File size: 6,816 Bytes
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 |
import os
import openai
import PyPDF2
import gradio as gr
import docx
import re
import plotly.graph_objects as go
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 show_file(self,file_path):
return file_path.name
def launch_gradio_interface(self, share: bool = True):
with gr.Blocks(css="style.css",theme='karthikeyan-adople/hudsonhayes-gray') as app:
with gr.Row():
with gr.Column(elem_id="col-container"):
gr.HTML("""<center><h1>Resume</h1></center>""")
file_output = gr.File(elem_classes="filenameshow")
upload_button = gr.UploadButton(
"Browse File",file_types=[".txt", ".pdf", ".doc", ".docx",".json",".csv"],
elem_classes="filenameshow")
with gr.TabItem("Designation"):
btn = gr.Button(value="Submit")
output_text = gr.Textbox(label="Designation List")
with gr.TabItem("Summarized"):
analyse = gr.Button("Analyze")
summary_result = gr.Textbox(label="Summarized",lines=8)
with gr.TabItem("Skills and Education Gaps"):
analyse_resume = gr.Button("Analyze Resume")
result = gr.Textbox(label="Skills and Education Gaps",lines=8)
with gr.TabItem("Course"):
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)
app.launch(debug=True)
if __name__ == "__main__":
resume_overall = Resume_Overall()
resume_overall.launch_gradio_interface() |