|
from transformers import pipeline, Conversation |
|
import gradio as gr |
|
from datetime import datetime |
|
|
|
from transformers.utils import logging |
|
logging.set_verbosity_error() |
|
|
|
|
|
chatbot = pipeline(task="conversational", |
|
model="google/gemma-7b-it") |
|
|
|
def get_job_description(company_name, sector, job_position): |
|
start_time = datetime.now() |
|
|
|
messages = [ |
|
{ |
|
"role": "user", |
|
"content": f"You are the bot director of Human Resources at { company_name }, a company of the { sector } sector. " + |
|
"Your goal is to hire people in different departments of the company. " + |
|
"Generate a text that describes the job in a creative, modern, formal and attractive way." |
|
}, |
|
{ |
|
"role": "assistant", |
|
"content": "Tell me what job role it is and I'll generate the description for you!" |
|
}, |
|
{ |
|
"role": "user", |
|
"content": job_position |
|
}, |
|
{ |
|
"role": "assistant", |
|
"content": "Description:" |
|
} |
|
] |
|
|
|
conversation = Conversation(messages) |
|
conversation = chatbot(conversation, do_sample=True, max_new_tokens=600, temperature=1) |
|
response = conversation.messages[-1]["content"] |
|
|
|
delta_time = datetime.now() - start_time |
|
|
|
return response, delta_time |
|
|
|
with gr.Blocks() as demo: |
|
gr.Markdown( |
|
""" |
|
# Job Description generator |
|
### This SLM demo uses ['google/gemma-2b-it'](https://huggingface.co/google/gemma-2b-it) |
|
""") |
|
|
|
interface = gr.Interface( |
|
fn = get_job_description, |
|
inputs = [gr.Textbox(placeholder="Enter the company name", label="Company name"), |
|
gr.Textbox(placeholder="Enter the company sector", label="Company sector"), |
|
gr.Textbox(placeholder="Enter the job position", label="Job position")], |
|
outputs = [gr.Markdown(), |
|
gr.Textbox(label="Elapsed time")] |
|
) |
|
|
|
demo.launch(server_port=7860) |
|
|
|
|
