(file_path)
text = "\n".join([paragraph.text for paragraph in doc.paragraphs])
return text
# Function to analyze CV for role fit using Llama 3.3 and Groq API
def analyze_cv_with_llama(cv_text, job_description):
completion = client.chat.completions.create(
model="llama-3.3-70b-versatile",
messages=[
{
"role": "system",
"content": (
"You are a hiring expert with extensive experience in talent acquisition. "
"Analyze the following CV content against a job description and score over 100 in these areas: "
"Relevance to the job, Work experience, Skills, Educational background, Achievements and Impact, "
"and Format and Presentation. Provide detailed scores and feedback for each area, make sure you put the name of the candidate bold as heading."
)
},
{"role": "user", "content": f"Job Description:\n{job_description}\n\nCV:\n{cv_text}"}
],
temperature=0.7,
max_tokens=2048,
top_p=0.9,
stream=False,
stop=None,
)
# Collect and return the analysis response
analysis_text = ''.join([chunk.message.content for chunk in completion.choices])
return analysis_text
# Function to generate a PDF with analysis results
def generate_pdf(content, output_path):
pdf = canvas.Canvas(output_path, pagesize=letter)
pdf.setFont("Helvetica", 10)
width, height = letter
y = height - 40 # Start position
for line in content.split("\n"):
pdf.drawString(40, y, line)
y -= 15 # Move down for the next line
if y < 40: # Create a new page if space is insufficient
pdf.showPage()
pdf.setFont("Helvetica", 10)
y = height - 40
pdf.save()
# Function to handle file upload and job screening
def process_file(file, job_description):
# Save the uploaded file to a temporary location
file_path = file.name
file_extension = os.path.splitext(file_path)[1].lower()
# Read the file content based on its format
if file_extension == ".pdf":
cv_text = read_pdf(file_path)
elif file_extension == ".docx":
cv_text = read_docx(file_path)
else:
return "Unsupported file format", None
# Analyze the CV against the job description
analysis_result = analyze_cv_with_llama(cv_text, job_description)
# Generate PDF with analysis results
output_pdf_path = "cv_analysis_result.pdf"
generate_pdf(analysis_result, output_pdf_path)
return analysis_result, output_pdf_path
# Gradio Interface
def main():
with gr.Blocks() as iface:
with gr.Row():
file_input = gr.File(label="Upload CV/Resume (PDF or DOCX)")
job_description_input = gr.Textbox(label="Enter Job Description", lines=5, placeholder="Type the job description here...")
analysis_output = gr.Markdown(label="CV Screening Analysis")
download_button = gr.File(label="Download Analysis as PDF")
def process_input(file, job_description):
if not file or not job_description.strip():
return "Please upload a file and provide a job description.", None
return process_file(file, job_description)
submit_button = gr.Button("Analyze CV")
submit_button.click(process_input, [file_input, job_description_input], [analysis_output, download_button])
iface.launch(share=True)
if __name__ == "__main__":
main()
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