app.py

import gradio as gr
from transformers import pipeline
import os 


api_key = os.environ["api_key"]

pipeline = pipeline(task="text-generation", model="nkthiebaut/summarizely", use_auth_token=api_key)


prediction_parameters = {
    "max_length": 100,
    "repetition_penalty": 2.0,
    "top_k": 1,
    "top_p": 1,
    "temperature": 2.0,
    "return_full_text": False, # remove input from output
}
def predict(job_title, skills, temperature): 
    prompt = f"Skills list: {skills} Job title: {job_title} Summary: "
    prediction_parameters["temperature"] = temperature
    predictions = pipeline(prompt, **prediction_parameters)
    return predictions[0]["generated_text"]
    

title = "Summarizely: Generate Realistic Experience Descriptions"
description = """
    Enter your job title and skills and Summarizely will generate an experience summary. 
    Summarizely is a Hackathon project by the Machine Learning team at Hired.com.
"""

interface = gr.Interface(
    predict, 
    inputs=[
        gr.Textbox(value="Full Stack Engineer", label="👩‍🔧 Job title"),
        gr.Textbox(value="HTML, React, Docker", label="🧠 Skills list"), 
        gr.Slider(0.0, 10.0, value=2.0, label="🌡 Temperature (~degree of uncertainty)"),
    ], 
    outputs=gr.Textbox(label="📚 Summary"),
    examples=[
        ["Full Stack Engineer", "React, JavaScript, HTML, PHP, Scrum, Python, SQL", 2.0],
        ["Backend Engineer", "Python, Ruby, Rails, AWS, Jenkins", 2.0],
        ["Machine Learning Engineer", "PyTorch, TensorFlow, Scikit-learn, SQL, Machine Learning, Deep Learning", 2.0]
    ],
    title=title,
    description=description,
)
interface.queue(default_enabled=False)
interface.launch(auth=("hired", os.environ["password"]))