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import os
from ibm_watson_machine_learning.foundation_models import Model
from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams
from ibm_watson_machine_learning.foundation_models.utils.enums import ModelTypes, DecodingMethods
import gradio as gr
# Set up the API key and project ID for IBM Watson
watsonx_API = os.environ.get("watsonx_API")
project_id = os.environ.get("project_id")
# Generation parameters
gen_parms = {
"max_new_tokens": 512, # Adjust as needed for the length of the cover letter
"temperature": 0.7 # Adjust for creativity
}
# Model and project settings
model_id = "meta-llama/llama-2-13b-chat"
credentials={
"apikey": watsonx_API,
"url": "https://us-south.ml.cloud.ibm.com"
}
model = Model(
model_id = 'meta-llama/llama-2-13b-chat', # you can also specify like: ModelTypes.LLAMA_2_70B_CHAT
params = gen_parms,
credentials={
"apikey": watsonx_API,
"url": "https://us-south.ml.cloud.ibm.com"
},
project_id= project_id
)
# Initialize the model
model = Model(model_id, credentials, gen_parms, project_id)
# Function to generate customized career advice
def generate_career_advice(field, position_name, current_qualifications, likes, skills):
# Craft the prompt for the model
prompt = f"Generate a customized career advice using field: {field}, \
position_name: {position_name}, \
current_qualifications: {current_qualifications}, \
likes: {likes}, \
skills: {skills}."
generated_response = model.generate(prompt, gen_parms)
# Extract the generated text
career_advice = generated_response["results"][0]["generated_text"]
return career_advice
# Create Gradio interface for the cover letter generation application
career_advice_app = gr.Interface(
fn=generate_career_advice,
allow_flagging="never", # Deactivate the flag function in gradio as it is not needed.
inputs=[
gr.Textbox(label="Field of Interest (e.g., healthcare, trades, social service, etc., or enter 'not sure')", placeholder="Enter the field which you are interested in... or type 'not sure'."),
gr.Textbox(label="Position Name (e.g., nurse, personal support worker, software developer, plumber, etc., or enter 'not sure')", placeholder="Enter the name of the position you are interested in... or type 'not sure'"),
gr.Textbox(label="Current Qualifications (e.g., studying in high school, high school diploma, college diploma, etc.)", placeholder="Enter your current qualifications ..."),
gr.Textbox(label="Likes (e.g., I like working with my hands, I like to work outside, I like to help people, I like teaching, ...", placeholder="Enter activities you like ...", lines=10),
gr.Textbox(label="Skills (e.g., I am good at math, science, languages, computers, research, hand tools, etc.)", placeholder="Skills ...", lines=10),
],
outputs=gr.Textbox(label="Customized Career Advice"),
title="Customized AI-Powered Career Advice - by Wael Nawara",
description="Generate a customized career advice using field, position name, likes and skills"
)
# Launch the application
career_advice_app.launch(server_name="0.0.0.0", debug=True, server_port=7860, share=True)
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