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import os
import openai
import PyPDF2
import gradio as gr
import docx
class CourseGenarator:
def __init__(self):
openai.api_key = os.getenv("OPENAI_API_KEY")
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 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 gradio_interface(self):
with gr.Blocks(css="style.css",theme='karthikeyan-adople/hudsonhayes-gray') as app:
gr.HTML("""<center class="darkblue" style='background-color:rgb(0,1,36); text-align:center;padding:30px;'><center>
<img class="leftimage" align="left" src="https://companieslogo.com/img/orig/RAND.AS_BIG-0f1935a4.png?t=1651813778" alt="Image" width="210" height="210">
<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():
resume = gr.File(label="Resume",elem_classes="heightfit")
with gr.Column():
analyse = gr.Button("Analyze")
with gr.Column():
result = gr.Textbox(label="Summarized",lines=8)
analyse.click(self.response, [resume], result)
print(result)
app.launch()
ques = CourseGenarator()
ques.gradio_interface() |