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import gradio as gr
import os
import fitz
from openai import AzureOpenAI
import re
# client = AzureOpenAI(api_key=os.getenv("AZURE_OPENAI_KEY"),
# api_version="2023-07-01-preview",
# azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
# )
client = AzureOpenAI()
class ResumeAnalyser:
def __init__(self):
pass
def extract_text_from_file(self, file_path):
# Get the file extension
file_extension = os.path.splitext(file_path)[1]
if file_extension == '.pdf':
# Use PyMuPDF (fitz) for PDF text extraction
doc = fitz.open(file_path)
extracted_text = ""
for page in doc:
extracted_text += page.get_text()
doc.close()
return extracted_text
elif file_extension == '.txt':
with open(file_path, 'r') as file:
# Just read the entire contents of the text file
return file.read()
else:
return "Unsupported file type"
def responce_from_ai(self,job_description_path, resume_list_path):
result = ""
job_description = self.extract_text_from_file(job_description_path.name)
for resume_path in resume_list_path:
resume = self.extract_text_from_file(resume_path.name)
# Create a conversation for the OpenAI chat API
conversation = [
{"role": "system", "content": "You are a Mental Healthcare Chatbot."},
{"role": "user", "content": f"""Given the job description and the resume, assess the matching percentage to 100 and if 100 percentage not matched mention the remaining percentage with reason. **Job Description:**{job_description}**Resume:**{resume}
**Detailed Analysis:**
Introduction to say we've assessment the resume
the result should be in this format:
Matched Percentage: Precisely [get matching percentage between job description and resume]%.\n
Qualification Matching Percentage: [matching percentage between job description and resume qualifications].\n
Skills Matching Percentage: [matching percentage between job description and resume skills].\n
Experience Matching Percentage: [matching percentage between job description and resume experience].\n
Reason : [Reasons for why this resume matched and not matched.].\n
Skills To Improve : [Mention the skills to improve for the candidate according to the given job description. If there are no matches, simply say N/A.].\n
Keywords : [Return the matched keywords from resume and job_description. If there are no matches, simply say N/A.]\n
Company : [Extracted company name from job description].\n
Irrevelant: [mention the irrevelant skills and expericence]\n
Recommend Course: [mention specific course to recommend the candidate for job description needs].\n
Experience: [mention specific experience to recommend the candidate for job description needs].\n
Tailor Your Application: [Emphasize relevant areas].\n
Certifications: [Pursue certifications in mention area].\n
Feel free to contact us for further clarification.\n
Best wishes,
Your job is to write a proper E-Mail to the candidate from the organization with the job role, the candidate's name, organization name, and the body of this E-Mail should be in the above format."""}
]
# Call OpenAI GPT-3.5-turbo
chat_completion = client.chat.completions.create(
model = "GPT-3",
messages = conversation,
max_tokens=700,
temperature=0
)
response = chat_completion.choices[0].message.content
result += response + "\n-------------------------------------------------------------------------------------\n"
return result
def clear(self,jobDescription,resume,result_email):
jobDescription = None
resume = None
result_email = None
return jobDescription, resume, result_email
def gradio_interface(self):
with gr.Blocks(css="style.css",theme='freddyaboulton/test-blue') as app:
gr.HTML("""<center><h1 class ="center" style="color:#fff">ADOPLE AI</h1></center>
<br><center><h1 style="color:#fff">Candidate Assessment and Communication</h1></center>""")
with gr.Row(elem_id="col-container"):
with gr.Column(scale=0.55, min_width=150, ):
jobDescription = gr.File(label="Job Description", file_types = [".pdf",".txt"])
with gr.Column(scale=0.55, min_width=150):
resume = gr.File(label="Resume", file_types = [".pdf",".txt"] , file_count="multiple")
with gr.Row(elem_id="col-container"):
with gr.Column(scale=0.80, min_width=150):
analyse = gr.Button("Analyse")
with gr.Column(scale=0.20, min_width=150):
clear_btn = gr.ClearButton()
with gr.Row(elem_id="col-container"):
with gr.Column(scale=1.0, min_width=150):
result_email = gr.Textbox(label="E-mail", lines=10)
analyse.click(self.responce_from_ai, [jobDescription, resume], [result_email])
clear_btn.click(self.clear,[jobDescription,resume,result_email],[jobDescription,resume,result_email] )
app.launch()
if __name__ == "__main__":
resume = ResumeAnalyser()
answer = resume.gradio_interface() |