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Resume / app.py

robertselvam

Update app.py

742f3e7 verified about 2 months ago

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	import os
	from openai import AzureOpenAI
	import PyPDF2
	import gradio as gr
	import docx
	import re


	class Resume_Overall:
	def __init__(self):
	self.client = AzureOpenAI(api_key=os.getenv("AZURE_OPENAI_KEY"),
	api_version="2023-07-01-preview",
	azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
	)

	def extract_text_from_file(self,file_path):
	# Get the file extension
	file_extension = os.path.splitext(file_path)[1]

	if file_extension == '.pdf':
	with open(file_path, 'rb') as file:
	# Create a PDF file reader object
	reader = PyPDF2.PdfFileReader(file)

	# Create an empty string to hold the extracted text
	extracted_text = ""

	# Loop through each page in the PDF and extract the text
	for page_number in range(reader.getNumPages()):
	page = reader.getPage(page_number)
	extracted_text += page.extractText()
	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()

	elif file_extension == '.docx':
	doc = docx.Document(file_path)
	text = []
	for paragraph in doc.paragraphs:
	text.append(paragraph.text)
	return '\n'.join(text)

	else:
	return "Unsupported file type"

	def course_response(self,resume_path):
	resume_path = resume_path.name
	resume = self.extract_text_from_file(resume_path)



	# Define the prompt or input for the model
	conversation = [
	{"role": "system", "content": "You are a Resume Assistant."},
	{"role": "user", "content": f"""Analyze the resume to generate online courses with website links to improve skills following resume delimitted by triple backticks. Generate atmost five courses.
	result format should be:
	course:[course].
	website link:[website link]
	```{resume}```
	"""}
	]


	# Generate a response from the GPT-3 model
	chat_completion = self.client.chat.completions.create(
	model = "GPT-3",
	messages = conversation,
	max_tokens=200,
	temperature=0,
	n=1,
	stop=None,
	)

	# Extract the generated text from the API response
	generated_text = chat_completion.choices[0].message.content

	return generated_text
	def summary_response(self,resume_path):
	resume_path = resume_path.name
	resume = self.extract_text_from_file(resume_path)


	# Define the prompt or input for the model
	conversation = [
	{"role": "system", "content": "You are a Resume Summarizer."},
	{"role": "user", "content": f"""Analyze the resume to write the summary for following resume delimitted by triple backticks.
	```{resume}```
	"""}
	]

	# Generate a response from the GPT-3 model
	chat_completion = self.client.chat.completions.create(
	model = "GPT-3",
	messages = conversation,
	max_tokens=200,
	temperature=0,
	n=1,
	stop=None,
	)

	# Extract the generated text from the API response
	generated_text = chat_completion.choices[0].message.content

	return generated_text


	def skill_response(self,job_description_path):
	job_description_path = job_description_path.name
	resume = self.extract_text_from_file(job_description_path)


	# Define the prompt or input for the model
	conversation = [
	{"role": "system", "content": "You are a Resume Assistant."},
	{"role": "user", "content": f"""Find Education Gaps in given resume. Find Skills in resume.
	```{resume}```
	"""}
	]

	# Generate a response from the GPT-3 model
	chat_completion = self.client.chat.completions.create(
	model = "GPT-3", # Choose the GPT-3 engine you want to use
	messages = conversation,
	max_tokens=100, # Set the maximum number of tokens in the generated response
	temperature=0, # Controls the randomness of the output. Higher values = more random, lower values = more focused
	n=1, # Generate a single response
	stop=None, # Specify an optional stop sequence to limit the length of the response
	)

	# Extract the generated text from the API response
	generated_text = chat_completion.choices[0].message.content

	return generated_text

	def _generate_job_list(self, resume: str) -> str:
	conversation = [
	{"role": "system", "content": "You are a Resume Assistant."},
	{"role": "user", "content": f"List out perfect job roles for based on resume informations:{resume}"}
	]

	chat_completion = self.client.chat.completions.create(
	model = "GPT-3",
	messages = conversation,
	max_tokens=100,
	temperature=0,
	n=1,
	stop=None,
	)
	generated_text = chat_completion.choices[0].message.content
	return generated_text


	def job_list_interface(self, file) -> str:
	resume_text = self.extract_text_from_file(file.name)

	job_list = self._generate_job_list(resume_text)
	return job_list

	def generate_job_description(self, role, experience):
	# Generate a response from the GPT-3 model
	conversation = [
	{"role": "system", "content": "You are a Resume Assistant."},
	{"role": "user", "content": f"""Your task is generate Job description for this {role} with {experience} years of experience.
	Job Description Must have
	1. Job Title
	2. Job Summary : [200 words]
	3. Responsibilities : Five Responsibilities in five lines
	4. Required Skills : Six Skills
	5. Qualifications
	These topics must have in that Generated Job Description.
	"""}
	]

	chat_completion = self.client.chat.completions.create(
	model = "GPT-3", # Choose the GPT-3 engine you want to use
	messages = conversation,
	max_tokens=500, # Set the maximum number of tokens in the generated response
	temperature=0.5, # Controls the randomness of the output. Higher values = more random, lower values = more focused
	)

	# Extract the generated text from the API response
	generated_text = chat_completion.choices[0].message.content

	return generated_text

	def response(self,job_description_path):
	job_description_path = job_description_path.name
	job_description = self.extract_text_from_file(job_description_path)


	# Define the prompt or input for the model
	conversation = [
	{"role": "system", "content": "You are a Resume Assistant."},
	{"role": "user", "content": f"""Generate interview questions for screening following job_description delimitted by triple backticks. Generate atmost ten questions.
	```{job_description}```
	"""}
	]


	# Generate a response from the GPT-3 model
	chat_completion = self.client.chat.completions.create(
	model = "GPT-3", # Choose the GPT-3 engine you want to use
	messages = conversation,
	max_tokens=200, # Set the maximum number of tokens in the generated response
	temperature=0, # Controls the randomness of the output. Higher values = more random, lower values = more focused
	n=1, # Generate a single response
	stop=None, # Specify an optional stop sequence to limit the length of the response
	)

	# Extract the generated text from the API response
	generated_text = chat_completion.choices[0].message.content

	return generated_text

	def show_file(self,file_path):
	return file_path.name

	def launch_gradio_interface(self, share: bool = True):
	with gr.Blocks(css="style.css",theme='freddyaboulton/test-blue') as app:
	with gr.Tab("Resume"):
	with gr.Row(elem_id="col-container"):
	with gr.Column(scale=0.70):
	file_output = gr.File(elem_classes="filenameshow")
	with gr.Column(scale=0.30):
	upload_button = gr.UploadButton(
	"Browse File",file_types=[".txt", ".pdf", ".doc", ".docx",".json",".csv"],
	elem_classes="uploadbutton",scale=0.60)

	with gr.TabItem("Designation"):
	with gr.Column(elem_id = "col-container",scale=0.80):
	btn = gr.Button(value="Submit")
	output_text = gr.Textbox(label="Designation List",lines=8)
	with gr.TabItem("Summarized"):
	with gr.Column(elem_id = "col-container",scale=0.80):
	analyse = gr.Button("Analyze")
	summary_result = gr.Textbox(label="Summarized",lines=8)
	with gr.TabItem("Skills and Education Gaps"):
	with gr.Column(elem_id = "col-container",scale=0.80):
	analyse_resume = gr.Button("Analyze Resume")
	result = gr.Textbox(label="Skills and Education Gaps",lines=8)
	with gr.TabItem("Course"):
	with gr.Column(elem_id = "col-container",scale=0.80):
	course_analyse = gr.Button("Find Courses")
	course_result = gr.Textbox(label="Suggested Cources",lines=8)

	upload_button.upload(self.show_file,upload_button,file_output)
	course_analyse.click(self.course_response, [upload_button], course_result)
	analyse_resume.click(self.skill_response, [upload_button], result)
	btn.click(self.job_list_interface, upload_button, output_text)
	analyse.click(self.summary_response, [upload_button], summary_result)

	with gr.Tab("Job Description"):
	with gr.Row(elem_id="col-container"):
	with gr.Column(scale=0.70):
	file_output1 = gr.File(elem_classes="filenameshow")
	with gr.Column(scale=0.30):
	upload_button1 = gr.UploadButton(
	"Browse File",file_types=[".txt", ".pdf", ".doc", ".docx",".json",".csv"],
	elem_classes="uploadbutton")
	with gr.TabItem("Screening Question"):
	with gr.Row(elem_id="col-container"):
	jd_btn = gr.Button(value="Submit")
	with gr.Row(elem_id="col-container"):
	output_text1 = gr.Textbox(label="Screening Question")
	with gr.TabItem("Generate JD"):
	with gr.Row(elem_id="col-container"):
	with gr.Column(scale=0.50):
	rolls = gr.Textbox(label="Role",scale=0.60)
	with gr.Column(scale=0.50):
	experience = gr.Textbox(label="Experience",scale=0.60)
	with gr.Row(elem_id="col-container"):
	get_jd_btn = gr.Button("Generate JD")
	with gr.Row(elem_id="col-container"):
	result_jd = gr.Textbox(label="Job Description",lines=8)

	get_jd_btn.click(self.generate_job_description, [rolls,experience], result_jd)
	upload_button1.upload(self.show_file,upload_button1,file_output1)
	jd_btn.click(self.response,[upload_button1], output_text1)

	app.launch(debug=True)

	if __name__ == "__main__":
	resume_overall = Resume_Overall()
	resume_overall.launch_gradio_interface()