File size: 11,232 Bytes
77de8f5 c6a6642 77de8f5 f83ca37 77de8f5 b833576 8940745 d5a07b7 7e9b9e4 d5a07b7 7e9b9e4 f83ca37 d5a07b7 daab587 f83ca37 d5a07b7 f83ca37 d5a07b7 f83ca37 d5a07b7 f83ca37 d5a07b7 06ad49c 8940745 d5a07b7 c6a6642 d5a07b7 8940745 f83ca37 b9b753c f83ca37 511f751 f83ca37 511f751 f83ca37 511f751 f83ca37 511f751 f83ca37 511f751 f83ca37 77de8f5 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 |
import os
import openai
import PyPDF2
import gradio as gr
import docx
import re
class Resume_Overall:
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':
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
prompt = 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
response = openai.Completion.create(
engine='text-davinci-003',
prompt=prompt,
max_tokens=200,
temperature=0,
n=1,
stop=None,
)
# Extract the generated text from the API response
generated_text = response.choices[0].text.strip()
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
prompt = f"""Analyze the resume to write the summary for following resume delimitted by triple backticks.
```{resume}```
"""
# Generate a response from the GPT-3 model
response = openai.Completion.create(
engine='text-davinci-003',
prompt=prompt,
max_tokens=200,
temperature=0,
n=1,
stop=None,
)
# Extract the generated text from the API response
generated_text = response.choices[0].text.strip()
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
prompt = f"""Find Education Gaps in given resume. Find Skills in resume.
```{resume}```
"""
# Generate a response from the GPT-3 model
response = openai.Completion.create(
engine='text-davinci-003', # Choose the GPT-3 engine you want to use
prompt=prompt,
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 = response.choices[0].text.strip()
return generated_text
def _generate_job_list(self, resume: str) -> str:
prompt = f"List out perfect job roles for based on resume informations:{resume}"
response = openai.Completion.create(
engine='text-davinci-003',
prompt=prompt,
max_tokens=100,
temperature=0,
n=1,
stop=None,
)
generated_text = response.choices[0].text.strip()
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
prompt = 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.
"""
response = openai.Completion.create(
engine='text-davinci-003', # Choose the GPT-3 engine you want to use
prompt=prompt,
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 = response.choices[0].text.strip()
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
prompt = 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
response = openai.Completion.create(
engine='text-davinci-003', # Choose the GPT-3 engine you want to use
prompt=prompt,
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 = response.choices[0].text.strip()
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():
with gr.Column(elem_id="col-container",scale=0.60):
file_output = gr.File(elem_classes="filenameshow")
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():
with gr.Column(elem_id="col-container"):
gr.HTML("""<center><h1>Resume</h1></center>""")
file_output1 = gr.File(elem_classes="filenameshow")
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():
jd_btn = gr.Button(value="Submit")
with gr.Row():
output_text1 = gr.Textbox(label="Screening Question")
with gr.TabItem("Generate JD"):
with gr.Row():
with gr.Column(elem_id="col-container"):
rolls = gr.Textbox(label="Role")
with gr.Column(elem_id="col-container"):
experience = gr.Textbox(label="Experience")
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() |