test-job-app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from smolagents import ToolCallingAgent
import torch

# Load the SmolLM model and tokenizer
# model_name = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
model_name = "HuggingFaceTB/SmolLM2-360M-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

def smol_lm_jd_process(job_description, system_prompt):
    # System Prompt and job description
    prompt = f"""<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{job_description}<|im_end|>
<|im_start|>assistant
"""
    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    output = model.generate(**inputs, max_new_tokens=512, do_sample=True, temperature=0.6, top_k=40, top_p=0.9, repetition_penalty=1.1)
    response = tokenizer.decode(output[0], skip_special_tokens=False)
    # Extract the assistant's response
    start_idx = response.find("<|im_start|>assistant")
    end_idx = response.find("<|im_end|>", start_idx)
    response = response[start_idx + len("<|im_start|>assistant\n"):end_idx].strip()
    return response

def process_job_description(company_name, company_url, job_description):
    # Step 2: Extract key qualifications, skills, and requirements
    system_prompt_requirements = "Extract key qualifications, skills, and requirements from this job description. Output as bullet points. Remove benefits/salary, bragging about the company, and other fluff not relevant to the skills, qualifications, and job requirements. ONLY INCLUDE INFORMATION THAT TELLS THE USER WHAT SKILLS THE EMPLOYER SEEKS."
    role_requirements = smol_lm_jd_process(job_description, system_prompt_requirements)

    # Step 3: Create a concise summary of the job description
    system_prompt_summary = "Create a concise 150-200 word summary of this job description. Remove company bragging bragging about the company, and other fluff not relevant to the position and what is desired from the candidate. FOCUS ON ASPECTS THAT POINT THE USER IN WHAT THE EMPLOYER WANTS FROM A CANDIDATE IN TERMS OF SKILLS, ACCOMPLISHMENTS, AND SUCH"
    clean_job_description = smol_lm_jd_process(job_description, system_prompt_summary)

    return {
        "Company Name": company_name,
        "Company URL": company_url,
        "Original Job Description": job_description,
        "Role Requirements": role_requirements,
        "Clean Job Description": clean_job_description
    }

# Create the Gradio app
demo = gr.Blocks()

with demo:
    gr.Markdown("# Job Description Input")
    company_name = gr.Textbox(label="Company Name")
    company_url = gr.Textbox(label="Company URL")
    job_description = gr.TextArea(label="Paste Job Description")

    gr.Button("Submit").click(
        process_job_description,
        inputs=[company_name, company_url, job_description],
        outputs=gr.JSON(label="Output")
    )

if __name__ == "__main__":
    demo.launch()