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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">Resume Summarizer</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() |