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