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| !python -m pip install --upgrade pip | |
| !pip install https://pypi.org/simple/bitsandbytes | |
| from langchain.llms.huggingface_pipeline import HuggingFacePipeline | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline | |
| from transformers import BitsAndBytesConfig | |
| import pdfplumber | |
| from langchain.prompts import PromptTemplate | |
| nf4_config = BitsAndBytesConfig( | |
| load_in_4bit=True, | |
| bnb_4bit_quant_type="nf4", | |
| bnb_4bit_use_double_quant=True, | |
| bnb_4bit_compute_dtype=torch.bfloat16 | |
| ) | |
| model_id = "huggingFaceH4/zephyr-7b-alpha" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_id, | |
| quantization_config=nf4_config, | |
| device_map="auto" | |
| ) | |
| model.tie_weights() | |
| pipe = pipeline("text-generation", | |
| model=model, | |
| tokenizer=tokenizer, | |
| max_new_tokens= 512 | |
| ) | |
| llm = HuggingFacePipeline(pipeline=pipe) | |
| ## LLM Response | |
| def get_llm_response(input): | |
| res = llm.predict(input) | |
| return res | |
| def input_pdf_text(uploaded_file): | |
| with open(uploaded_file, 'rb') as f: | |
| pdf = pdfplumber.open(f) | |
| text = "" | |
| for page in pdf.pages: | |
| text += page.extract_text() | |
| return text | |
| def Get_Response(upload_pdf,jd): | |
| text = input_pdf_text(upload_pdf) | |
| prompt_template = PromptTemplate.from_template( | |
| """ | |
| Hey Act Like a skilled or very experience ATS(Application Tracking System) | |
| with a deep understanding of tech field, software engineering, data science, | |
| data analyst and big data engineer. Your Task is to evaluate the resume based on the | |
| given job description. | |
| You must consider the job market is very competitive and you should provide | |
| best assistance for the improving the resume. Assisn the percentage Matching | |
| based on JD(Job Description) and the missing keywords with high accuracy | |
| resume:{text} | |
| description:{jd} | |
| I want the response in one single tring having the structure | |
| {{"JD Match":"%","MissingKeywords:[]","Profile Summary":""}} | |
| """) | |
| prompt = prompt_template.format(text=text,jd=jd) | |
| response = llm.predict(prompt) | |
| return response | |
| # Define Gradio interface | |
| interface = gr.Interface( | |
| fn=Get_Response, | |
| inputs=["file","text"], | |
| # inputs=[ | |
| # gr.File("upload_pdf", label="Upload PDF"), | |
| # gr.Textbox("jd", label="Job Description"), | |
| # ], | |
| outputs="text", | |
| title="Get ATS-Style Resume Evaluation", | |
| description="Upload a resume PDF and provide a job description to get an evaluation with JD match percentage, missing keywords, and profile summary.", | |
| ) | |
| # Launch the Gradio application | |
| interface.launch(debug=True, share=True) |