app.py

import gradio as gr
import openai
import fitz  # PyMuPDF
import torch
from transformers import pipeline
from huggingface_hub import login

import os

hf_api_key = os.getenv("HF_API_KEY")
login(token=hf_api_key)

# Set OpenAI and Hugging Face API keys
openai.api_key = "sk-1E6ExsyFb-cdU8jPNDP1dsEq_ra_bazU-EXQZQ86pJT3BlbkFJ4zURsV0t--3qNM7A-P57NUqZIBosrL7POwzpjR5EQA"

# Initialize the Gemma model
gemma_pipe = pipeline(
    "text-generation",
    model="google/gemma-2-27b-it",
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cpu"
)

def extract_text_from_pdf(pdf_file):
    document = fitz.open(pdf_file)
    text = ""
    for page_num in range(len(document)):
        page = document.load_page(page_num)
        text += page.get_text()
    return text

def evaluate_with_gpt(pdf_file, job_description):
    resume_text = extract_text_from_pdf(pdf_file)

    prompt = f"""به عنوان یک تحلیلگر با تجربه سیستم ردیابی متقاضی (ATS)، نقش شما شامل ارزیابی رزومه در برابر شرح شغل است.
    رزومه:{resume_text}
    شرح شغل:{job_description}
    """
    
    response = openai.ChatCompletion.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": prompt}
        ]
    )
    
    return response.choices[0].message['content']

def evaluate_with_gemma(pdf_file, job_description):
    resume_text = extract_text_from_pdf(pdf_file)

    prompt = f"Evaluate the following resume against the job description. Resume: {resume_text} Job Description: {job_description}"
    
    outputs = gemma_pipe(prompt, max_new_tokens=256)
    return outputs[0]["generated_text"].strip()

def evaluate_both_models(pdf_file, job_description):
    gpt_result = evaluate_with_gpt(pdf_file, job_description)
    gemma_result = evaluate_with_gemma(pdf_file, job_description)
    return f"GPT-4o Result:\n{gpt_result}\n\nGemma Result:\n{gemma_result}"

iface = gr.Interface(
    fn=lambda pdf, jd, model: evaluate_with_gpt(pdf, jd) if model == "GPT-4o" else evaluate_with_gemma(pdf, jd) if model == "Gemma" else evaluate_both_models(pdf, jd),
    inputs=[
        gr.File(label="Upload Resume PDF"),
        gr.Textbox(lines=10, label="Job Description"),
        gr.Radio(choices=["GPT-4o", "Gemma", "Both"], label="Choose Model")
    ],
    outputs="text",
    title="Resume Evaluator"
)

iface.launch()