import os os.environ["OPENAI_API_KEY"] = "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" import openai import os import PyPDF2 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from dotenv import load_dotenv, find_dotenv _ = load_dotenv(find_dotenv()) openai.api_key = os.getenv('OPENAI_API_KEY') def get_completion(prompt, model="gpt-3.5-turbo", temperature=0, max_tokens=500): messages = [{"role": "user", "content": prompt}] response = openai.ChatCompletion.create( model=model, temperature=temperature, max_tokens=max_tokens, messages=messages, ) return response.choices[0].message["content"] def generate_prompt(text, format="text"): prompt = f"""Play as an AI HR recruiter specialist and extract all the jobs as a list with : 1. Job Title 2. Location 3. Educations as list 4. Experiences as list 5. Skills and Competences as list 6. Functions and Tasks as list 7. Return the result in {format} format \ here the text \ ``` {text}``` """ return prompt import PyPDF2 def read_pdf(file_path): pdf_file = open(file_path, 'rb') pdf_reader = PyPDF2.PdfReader(pdf_file) text = "" for page in pdf_reader.pages: text += page.extract_text() pdf_file.close() return text import PyPDF2 def summarize(input): text = read_pdf(input.name) prompt = generate_prompt(text) response = get_completion(prompt) return response import gradio as gr gr.close_all() demo = gr.Interface(fn=summarize, inputs="file", outputs="text",title="Experiences And Skills Extraction From PDF file with gpt-3.5-turbo") demo.launch() #++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ import gradio as gr def colorize_text(text): return "" + text + "" "" + "Experiences" + "" iface = gr.Interface(fn=colorize_text, inputs="text", outputs="html") iface.launch()